Ranking is the most important component in a search system. Mostsearch systems deal with large am... more Ranking is the most important component in a search system. Mostsearch systems deal with large amounts of natural language data,hence an effective ranking system requires a deep understandingof text semantics. Recently, deep learning based natural languageprocessing (deep NLP) models have generated promising results onranking systems. BERT is one of the most successful models thatlearn contextual embedding, which has been applied to capturecomplex query-document relations for search ranking. However,this is generally done by exhaustively interacting each query wordwith each document word, which is inefficient for online servingin search product systems. In this paper, we investigate how tobuild an efficient BERT-based ranking model for industry use cases.The solution is further extended to a general ranking framework,DeText, that is open sourced and can be applied to various rankingproductions. Offline and online experiments of DeText on threereal-world search systems present signif...
Social media expands the ways people communicate with each other. On a popular social media websi... more Social media expands the ways people communicate with each other. On a popular social media website, a user typically has hundreds of contacts (or friends) on average. As a person’s social network grows, friend management is increasingly important for effective communications. Often one can only afford to maintain close friendship in a small scale due to limited time and other resources. In other words, the majority of one’s connections are so-so friends and do not hold strong influence on the user. One approach resorts to network denoising by which unimportant connections are removed as noise. We study the challenges of network denoising in social media and how we can leverage a variety of social media information to denosing the links. We formulate the network denoising task as an optimization problem, and show the efficacy of our network denoising approach and its scalability in the domain of behavior inference.
Social media is immensely popular, with billions of users across various platform. The study of s... more Social media is immensely popular, with billions of users across various platform. The study of social media has allowed for deeper inquiries into questions posed by computer scientists, social scientists, and others. Social media posts tagged with location have provided means for researchers to perform even deeper analysis into their data. While location information allows for rich insight into social media data, very few posts are explicitly tagged with geographic information. In this work, we begin by introducing some state-of-the-art analysis techniques that can be performed using the location of a social media post. Next, we introduce some systems that help first responders provide relief with the help of the location of social media posts. Finally, we discuss how machine learning techniques can be applied to infer the location of a social media post, bringing this analysis to any message posted on social media.
Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020
As the largest professional network, LinkedIn hosts millions of user profiles and job postings. U... more As the largest professional network, LinkedIn hosts millions of user profiles and job postings. Users effectively find what they need by entering search queries. However, finding what they are looking for can be a challenge, especially if they are unfamiliar with specific keywords from their industry. Query Suggestion is a popular feature where a search engine can suggest alternate, related queries. At LinkedIn, we have productionized a deep learning Seq2Seq model to transform an input query into several alternatives. This model is trained by examining search history directly typed by users. Once online, we can determine whether or not users clicked on suggested queries. This new feedback data indicates which suggestions caught the user's attention. In this work, we propose training a model with both the search history and user feedback datasets. We examine several ways to incorporate feedback without any architectural change, including adding a novel pairwise ranking loss term ...
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019
Deep learning models have been very successful in many natural language processing tasks. Search ... more Deep learning models have been very successful in many natural language processing tasks. Search engine works with rich natural language data, e.g., queries and documents, which implies great potential of applying deep natural language processing on such data to improve search performance. Furthermore, it opens an unprecedented opportunity to explore more advanced search experience, such as conversational search and chatbot. This tutorial offers an overview on deep learning based natural language processing for search systems from an industry perspective. We focus on how deep natural language processing powers search systems in practice. The tutorial introduces basic concepts, elaborates associated challenges, reviews the state-of-the-art approaches, covers end-to-end tasks in search systems with examples, and discusses the future trend.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019
Search and recommender systems share many fundamental components including language understanding... more Search and recommender systems share many fundamental components including language understanding, retrieval and ranking, and language generation. Building powerful search and recommender systems requires processing natural language effectively and efficiently. Recent rapid growth of deep learning technologies has presented both opportunities and challenges in this area. This tutorial offers an overview of deep learning based natural language processing (NLP) for search and recommender systems from an industry perspective. It first introduces deep learning based NLP technologies, including language understanding and language generation. Then it details how those technologies can be applied to common tasks in search and recommender systems, including query and document understanding, retrieval and ranking, and language generation. Applications in LinkedIn production systems are presented. The tutorial concludes with discussion of future trend.
Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020
Query Auto Completion (QAC), as the starting point of information retrieval tasks, is critical to... more Query Auto Completion (QAC), as the starting point of information retrieval tasks, is critical to user experience. Generally it has two steps: generating completed query candidates according to query prefixes, and ranking them based on extracted features. Three major challenges are observed for a query auto completion system: (1) QAC has a strict online latency requirement. For each keystroke, results must be returned within tens of milliseconds, which poses a significant challenge in designing sophisticated language models for it. (2) For unseen queries, generated candidates are of poor quality as contextual information is not fully utilized. (3) Traditional QAC systems heavily rely on handcrafted features such as the query candidate frequency in search logs, lacking sufficient semantic understanding of the candidate. In this paper, we propose an efficient neural QAC system with effective context modeling to overcome these challenges. On the candidate generation side, this system uses as much information as possible in unseen prefixes to generate relevant candidates, increasing the recall by a large margin. On the candidate ranking side, an unnormalized language model is proposed, which effectively captures deep semantics of queries. This approach presents better ranking performance over state-of-the-art neural ranking methods and reduces ∼95% latency compared to neural language modeling methods. The empirical results on public datasets show that our model achieves a good balance between accuracy and efficiency. This system is served in LinkedIn job search with significant product impact observed.
Point-of-interest (POI) recommendation has become an important approach to help people discover a... more Point-of-interest (POI) recommendation has become an important approach to help people discover attractive locations. But the extreme sparsity of the user-POI matrix creates a severe challenge. To address this challenge, researchers have begun to explore the review content information for POI recommendations. Existing methods are based on bag-of-words or embedding techniques which leads to a shallow understanding of user preference. In order to capture valuable information about user preference, we propose a content-aware POI recommendation based on convolutional neural network (CPC). We utilize a convolutional neural network as the foundation of a unified POI recommendation framework and introduce the three types of content information, including POI properties, user interests and sentiment indications. The experimental results indicate that convolutional neural network is very capable of capturing semantic and sentiment information from review content and demonstrate that the relevant information in reviews can improve POI recommendation performance on location-based social networks.
Proceedings of the 22nd international conference on World Wide Web - WWW '13, 2013
The explosion of social media services presents a great opportunity to understand the sentiment o... more The explosion of social media services presents a great opportunity to understand the sentiment of the public via analyzing its large-scale and opinion-rich data. In social media, it is easy to amass vast quantities of unlabeled data, but very costly to obtain sentiment labels, which makes unsupervised sentiment analysis essential for various applications. It is challenging for traditional lexicon-based unsupervised methods due to the fact that expressions in social media are unstructured, informal, and fast-evolving. Emoticons and product ratings are examples of emotional signals that are associated with sentiments expressed in posts or words. Inspired by the wide availability of emotional signals in social media, we propose to study the problem of unsupervised sentiment analysis with emotional signals. In particular, we investigate whether the signals can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation. We further incorporate the signals into an unsupervised learning framework for sentiment analysis. In the experiment, we compare the proposed framework with the state-of-the-art methods on two Twitter datasets and empirically evaluate our proposed framework to gain a deep understanding of the effects of emotional signals.
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014
Feature selection has been proven to be efficient in preparing high dimensional data for data min... more Feature selection has been proven to be efficient in preparing high dimensional data for data mining and machine learning. As most data is unlabeled, unsupervised feature selection has attracted more and more attention in recent years. Discriminant analysis has been proven to be a powerful technique to select discriminative features for supervised feature selection. To apply discriminant analysis, we usually need label information which is absent for unlabeled data. This gap makes it challenging to apply discriminant analysis for unsupervised feature selection. In this paper, we investigate how to exploit discriminant analysis in unsupervised scenarios to select discriminative features. We introduce the concept of pseudo labels, which enable discriminant analysis on unlabeled data, propose a novel unsupervised feature selection framework DisUFS which incorporates learning discriminative features with generating pseudo labels, and develop an effective algorithm for DisUFS. Experimental results on different types of real-world data demonstrate the effectiveness of the proposed framework DisUFS.
