Abstract
Users’ preferences, and consequently their ratings and reviews to items, change over time. Likewise, characteristics of items are also time-varying. By dividing data into time periods, temporal Recommender Systems (RSs) improve recommendation accuracy by exploring the temporal dynamics in user rating data. However, temporal RSs have to cope with rating sparsity in each time period. Meanwhile, reviews generated by users contain rich information about their preferences, which can be exploited to address rating sparsity and further improve the performance of temporal RSs. In this article, we develop a temporal rating model with topics that jointly mines the temporal dynamics of both user-item ratings and reviews. Studying temporal drifts in reviews helps us understand item rating evolutions and user interest changes over time. Our model also automatically splits the review text in each time period into interim words and intrinsic words. By linking interim words and intrinsic words to short-term and long-term item features, respectively, we jointly mine the temporal changes in user and item latent features together with the associated review text in a single learning stage. Through experiments on 28 real-world datasets collected from Amazon, we show that the rating prediction accuracy of our model significantly outperforms the existing state-of-art RS models. And our model can automatically identify representative interim words in each time period as well as intrinsic words across all time periods. This can be very useful in understanding the time evolution of users’ preferences and items’ characteristics.
- Robert M. Bell and Yehuda Koren. 2007. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In Proceedings of the 7th IEEE International Conference on Data Mining (ICDM’07). IEEE, 43--52. Google Scholar
Digital Library
- David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3 (2003), 993--1022. Google Scholar
Digital Library
- Pedro G. Campos, Fernando Díez, and Iván Cantador. 2014. Time-aware recommender systems: A comprehensive survey and analysis of existing evaluation protocols. User Model. User-Adapt. Interact. 24, 1--2 (2014), 67--119. Google Scholar
Digital Library
- Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J. Smola, Jing Jiang, and Chong Wang. 2014. Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 193--202. Google Scholar
Digital Library
- Michele Gorgoglione, Umberto Panniello, and Alexander Tuzhilin. 2011. The effect of context-aware recommendations on customer purchasing behavior and trust. In Proceedings of the 5th ACM Conference on Recommender Systems. ACM, 85--92. Google Scholar
Digital Library
- Thomas L. Griffiths and Mark Steyvers. 2004. Finding scientific topics. Proc. National. Acad. Sci. U.S.A. 101, suppl. 1 (2004), 5228--5235.Google Scholar
Cross Ref
- Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. 2015. Trirank: Review-aware explainable recommendation by modeling aspects. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 1661--1670. Google Scholar
Digital Library
- Christoph Hermann. 2010. Time-based recommendations for lecture materials. In Proceedings of the 2010 World Conference on Educational Multimedia, Hypermedia, and Telecommunications. 1028--1033.Google Scholar
- Thomas Hofmann. 2004. Latent semantic models for collaborative filtering. ACM Trans. Info. Syst. (TOIS) 22, 1 (2004), 89--115. Google Scholar
Digital Library
- Guang-Neng Hu, Xin-Yu Dai, Yunya Song, Shu-Jian Huang, and Jia-Jun Chen. 2016. A synthetic approach for recommendation: Combining ratings, social relations, and reviews. arXiv:1601.02327 (2016). Google Scholar
Digital Library
- Tereza Iofciu and Gianluca Demartini. 2009. Time based tag recommendation using direct and extended users sets. ECML PKDD Disc. Chall. 209 (2009), 99--107. Google Scholar
Digital Library
- Paul B. Kantor, Lior Rokach, Francesco Ricci, and Bracha Shapira. 2011. Recommender Systems Handbook. Springer.Google Scholar
- Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 426--434. Google Scholar
Digital Library
- Yehuda Koren. 2010. Collaborative filtering with temporal dynamics. Commun. ACM 53, 4 (2010), 89--97. Google Scholar
Digital Library
- Neal Lathia, Stephen Hailes, and Licia Capra. 2009. Temporal collaborative filtering with adaptive neighbourhoods. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 796--797. Google Scholar
Digital Library
- Guang Ling, Michael R. Lyu, and Irwin King. 2014. Ratings meet reviews, a combined approach to recommend. In Proceedings of the 8th ACM Conference on Recommender systems. ACM, 105--112. Google Scholar
Digital Library
- Hao Ma, Haixuan Yang, Michael R. Lyu, and Irwin King. 2008. Sorec: Social recommendation using probabilistic matrix factorization. In Proceedings of the 17th ACM Conference on Information and Knowledge Management. ACM, 931--940. Google Scholar
