Abstract
Cold start problem is a key challenge in recommendation system as new users are always present. Most of existing approaches address this problem by leveraging meta data to estimate the tastes of new user. Recently, social network has been becoming an integral part of daily life. Usually, social network information reflect users preferences to some extent, combining this kind of data would contribute to address the cold start problem. Existing approaches of this kind are either leverage relationships between users or utilize meta data such as demographic information. The huge textual information in social network has been neglected. In this paper, we propose a novel recommendation framework, in which the textual data in social network are used to improve the recommendation accuracy for new users. In particularly, both of new user’s interests and items are modeled by mining the textual data in social network. Experimental results demonstrate that our approach is superior to other baseline methods in both precision and diversity.
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References
Ahn, H.J.: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf. Sci. 178(1), 37–51 (2008)
Chen, C.C., Wan, Y.H., Chung, M.C., Sun, Y.C.: An effective recommendation method for cold start new users using trust and distrust networks. Inf. Sci. 224, 19–36 (2013)
Victor, P., Cornelis, C., De Cock, M., Teredesai, A.M.: Key figure impact in trust-enhanced recommender systems. AI Commun. 21(2), 127–143 (2008)
Zhang, D.Q., Hsu, C.H., Chen, M., Chen, Q., Xiong, N., Lloret, J.: Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems. IEEE Trans. Emerg. Top.Comput. 2(2), 239–250 (2014)
Schein, A.I., Popescul, A., Ungar, L., Ungar, H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 253–260, (2002)
Bobadilla, J., Ortega, F., Hernando, A., Bernal, J.: A collaborative filtering approach to mitigate the new user cold start problem. Knowl.-Based Syst. 26, 225–238 (2012)
Phelan, O., McCarthy, K., Bennett, M., Smyth, B.: Terms of a feather: content-based news recommendation and discovery using twitter. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 448–459. Springer, Heidelberg (2011)
Park, S., Chu, W.: Pairwise preference regression for cold-start recommendation. In: Proceedings of the third ACM Conference on Recommender Systems, pp. 21–28 (2009)
Zhou, K., Yang, S.H., Zha, H.Y.: Functional matrix factorizations for cold-start recommendation. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp. 315–324 (2011)
Claypool, M., Gokhale, A., Miranda, T., Murnikov, P., Netes, D., Sartin, M.: Combining content-based and collaborative filters in an online newspaper. In: Proceedings of ACM SIGIR Workshop on Recommender Systems, vol. 60 (1999)
Lin, J., Sugiyama, K., Kan, M., Chua, T.: Addressing cold-start in app recommendation: Latent user models constructed from twitter followers. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 283–292 (2013)
Su, X.Y., Khoshgoftaar, T.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 4, 2009 (2009)
Liu, N., Meng, X.R., Liu, C., Yang, Q.: Wisdom of the better few: cold start recommendation via representative based rating elicitation. In: Proceedings of the fifth ACM Conference on Recommender Systems, pp. 37–44 (2011)
Zhang, M., Tang, J., Zhang, X.C., Xue, X.Y.: Addressing cold start in recommender systems: a semi-supervised co-training algorithm. In: Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 73–82 (2014)
Kim, B.M., Li, Q.: Probabilistic model estimation for collaborative filtering based on items attributes. In: Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 185–191 (2004)
Yu, K., Schwaighofer, A., Tresp, V.: Probabilistic memory-based collaborative filtering. IEEE Trans. Knowl. Data Eng. 16(1), 56–69 (2004)
Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (Grant Nos. 61170189, 61370126, 61202239), National High Technology Research and Development Program of China under grant (No. 2015AA016004), the Fund of the State Key Laboratory of Software Development Environment (No. SKLSDE-2015ZX-16), and Microsoft Research Asia Fund (No. FY14-RES-OPP-105).
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Li, C., Wang, F., Yang, Y., Li, Z., Zhang, X. (2015). Exploring Social Network Information for Solving Cold Start in Product Recommendation. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9419. Springer, Cham. https://doi.org/10.1007/978-3-319-26187-4_24
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DOI: https://doi.org/10.1007/978-3-319-26187-4_24
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