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13, 2013
Social media expands the ways people communicate with each other. On a popular social media websi... more Social media expands the ways people communicate with each other. On a popular social media website, a user typically has hundreds of contacts (or friends) on average. As a person's social network grows, friend management is increasingly important for effective communications. Often, one can only afford to maintain close friendship in a small scale due to limited time and other resources. In other words, the majority of one's connections are so-so friends and do not hold strong influence on the user. One approach resorts to network denoising, by which unimportant connections are removed as noise. We study the challenges of network denoising in social media and how we can leverage a variety of social media information to denoise the links. We formulate the network denoising task as an optimization problem, and show the efficacy of our network denoising approach and its scalability experimentally in the domain of behavior inference.
Social Computing, Behavioral-Cultural Modeling and Prediction, 2011
The efficiency at which governments and non-governmental organizations (NGOs) are able to respond... more The efficiency at which governments and non-governmental organizations (NGOs) are able to respond to a crisis and provide relief to victims has gained increased attention. This emphasis coincides with significant events such as tsunamis, hurricanes, earthquakes, and environmental disasters occuring during the last decade. Crowdsourcing applications such as Twitter, Ushahidi, and Sahana have proven useful for gathering information about a crisis yet have limited utility for response coordination. In this paper, we briefly describe the shortfalls of current crowdsourcing applications applied to disaster relief coordination and discuss one approach aimed at facilitating efficient collaborations amongst disparate organizations responding to a crisis.
Proceedings of the fifth ACM international conference on Web search and data mining - WSDM '12, 2012
Traditionally, research about trust assumes a single type of trust between users. However, trust,... more Traditionally, research about trust assumes a single type of trust between users. However, trust, as a social concept, inherently has many facets indicating multiple and heterogeneous trust relationships between users. Due to the presence of a large trust network for an online user, it is necessary to discern multi-faceted trust as there are naturally experts of different types. Our study in product review sites reveals that people place trust differently to different people. Since the widely used adjacency matrix cannot capture multi-faceted trust relationships between users, we propose a novel approach by incorporating these relationships into traditional rating prediction algorithms to reliably estimate their strengths. Our work results in interesting findings such as heterogeneous pairs of reciprocal links. Experimental results on real-world data from Epinions and Ciao show that our work of discerning multi-faceted trust can be applied to improve the performance of tasks such as rating prediction, facet-sensitive ranking, and status theory.
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12, 2012
Location-based social networks (LBSNs) have attracted an increasing number of users in recent yea... more Location-based social networks (LBSNs) have attracted an increasing number of users in recent years. The availability of geographical and social information of online LBSNs provides an unprecedented opportunity to study the human movement from their socio-spatial behavior, enabling a variety of location-based services. Previous work on LBSNs reported limited improvements from using the social network information for location prediction; as users can check-in at new places, traditional work on location prediction that relies on mining a user's historical trajectories is not designed for this "cold start" problem of predicting new check-ins. In this paper, we propose to utilize the social network information for solving the "cold start" location prediction problem, with a geo-social correlation model to capture social correlations on LBSNs considering social networks and geographical distance. The experimental results on a real-world LBSN demonstrate that our approach properly models the social correlations of a user's new check-ins by considering various correlation strengths and correlation measures.
Synthesis Lectures on Data Mining and Knowledge Discovery, 2015
e series publishes 50-to 150-page publications on topics pertaining to data mining, web mining, ... more e series publishes 50-to 150-page publications on topics pertaining to data mining, web mining, text mining, and knowledge discovery, including tutorials and case studies. Potential topics include: data mining algorithms, innovative data mining applications, data mining systems, mining text, web and semi-structured data, high performance and parallel/distributed data mining, data mining standards, data mining and knowledge discovery framework and process, data mining foundations, mining data streams and sensor data, mining multi-media data, mining social networks and graph data, mining spatial and temporal data, pre-processing and post-processing in data mining, robust and scalable statistical methods, security, privacy, and adversarial data mining, visual data mining, visual analytics, and data visualization.