Digital Library
- Benjamin Marlin, Richard S. Zemel, Sam Roweis, and Malcolm Slaney. 2012. Collaborative filtering and the missing at random assumption. arXiv:1206.5267 (2012). Google Scholar
Digital Library
- Yasuko Matsubara, Yasushi Sakurai, Christos Faloutsos, Tomoharu Iwata, and Masatoshi Yoshikawa. 2012. Fast mining and forecasting of complex time-stamped events. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 271--279. Google Scholar
Digital Library
- Julian McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: Understanding rating dimensions with review text. In Proceedings of the 7th ACM Conference on Recommender Systems. ACM, 165--172. Google Scholar
Digital Library
- Julian John McAuley and Jure Leskovec. 2013. From amateurs to connoisseurs: Modeling the evolution of user expertise through online reviews. In Proceedings of the 22nd International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 897--908. Google Scholar
Digital Library
- Qiaozhu Mei, Xu Ling, Matthew Wondra, Hang Su, and ChengXiang Zhai. 2007. Topic sentiment mixture: Modeling facets and opinions in weblogs. In Proceedings of the 16th International Conference on World Wide Web. ACM, 171--180. Google Scholar
Digital Library
- Andriy Mnih and Ruslan Salakhutdinov. 2007. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems. 1257--1264. Google Scholar
Digital Library
- Jorge Nocedal. 1980. Updating quasi-Newton matrices with limited storage. Math. Comput. 35, 151 (1980), 773--782.Google Scholar
- Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD Cup and Workshop, Vol. 2007. 5--8.Google Scholar
- H. Pragarauskas and Oliver Gross. 2010. Temporal collaborative filtering with Bayesian probabilistic tensor factorization. (2010).Google Scholar
- Steffen Rendle. 2012. Factorization machines with libfm. ACM Trans. Intell. Syst. Technol. (TIST) 3, 3 (2012), 57. Google Scholar
Digital Library
- Ruslan Salakhutdinov and Andriy Mnih. 2008. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In Proceedings of the 25th International Conference on Machine Learning. ACM, 880--887. Google Scholar
Digital Library
- Karen Sparck Jones. 1972. A statistical interpretation of term specificity and its application in retrieval. J. Document. 28, 1 (1972), 11--21.Google Scholar
Cross Ref
- Chong Wang and David M. Blei. 2011. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 448--456. Google Scholar
Digital Library
- Hongning Wang, Yue Lu, and ChengXiang Zhai. 2011. Latent aspect rating analysis without aspect keyword supervision. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 618--626. Google Scholar
Digital Library
- Ho Chung Wu, Robert Wing Pong Luk, Kam Fai Wong, and Kui Lam Kwok. 2008. Interpreting tf-idf term weights as making relevance decisions. ACM Trans. Info. Syst. (TOIS) 26, 3 (2008), 13. Google Scholar
Digital Library
- Liang Xiang, Quan Yuan, Shiwan Zhao, Li Chen, Xiatian Zhang, Qing Yang, and Jimeng Sun. 2010. Temporal recommendation on graphs via long-and short-term preference fusion. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 723--732. Google Scholar
Digital Library
- Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hongyuan Zha, and Zhaohui Zheng. 2011. Collaborative competitive filtering: Learning recommender using context of user choice. In Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 295--304. Google Scholar
Digital Library
- Wayne Xin Zhao, Jing Jiang, Hongfei Yan, and Xiaoming Li. 2010. Jointly modeling aspects and opinions with a MaxEnt-LDA hybrid. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 56--65. Google Scholar
Digital Library
Index Terms
Recommendation in a Changing World: Exploiting Temporal Dynamics in Ratings and Reviews
Recommendations
Recommend at opportune moments
AIRS'11: Proceedings of the 7th Asia conference on Information Retrieval TechnologyWe propose an approach to adapt the existing item-based (movie-based) collaborative filtering algorithm based on the timestamp of ratings to recommend movies to users at opportune moments. Over the last few years, researchers focused recommendation ...
New Recommendation Techniques for Multicriteria Rating Systems
Traditional single-rating recommender systems have been successful in a number of personalization applications, but the research area of multicriteria recommender systems has been largely untouched. Taking full advantage of multicriteria ratings in ...
A framework for diversifying recommendation lists by user interest expansion
Recommender systems have been widely used to discover users' preferences and recommend interesting items to users during this age of information overload. Researchers in the field of recommender systems have realized that the quality of a top-N ...






Comments