Abstract
In recommender systems, the cold-start problem is a common challenge. When a new item has no ratings, it becomes difficult to relate it to other items or users. In this paper, we address the cold-start problem and propose to leverage on social-media trends and reputations to improve the recommendation of new items. The proposed framework models the long-term reputation of actors and directors, to better characterize new movies. In addition, movies popularity are deduced from social-media trends that are related to the corresponding new movie. A principled method is then applied to infer cold-start recommendations from these social-media signals. Experiments on a realistic time-frame, covering several movie-awards events between January 2014 and March 2014, showed significant improvements over ratings-only and metadata-only based recommendations.
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References
Peleja, F., Dias, P., Martins, F., Magalhães, J.: A recommender system for the TV on the web: integrating unrated reviews and movie ratings. Multimedia Syst. 19, 543–558 (2013)
Dias, P., Magalhaes, J.: Multi-user diverse recommendations through greedy vertex-angle maximization. In: International Symposium on Intelligent Data Analysis, pp. 96–107 (2014)
Mathioudakis, M., Koudas, N.: Twittermonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 1155–1158 (2010)
Li, C., Sun, A., Datta, A.: Twevent: segment-based event detection from tweets. In: Proceedings of the 21st ACM international conference on Information and knowledge management, pp. 155–164 (2012)
Ozdikis, O., Senkul, P., Oguztuzun, H.: Semantic expansion of hashtags for enhanced event detection in twitter. In: Proceedings of the 1st International Workshop on Online Social Systems (2012)
Krauss, J., Nann, S., Simon, D., Gloor, P.A., Fischbach, K.: Predicting movie success and academy awards through sentiment and social network analysis. In: ECIS, pp. 2026–2037 (2008)
Joshi, M., Das, D., Gimpel, K., Smith, N.A.: Movie reviews and revenues: an experiment in text regression. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 293–296. Association for Computational Linguistics, Stroudsburg, PA, USA (2010)
Asur, S., Huberman, B.A.: Predicting the future with social media. In: Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on, pp. 492–499 (2010)
Oghina, A., Breuss, M., Tsagkias, M., de Rijke, M.: Predicting IMDB Movie Ratings Using Social Media. In: Baeza-Yates, R., de Vries, Arjen P., Zaragoza, H., Cambazoglu, B.Barla, Murdock, V., Lempel, R., Silvestri, F. (eds.) ECIR 2012. LNCS, vol. 7224, pp. 503–507. Springer, Heidelberg (2012). doi:10.1007/978-3-642-28997-2_51
Moshfeghi, Y., Piwowarski, B., Jose, J.M.: Handling data sparsity in collaborative filtering using emotion and semantic based features. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR 2011, p. 625. ACM Press, New York (2011)
Jakob, N., Weber, S.-H., Müller, M.-C., Gurevych, I.: Beyond the stars: exploiting free-text user reviews for improving the accuracy of movie recommendations. In: Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, Hong Kong, pp. 57–64 (2009)
Zhang, W., Ding, G., Chen, L., Li, C.: Augmenting online video recommendations by fusing review sentiment classification. In: Workshop on Recommender Systems and the Social Web (2010)
Amatriain, X., Lathia, N., Pujol, J.M., Kwak, H., Oliver, N.: The wisdom of the few: a collaborative filtering approach based on expert opinions from the web. In: Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, pp. 532–539 (2009)
Peleja, F., Santos, J., Magalhães, J.: Ranking linked-entities in a sentiment graph. In: The 2014 IEEE/WIC/ACM International Conference on Web Intelligence (2014)
Acknowledgements
This work has been partially funded by the CMU Portugal research project GoLocal Ref. CMUP-ERI/TIC/0033/2014, by the H2020 ICT project COGNITUS with the grant agreement No 687605 and by the project NOVA LINCS Ref. UID/CEC/04516/2013.
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Santos, J., Peleja, F., Martins, F., Magalhães, J. (2017). Improving Cold-Start Recommendations with Social-Media Trends and Reputations. In: Adams, N., Tucker, A., Weston, D. (eds) Advances in Intelligent Data Analysis XVI. IDA 2017. Lecture Notes in Computer Science(), vol 10584. Springer, Cham. https://doi.org/10.1007/978-3-319-68765-0_25
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DOI: https://doi.org/10.1007/978-3-319-68765-0_25
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