Proceedings of the 7th ACM conference on Recommender systems, 2013
Online reviews play a vital role in the decision-making process for online users. Helpful reviews... more Online reviews play a vital role in the decision-making process for online users. Helpful reviews are usually buried in a large number of unhelpful reviews, and with the consistently increasing number of reviews, it becomes more and more difficult for online users to find helpful reviews. Therefore most online review websites allow online users to rate the helpfulness of a review and a global helpfulness score is computed for the review based on its available ratings. However, in reality, user-specified helpfulness ratings for reviews are very sparse-a few reviews attract large numbers of helpfulness ratings while most reviews obtain few or even no helpfulness ratings. The available helpfulness ratings are too sparse for online users to assess the helpfulness of reviews. Also the helpfulness of a review is not necessarily equally useful for all users and users with different background may treat the helpfulness of a review very differently. The user idiosyncracy of review helpfulness motivates us to study the problem of review helpfulness rating prediction in this paper. We first identify various types of context information, model them mathematically, and propose a context-aware review helpfulness rating prediction framework CAP. Experimental results demonstrate the effectiveness of the proposed framework and the importance of context awareness in solving the review helpfulness rating prediction problem.
Location based service is an indispensable part of today's mobile era. While it brings a lot ... more Location based service is an indispensable part of today's mobile era. While it brings a lot of benefits to people, the breach to individual location privacy is always a concern and impedes the smooth development of location based service. A user can be easily tracked once she subscribes or uses the service from an untrusted location based service server. In this paper, we try to address this problem by proposing a secure and efficient location based service system. In our system, a user does not leak any of her location information while she can still obtain the desired information associated with the location. We propose a novel method to map a user's current location to the index of the information stored in the location based service server. We demonstrated the efficiency of our system through simulations.
Social media websites allow users to exchange short texts such as Tweets in microblogging, user s... more Social media websites allow users to exchange short texts such as Tweets in microblogging, user status in friendship networks. Their limited length, pervasive abbreviations, and coined acronyms and words exacerbate the problems of synonymy and polysemy, and bring about new challenges to data mining applications such as text clustering and classification, etc. To address these issues, we dissect some potential causes and devise an efficient approach that enriches data representation by employing machine translation to increase the number of features from different languages. Then we propose a novel framework which performs multi-language knowledge integration and feature reduction simultaneously through matrix factorization techniques. The proposed approach is evaluated extensively in terms of effectiveness on two social media datasets from Facebook and Twitter. With its significant performance improvement, we further investigate potential factors that contribute to the improved performance.
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12, 2012
Most existing research about online trust assumes static trust relations between users. As we are... more Most existing research about online trust assumes static trust relations between users. As we are informed by social sciences, trust evolves as humans interact. Little work exists studying trust evolution in an online world. Researching online trust evolution faces unique challenges because more often than not, available data is from passive observation. In this paper, we leverage social science theories to develop a methodology that enables the study of online trust evolution. In particular, we propose a framework of evolution trust, eTrust, which exploits the dynamics of user preferences in the context of online product review. We present technical details about modeling trust evolution, and perform experiments to show how the exploitation of trust evolution can help improve the performance of online applications such as rating and trust prediction.
2010 IEEE International Conference on Data Mining, 2010
The increasing popularity of social media is shortening the distance between people. Social activ... more The increasing popularity of social media is shortening the distance between people. Social activities, e.g., tagging in Flickr, bookmarking in Delicious, twittering in Twitter, etc. are reshaping people's social life and redefining their social roles. People with shared interests tend to form their groups in social media, and users within the same community likely exhibit similar social behavior (e.g., going for the same movies, having similar political viewpoints), which in turn reinforces the community structure. The multiple interactions in social activities entail that the community structures are often overlapping, i.e., one person is involved in several communities. We propose a novel co-clustering framework, which takes advantage of networking information between users and tags in social media, to discover these overlapping communities. In our method, users are connected via tags and tags are connected to users. This explicit representation of users and tags is useful for understanding group evolution by looking at who is interested in what. The efficacy of our method is supported by empirical evaluation in both synthetic and online social networking data.
