Abstract
Collaborative filtering and content-based recommendation methods are two major approaches used in recommender systems. These two methods have some drawbacks in dealing with situations such as sparse data and cold start problems. Recently, combined methods were proposed to overcome these problems. However, a highly effective recommender system may still face a new challenge on interest drift. In this case, customer interests may change over time. For example, more recent users’ ratings on items may reflect more on users’ current interests than those of long time ago. Unfortunately, current available combination approaches do not consider this important factor and training data sets are regarded as static and time-insensitive. In this paper, we present a novel hybrid recommender system to overcome the interest drift problem by embedding the time-sensitive functions into the recommendation process. The users’ interests changing behaviours are considered with time function. Our experiments demonstrate a better performance than that of the collaborative filtering approaches considering interests drift and those of the combined approaches without considering interests drift.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Adomavicius, M.G., Tuzhilin, M.A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 734–749 (2005)
Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: SIGIR 2002. Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 253–260. ACM Press, New York, NY, USA (2002)
Huang, Z., Chen, H., Zeng, D.: Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans. Inf. Syst. 22, 116–142 (2004)
Balabanovi, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40, 66–72 (1997)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper (1999)
Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. In: Eighteenth national conference on Artificial intelligence, pp. 187–192. American Association for Artificial Intelligence, Menlo Park, CA, USA (2002)
Popescul, A., Ungar, L., Pennock, D., Lawrence, S.: Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In: 17th Conference on Uncertainty in Artificial Intelligence, pp. 437–444. Seattle, Washington (2001)
Ding, Y., Li, X.: Time weight collaborative filtering. In: CIKM 2005. Proceedings of the 14th ACM international conference on Information and knowledge management, pp. 485–492. ACM Press, New York, NY, USA (2005)
Li, X., Barajas, J.M., Ding, Y.: Collaborative filtering on streaming data with interest-drifting. International Journal of Intelligent Data Analysis 11(1), 75–87 (2007)
Hofmann, T.: Collaborative filtering via gaussian probabilistic latent semantic analysis. In: SIGIR 2003. Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, pp. 259–266. ACM Press, New York, NY, USA (2003)
Tang, T.Y., Winoto, P., Chan, K.C.C.: On the temporal analysis for improved hybrid recommendations. In: WI 2003. Proceedings of the IEEE/WIC International Conference on Web Intelligence, p. 214. IEEE Computer Society, Washington, DC, USA (2003)
Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive web search based on user profile constructed without any effort from users. In: WWW 2004. Proceedings of the 13th international conference on World Wide Web, pp. 675–684. ACM Press, New York, NY, USA (2004)
Ali, K., Stam, W.V.: TiVo: Making Show Recommendations Using a Distributed Collaborative Filtering Architecture. In: KDD 2004. Proceedings of The Tenth ACM SIGKDD Conference, Seattle, WA (2004)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender systems–a case study (2000)
Rojsattarat, E., Soonthornphisaj, N.: Hybrid Recommendation: Combining Content-Based Prediction and Collaborative Filtering. In: Liu, J., Cheung, Y.-m., Yin, H. (eds.) IDEAL 2003. LNCS, vol. 2690, pp. 337–344. Springer, Heidelberg (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, S., Li, X., Ding, Y., Orlowska, M.E. (2007). A Recommender System with Interest-Drifting. In: Benatallah, B., Casati, F., Georgakopoulos, D., Bartolini, C., Sadiq, W., Godart, C. (eds) Web Information Systems Engineering – WISE 2007. WISE 2007. Lecture Notes in Computer Science, vol 4831. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76993-4_55
Download citation
DOI: https://doi.org/10.1007/978-3-540-76993-4_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76992-7
Online ISBN: 978-3-540-76993-4
eBook Packages: Computer ScienceComputer Science (R0)