How Hidden Aspects Can Improve Recommendation?
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Abstract
Nowadays, more and more people are using online news platforms as their main source of information about daily life events. Users of such platforms discuss around topics providing new insights and sometimes revealing hidden aspects about topics. The valuable information provided by users needs to be exploited to improve the accuracy of news recommendation and thus keep users always motivated to provide comments. However, exploiting user generated content is very challenging due its noisy nature. In this paper, we address this problem by proposing a novel news recommendation system that (1) enrich the profile of news article with user generated content, (2) deal with noisy contents by proposing a ranking model for users’ comments, and (3) propose a diversification model for comments to remove redundancies and provide a wide coverage of topic aspects. The results show that our approach outperforms baseline approaches achieving high accuracy.
Keywords
News recommendation Opinion mining DiversificationPreview
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References
- 1.Abbar, S., Amer-Yahia, S., Indyk, P., Mahabadi, S.: Real-time recommendation of diverse related articles. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013, Republic and Canton of Geneva, Switzerland, pp. 1–12. International World Wide Web Conferences Steering Committee (2013)Google Scholar
- 2.Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM, pp. 5–14 (2009)Google Scholar
- 3.Danescu-Niculescu-Mizil, C., Kossinets, G., Kleinberg, J., Lee, L.: How opinions are received by online communities: a case study on amazon.com helpfulness votes. In: Proceedings of the 18th international conference on World Wide Web, WWW 2009, pp. 141–150. ACM, New York (2009)Google Scholar
- 4.Ganesan, K., Zhai, C.: Opinion-based entity ranking. Inf. Retr. 15(2), 116–150 (2012)CrossRefGoogle Scholar
- 5.Gollapudi, S., Sharma, A.: An axiomatic approach for result diversification. In: WWW, pp. 381–390 (2009)Google Scholar
- 6.Gollapudi, S., Sharma, A.: An axiomatic approach for result diversification. In: Proceedings of the 18th International Conference on World Wide Web, WWW 2009, pp. 381–390. ACM, New York (2009)Google Scholar
- 7.Hassin, R., Rubinstein, S., Tamir, A.: Approximation algorithms for maximum dispersion. Operations Research Letters 21, 133–137 (1997)MathSciNetCrossRefGoogle Scholar
- 8.Hong, Y., Lu, J., Yao, J., Zhu, Q., Zhou, G.: What reviews are satisfactory: novel features for automatic helpfulness voting. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 495–504. ACM, New York (2012)Google Scholar
- 9.Hu, M., Sun, A., Lim, E.-P.: Comments-oriented document summarization: Understanding documents with readers’ feedback. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 291–298. ACM, New York (2008)Google Scholar
- 10.Kacimi, M., Gamper, J.: Diversifying search results of controversial queries. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 93–98. ACM, New York (2011)Google Scholar
- 11.Kant, R., Sengamedu, S.H., Kumar, K.S.: Comment spam detection by sequence mining. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM 2012, pp. 183–192. ACM, New York (2012)Google Scholar
- 12.Kim, S., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 423–430 (2006)Google Scholar
- 13.Korte, B., Hausmann, D.: An analysis of the greedy heuristic for independence systems. Annals of Discrete Mathematics 2, 65–74 (1978)MathSciNetCrossRefGoogle Scholar
- 14.Lin, C., He, Y.: Joint sentiment/topic model for sentiment analysis. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 375–384. ACM, New York (2009)Google Scholar
- 15.Litvak, M., Matz, L.: Smartnews: Bringing order into comments chaos. In: Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, KDIR, vol. 13 (2013)Google Scholar
- 16.Meguebli, Y., Kacimi, M., Doan, B.-L., Popineau, F.: Building rich user profiles for personalized news recommendation. In: Proceedings of 2nd International Workshop on News Recommendation and Analytics (2014)Google Scholar
- 17.Meguebli, Y., Kacimi, M., Doan, B.-L., Popineau, F.: Unsupervised approach for identifying users’ political orientations. In: de Rijke, M., Kenter, T., de Vries, A.P., Zhai, C., de Jong, F., Radinsky, K., Hofmann, K. (eds.) ECIR 2014. LNCS, vol. 8416, pp. 507–512. Springer, Heidelberg (2014)CrossRefGoogle Scholar
- 18.Real, R., Vargas, J.M.: The probabilistic basis of jaccard’s index of similarity. Systematic Biology 45(3), 380–385 (1996)CrossRefGoogle Scholar
- 19.Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2009, Arlington, Virginia, United States, pp. 452–461. AUAI Press (2009)Google Scholar
- 20.Shmueli, E., Kagian, A., Koren, Y., Lempel, R.: Care to comment?: Recommendations for commenting on news stories. In: Proceedings of the 21st International Conference on World Wide Web, WWW 2012, pp. 429–438. ACM, New York (2012)Google Scholar
- 21.Terra, E., Clarke, C.L.A.: Frequency estimates for statistical word similarity measures. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, NAACL 2003, pp. 165–172. Association for Computational Linguistics, Stroudsburg (2003)Google Scholar
- 22.Tsagkias, M., Weerkamp, W., de Rijke, M.: Predicting the volume of comments on online news stories. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 1765–1768. ACM, New York (2009)Google Scholar
- 23.Tsagkias, M., Weerkamp, W., de Rijke, M.: News comments:Exploring, modeling, and online prediction. In: Gurrin, C., He, Y., Kazai, G., Kruschwitz, U., Little, S., Roelleke, T., Rüger, S., van Rijsbergen, K. (eds.) ECIR 2010. LNCS, vol. 5993, pp. 191–203. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 24.Tsur, O., Rappoport, A.: Revrank: A fully unsupervised algorithm for selecting the most helpful book reviews. In: International AAAI Conference on Weblogs and Social Media (2009)Google Scholar
- 25.Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: A rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 783–792. ACM, New York (2010)Google Scholar
- 26.Yee, W.G., Yates, A., Liu, S., Frieder, O.: Are web user comments useful for search. In: Proc. LSDS-IR, pp. 63–70 (2009)Google Scholar
- 27.Zhuang, L., Jing, F., Zhu, X.-Y.: Movie review mining and summarization. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, CIKM 2006, pp. 43–50. ACM, New York (2006)Google Scholar