Ranking is the most important component in a search system. Mostsearch systems deal with large am... more Ranking is the most important component in a search system. Mostsearch systems deal with large amounts of natural language data,hence an effective ranking system requires a deep understandingof text semantics. Recently, deep learning based natural languageprocessing (deep NLP) models have generated promising results onranking systems. BERT is one of the most successful models thatlearn contextual embedding, which has been applied to capturecomplex query-document relations for search ranking. However,this is generally done by exhaustively interacting each query wordwith each document word, which is inefficient for online servingin search product systems. In this paper, we investigate how tobuild an efficient BERT-based ranking model for industry use cases.The solution is further extended to a general ranking framework,DeText, that is open sourced and can be applied to various rankingproductions. Offline and online experiments of DeText on threereal-world search systems present signif...
Social media expands the ways people communicate with each other. On a popular social media websi... more Social media expands the ways people communicate with each other. On a popular social media website, a user typically has hundreds of contacts (or friends) on average. As a person’s social network grows, friend management is increasingly important for effective communications. Often one can only afford to maintain close friendship in a small scale due to limited time and other resources. In other words, the majority of one’s connections are so-so friends and do not hold strong influence on the user. One approach resorts to network denoising by which unimportant connections are removed as noise. We study the challenges of network denoising in social media and how we can leverage a variety of social media information to denosing the links. We formulate the network denoising task as an optimization problem, and show the efficacy of our network denoising approach and its scalability in the domain of behavior inference.
Social media is immensely popular, with billions of users across various platform. The study of s... more Social media is immensely popular, with billions of users across various platform. The study of social media has allowed for deeper inquiries into questions posed by computer scientists, social scientists, and others. Social media posts tagged with location have provided means for researchers to perform even deeper analysis into their data. While location information allows for rich insight into social media data, very few posts are explicitly tagged with geographic information. In this work, we begin by introducing some state-of-the-art analysis techniques that can be performed using the location of a social media post. Next, we introduce some systems that help first responders provide relief with the help of the location of social media posts. Finally, we discuss how machine learning techniques can be applied to infer the location of a social media post, bringing this analysis to any message posted on social media.
Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020
As the largest professional network, LinkedIn hosts millions of user profiles and job postings. U... more As the largest professional network, LinkedIn hosts millions of user profiles and job postings. Users effectively find what they need by entering search queries. However, finding what they are looking for can be a challenge, especially if they are unfamiliar with specific keywords from their industry. Query Suggestion is a popular feature where a search engine can suggest alternate, related queries. At LinkedIn, we have productionized a deep learning Seq2Seq model to transform an input query into several alternatives. This model is trained by examining search history directly typed by users. Once online, we can determine whether or not users clicked on suggested queries. This new feedback data indicates which suggestions caught the user's attention. In this work, we propose training a model with both the search history and user feedback datasets. We examine several ways to incorporate feedback without any architectural change, including adding a novel pairwise ranking loss term ...
Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019
Deep learning models have been very successful in many natural language processing tasks. Search ... more Deep learning models have been very successful in many natural language processing tasks. Search engine works with rich natural language data, e.g., queries and documents, which implies great potential of applying deep natural language processing on such data to improve search performance. Furthermore, it opens an unprecedented opportunity to explore more advanced search experience, such as conversational search and chatbot. This tutorial offers an overview on deep learning based natural language processing for search systems from an industry perspective. We focus on how deep natural language processing powers search systems in practice. The tutorial introduces basic concepts, elaborates associated challenges, reviews the state-of-the-art approaches, covers end-to-end tasks in search systems with examples, and discusses the future trend.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019
Search and recommender systems share many fundamental components including language understanding... more Search and recommender systems share many fundamental components including language understanding, retrieval and ranking, and language generation. Building powerful search and recommender systems requires processing natural language effectively and efficiently. Recent rapid growth of deep learning technologies has presented both opportunities and challenges in this area. This tutorial offers an overview of deep learning based natural language processing (NLP) for search and recommender systems from an industry perspective. It first introduces deep learning based NLP technologies, including language understanding and language generation. Then it details how those technologies can be applied to common tasks in search and recommender systems, including query and document understanding, retrieval and ranking, and language generation. Applications in LinkedIn production systems are presented. The tutorial concludes with discussion of future trend.
Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020
Query Auto Completion (QAC), as the starting point of information retrieval tasks, is critical to... more Query Auto Completion (QAC), as the starting point of information retrieval tasks, is critical to user experience. Generally it has two steps: generating completed query candidates according to query prefixes, and ranking them based on extracted features. Three major challenges are observed for a query auto completion system: (1) QAC has a strict online latency requirement. For each keystroke, results must be returned within tens of milliseconds, which poses a significant challenge in designing sophisticated language models for it. (2) For unseen queries, generated candidates are of poor quality as contextual information is not fully utilized. (3) Traditional QAC systems heavily rely on handcrafted features such as the query candidate frequency in search logs, lacking sufficient semantic understanding of the candidate. In this paper, we propose an efficient neural QAC system with effective context modeling to overcome these challenges. On the candidate generation side, this system uses as much information as possible in unseen prefixes to generate relevant candidates, increasing the recall by a large margin. On the candidate ranking side, an unnormalized language model is proposed, which effectively captures deep semantics of queries. This approach presents better ranking performance over state-of-the-art neural ranking methods and reduces ∼95% latency compared to neural language modeling methods. The empirical results on public datasets show that our model achieves a good balance between accuracy and efficiency. This system is served in LinkedIn job search with significant product impact observed.
Point-of-interest (POI) recommendation has become an important approach to help people discover a... more Point-of-interest (POI) recommendation has become an important approach to help people discover attractive locations. But the extreme sparsity of the user-POI matrix creates a severe challenge. To address this challenge, researchers have begun to explore the review content information for POI recommendations. Existing methods are based on bag-of-words or embedding techniques which leads to a shallow understanding of user preference. In order to capture valuable information about user preference, we propose a content-aware POI recommendation based on convolutional neural network (CPC). We utilize a convolutional neural network as the foundation of a unified POI recommendation framework and introduce the three types of content information, including POI properties, user interests and sentiment indications. The experimental results indicate that convolutional neural network is very capable of capturing semantic and sentiment information from review content and demonstrate that the relevant information in reviews can improve POI recommendation performance on location-based social networks.
Proceedings of the 22nd international conference on World Wide Web - WWW '13, 2013
The explosion of social media services presents a great opportunity to understand the sentiment o... more The explosion of social media services presents a great opportunity to understand the sentiment of the public via analyzing its large-scale and opinion-rich data. In social media, it is easy to amass vast quantities of unlabeled data, but very costly to obtain sentiment labels, which makes unsupervised sentiment analysis essential for various applications. It is challenging for traditional lexicon-based unsupervised methods due to the fact that expressions in social media are unstructured, informal, and fast-evolving. Emoticons and product ratings are examples of emotional signals that are associated with sentiments expressed in posts or words. Inspired by the wide availability of emotional signals in social media, we propose to study the problem of unsupervised sentiment analysis with emotional signals. In particular, we investigate whether the signals can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation. We further incorporate the signals into an unsupervised learning framework for sentiment analysis. In the experiment, we compare the proposed framework with the state-of-the-art methods on two Twitter datasets and empirically evaluate our proposed framework to gain a deep understanding of the effects of emotional signals.
Proceedings of the 2014 SIAM International Conference on Data Mining, 2014
Feature selection has been proven to be efficient in preparing high dimensional data for data min... more Feature selection has been proven to be efficient in preparing high dimensional data for data mining and machine learning. As most data is unlabeled, unsupervised feature selection has attracted more and more attention in recent years. Discriminant analysis has been proven to be a powerful technique to select discriminative features for supervised feature selection. To apply discriminant analysis, we usually need label information which is absent for unlabeled data. This gap makes it challenging to apply discriminant analysis for unsupervised feature selection. In this paper, we investigate how to exploit discriminant analysis in unsupervised scenarios to select discriminative features. We introduce the concept of pseudo labels, which enable discriminant analysis on unlabeled data, propose a novel unsupervised feature selection framework DisUFS which incorporates learning discriminative features with generating pseudo labels, and develop an effective algorithm for DisUFS. Experimental results on different types of real-world data demonstrate the effectiveness of the proposed framework DisUFS.
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM '13, 2013
Social media expands the ways people communicate with each other. On a popular social media websi... more Social media expands the ways people communicate with each other. On a popular social media website, a user typically has hundreds of contacts (or friends) on average. As a person's social network grows, friend management is increasingly important for effective communications. Often, one can only afford to maintain close friendship in a small scale due to limited time and other resources. In other words, the majority of one's connections are so-so friends and do not hold strong influence on the user. One approach resorts to network denoising, by which unimportant connections are removed as noise. We study the challenges of network denoising in social media and how we can leverage a variety of social media information to denoise the links. We formulate the network denoising task as an optimization problem, and show the efficacy of our network denoising approach and its scalability experimentally in the domain of behavior inference.
Social Computing, Behavioral-Cultural Modeling and Prediction, 2011
The efficiency at which governments and non-governmental organizations (NGOs) are able to respond... more The efficiency at which governments and non-governmental organizations (NGOs) are able to respond to a crisis and provide relief to victims has gained increased attention. This emphasis coincides with significant events such as tsunamis, hurricanes, earthquakes, and environmental disasters occuring during the last decade. Crowdsourcing applications such as Twitter, Ushahidi, and Sahana have proven useful for gathering information about a crisis yet have limited utility for response coordination. In this paper, we briefly describe the shortfalls of current crowdsourcing applications applied to disaster relief coordination and discuss one approach aimed at facilitating efficient collaborations amongst disparate organizations responding to a crisis.
Proceedings of the fifth ACM international conference on Web search and data mining - WSDM '12, 2012
Traditionally, research about trust assumes a single type of trust between users. However, trust,... more Traditionally, research about trust assumes a single type of trust between users. However, trust, as a social concept, inherently has many facets indicating multiple and heterogeneous trust relationships between users. Due to the presence of a large trust network for an online user, it is necessary to discern multi-faceted trust as there are naturally experts of different types. Our study in product review sites reveals that people place trust differently to different people. Since the widely used adjacency matrix cannot capture multi-faceted trust relationships between users, we propose a novel approach by incorporating these relationships into traditional rating prediction algorithms to reliably estimate their strengths. Our work results in interesting findings such as heterogeneous pairs of reciprocal links. Experimental results on real-world data from Epinions and Ciao show that our work of discerning multi-faceted trust can be applied to improve the performance of tasks such as rating prediction, facet-sensitive ranking, and status theory.
Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12, 2012
Location-based social networks (LBSNs) have attracted an increasing number of users in recent yea... more Location-based social networks (LBSNs) have attracted an increasing number of users in recent years. The availability of geographical and social information of online LBSNs provides an unprecedented opportunity to study the human movement from their socio-spatial behavior, enabling a variety of location-based services. Previous work on LBSNs reported limited improvements from using the social network information for location prediction; as users can check-in at new places, traditional work on location prediction that relies on mining a user's historical trajectories is not designed for this "cold start" problem of predicting new check-ins. In this paper, we propose to utilize the social network information for solving the "cold start" location prediction problem, with a geo-social correlation model to capture social correlations on LBSNs considering social networks and geographical distance. The experimental results on a real-world LBSN demonstrate that our approach properly models the social correlations of a user's new check-ins by considering various correlation strengths and correlation measures.
Synthesis Lectures on Data Mining and Knowledge Discovery, 2015
e series publishes 50-to 150-page publications on topics pertaining to data mining, web mining, ... more e series publishes 50-to 150-page publications on topics pertaining to data mining, web mining, text mining, and knowledge discovery, including tutorials and case studies. Potential topics include: data mining algorithms, innovative data mining applications, data mining systems, mining text, web and semi-structured data, high performance and parallel/distributed data mining, data mining standards, data mining and knowledge discovery framework and process, data mining foundations, mining data streams and sensor data, mining multi-media data, mining social networks and graph data, mining spatial and temporal data, pre-processing and post-processing in data mining, robust and scalable statistical methods, security, privacy, and adversarial data mining, visual data mining, visual analytics, and data visualization.
Proceedings of the 7th ACM conference on Recommender systems, 2013
Online reviews play a vital role in the decision-making process for online users. Helpful reviews... more Online reviews play a vital role in the decision-making process for online users. Helpful reviews are usually buried in a large number of unhelpful reviews, and with the consistently increasing number of reviews, it becomes more and more difficult for online users to find helpful reviews. Therefore most online review websites allow online users to rate the helpfulness of a review and a global helpfulness score is computed for the review based on its available ratings. However, in reality, user-specified helpfulness ratings for reviews are very sparse-a few reviews attract large numbers of helpfulness ratings while most reviews obtain few or even no helpfulness ratings. The available helpfulness ratings are too sparse for online users to assess the helpfulness of reviews. Also the helpfulness of a review is not necessarily equally useful for all users and users with different background may treat the helpfulness of a review very differently. The user idiosyncracy of review helpfulness motivates us to study the problem of review helpfulness rating prediction in this paper. We first identify various types of context information, model them mathematically, and propose a context-aware review helpfulness rating prediction framework CAP. Experimental results demonstrate the effectiveness of the proposed framework and the importance of context awareness in solving the review helpfulness rating prediction problem.
Location based service is an indispensable part of today's mobile era. While it brings a lot ... more Location based service is an indispensable part of today's mobile era. While it brings a lot of benefits to people, the breach to individual location privacy is always a concern and impedes the smooth development of location based service. A user can be easily tracked once she subscribes or uses the service from an untrusted location based service server. In this paper, we try to address this problem by proposing a secure and efficient location based service system. In our system, a user does not leak any of her location information while she can still obtain the desired information associated with the location. We propose a novel method to map a user's current location to the index of the information stored in the location based service server. We demonstrated the efficiency of our system through simulations.
Social media websites allow users to exchange short texts such as Tweets in microblogging, user s... more Social media websites allow users to exchange short texts such as Tweets in microblogging, user status in friendship networks. Their limited length, pervasive abbreviations, and coined acronyms and words exacerbate the problems of synonymy and polysemy, and bring about new challenges to data mining applications such as text clustering and classification, etc. To address these issues, we dissect some potential causes and devise an efficient approach that enriches data representation by employing machine translation to increase the number of features from different languages. Then we propose a novel framework which performs multi-language knowledge integration and feature reduction simultaneously through matrix factorization techniques. The proposed approach is evaluated extensively in terms of effectiveness on two social media datasets from Facebook and Twitter. With its significant performance improvement, we further investigate potential factors that contribute to the improved performance.
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '12, 2012
Most existing research about online trust assumes static trust relations between users. As we are... more Most existing research about online trust assumes static trust relations between users. As we are informed by social sciences, trust evolves as humans interact. Little work exists studying trust evolution in an online world. Researching online trust evolution faces unique challenges because more often than not, available data is from passive observation. In this paper, we leverage social science theories to develop a methodology that enables the study of online trust evolution. In particular, we propose a framework of evolution trust, eTrust, which exploits the dynamics of user preferences in the context of online product review. We present technical details about modeling trust evolution, and perform experiments to show how the exploitation of trust evolution can help improve the performance of online applications such as rating and trust prediction.
2010 IEEE International Conference on Data Mining, 2010
The increasing popularity of social media is shortening the distance between people. Social activ... more The increasing popularity of social media is shortening the distance between people. Social activities, e.g., tagging in Flickr, bookmarking in Delicious, twittering in Twitter, etc. are reshaping people's social life and redefining their social roles. People with shared interests tend to form their groups in social media, and users within the same community likely exhibit similar social behavior (e.g., going for the same movies, having similar political viewpoints), which in turn reinforces the community structure. The multiple interactions in social activities entail that the community structures are often overlapping, i.e., one person is involved in several communities. We propose a novel co-clustering framework, which takes advantage of networking information between users and tags in social media, to discover these overlapping communities. In our method, users are connected via tags and tags are connected to users. This explicit representation of users and tags is useful for understanding group evolution by looking at who is interested in what. The efficacy of our method is supported by empirical evaluation in both synthetic and online social networking data.
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Papers by Huiji Gao