Abstract
The explosive growth of e-commerce has led to the development of the recommendation system. The recommendation system aims to provide a set of items that meet users’ personalized needs through analyzing users’ consumption records. However, the timeliness of purchasing data and the implicity of feedback data pose severe challenges for the existing recommendation methods. To alleviate these challenges, we exploit the user’s consumption records from the perspectives of user and item, by modeling the data on both item and user level, where the item-level value reflects the grade of item, and the user-level value reflects the user’s purchase intention. In this article, we collect the description information and the reviews of the items from public websites, then adopt sentiment analysis techniques to model the similarities on user level and item level, respectively. In particular, we extend the traditional latent factor model and propose two novel methods—Item Level Similarity Matrix Factorization (ILMF) and User Level Similarity Matrix Factorization (ULMF)—by introducing two novel similarity measure methods. In ILMF and ULMF, the consistency between latent factors and explicit aspects is naturally incorporated into learning latent factors of the users and items, such that we can predict the users’ preferences on different items more accurately. Moreover, we propose Item-User Level Similarity Matrix Factorization (IULMF), which combines these two methods to study their contributions on the final performance. Experimental evaluations on the real datasets show that our methods outperform the baseline approaches in terms of both the precision and NDCG.
- F. Abbattista, M. Degemmis, N. Fanizzi, O. Licchelli, et al. 2007. Learning customer profiles for content-based filtering in e-commerce. Commun. ACM 50, 1 (Jan. 2007), 36--44. DOI:http://doi.acm.org/10.1145/1219092.1219093Google Scholar
- Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 6 (2005), 734--749. DOI:https://doi.org/10.1109/TKDE.2005.99Google Scholar
Digital Library
- Konstantin Bauman, Bing Liu, and Alexander Tuzhilin. 2017. Aspect based recommendations: Recommending items with the most valuable aspects based on user reviews. In KDD. ACM, 717--725. DOI:https://doi.org/10.1145/3097983.3098170Google Scholar
- Robert M. Bell and Yehuda Koren. 2007. Improved neighborhood-based collaborative filtering. In KDD Cup and Workshop at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 7--14.Google Scholar
- Robert M. Bell and Yehuda Koren. 2007. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In ICDM. IEEE Computer Society, 43--52. DOI:https://doi.org/10.1109/ICDM.2007.90Google Scholar
- David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3 (2003), 993--1022. Retrieved from http://www.jmlr.org/papers/v3/blei03a.html.Google Scholar
Digital Library
- Juan Cao, Xia Tian, Jintao Li, Yongdong Zhang, and Tang Sheng. 2009. A density-based method for adaptive LDA model selection. Neurocomputing 72, 7 (2009), 1775--1781.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 KDD. ACM, 193--202. DOI:https://doi.org/10.1145/2623330.2623758Google Scholar
- Yi Ding and Xue Li. 2005. Time weight collaborative filtering. In CIKM. ACM, 485--492. DOI:https://doi.org/10.1145/1099554.1099689Google Scholar
- Horatiu Dumitru, Marek Gibiec, Negar Hariri, Jane Cleland-Huang, Bamshad Mobasher, Carlos Castro-Herrera, and Mehdi Mirakhorli. 2011. On-demand feature recommendations derived from mining public product descriptions. In ICSE. ACM, 181--190. DOI:https://doi.org/10.1145/1985793.1985819Google Scholar
- Zhen Hai, Gao Cong, Kuiyu Chang, Wenting Liu, and Peng Cheng. 2014. Coarse-to-fine review selection via supervised joint aspect and sentiment model. In SIGIR. ACM, 617--626. DOI:https://doi.org/10.1145/2600428.2609570Google Scholar
- Chen Jian, Yin Jian, and Huang Jin. 2005. Automatic content-based recommendation in e-Commerce. In EEE. IEEE Computer Society, 748--753. DOI:https://doi.org/10.1109/EEE.2005.37Google Scholar
- Yohan Jo and Alice H. Oh. 2011. Aspect and sentiment unification model for online review analysis. In WSDM. ACM, 815--824. DOI:https://doi.org/10.1145/1935826.1935932Google Scholar
- Thorsten Joachims, Dayne Freitag, and Tom M. Mitchell. 1997. Web watcher: A tour guide for the world wide web. In IJCAI (1). Morgan Kaufmann, 770--777.Google Scholar
- Yehuda Koren. 2009. Collaborative filtering with temporal dynamics. Commun. In KDD. ACM, 447--456. DOI:https://doi.org/10.1145/1721654.1721677Google Scholar
- Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. IEEE Comput. 42, 8 (2009), 30--37. DOI:https://doi.org/10.1109/MC.2009.263Google Scholar
Digital Library
- Daniel D. Lee and H. Sebastian Seung. 2000. Algorithms for non-negative matrix factorization. In NIPS. MIT Press, 556--562. Retrieved from http://papers.nips.cc/paper/1861-algorithms-for-non-negative-matrix-factorization.Google Scholar
- Bing Liu. 2012. Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers. DOI:https://doi.org/10.2200/S00416ED1V01Y201204HLT016Google Scholar
Digital Library
- Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the next location: A recurrent model with spatial and temporal contexts. In AAAI. AAAI Press, 194--200. Retrieved from http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11900.Google Scholar
- Yue Lu, Malú Castellanos, Umeshwar Dayal, and Cheng Xiang Zhai. 2011. Automatic construction of a context-aware sentiment lexicon: An optimization approach. In WWW. ACM, 347--356. DOI:https://doi.org/10.1145/1963405.1963456Google Scholar
- Julian J. McAuley and Jure Leskovec. 2013 Hidden factors and hidden topics: Understanding rating dimensions with review text. In RecSys. ACM, 165--172. DOI:https://doi.org/10.1145/2507157.2507163Google Scholar
- Anusree Mitra. 1995. Advertising and the stability of consideration sets over multiple purchase occasions. Int. J. Res. Market. 12, 1 (1995), 81--94.Google Scholar
Cross Ref
- Samaneh Moghaddam, Mohsen Jamali, and Martin Ester. 2012. ETF: Extended tensor factorization model for personalizing prediction of review helpfulness. In WSDM. ACM, 163--172. DOI:https://doi.org/10.1145/2124295.2124316Google Scholar
- Raymond J. Mooney and Loriene Roy. 2000. Content-based book recommending using learning for text categorization. In ACM DL. ACM, 195--204. DOI:https://doi.org/10.1145/336597.336662Google Scholar
- Rong Pan, Yunhong Zhou, Bin Cao, Nathan Nan Liu, Rajan M. Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In ICDM. IEEE Computer Society, 502--511. DOI:https://doi.org/10.1109/ICDM.2008.16Google Scholar
- Weike Pan, Qiang Yang, Wanling Cai, Yaofeng Chen, Qing Zhang, Xiaogang Peng, and Zhong Ming. 2019. Transfer to rank for heterogeneous one-class collaborative filtering. ACM Trans. Inf. Syst. 37, 1 (2019), 10:1--10:20.Google Scholar
Digital Library
- Guang Qiu, Bing Liu, Jiajun Bu, and Chun Chen. 2011. Opinion word expansion and target extraction through double propagation. Comput. Ling. 37, 1 (2011), 9--27. DOI:https://doi.org/10.1162/coli_a_00034Google Scholar
Digital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In WWW. ACM, 811--820. DOI:https://doi.org/10.1145/1772690.1772773Google Scholar
- Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: An open architecture for collaborative filtering of netnews. In CSCW. ACM, 175--186. DOI:https://doi.org/10.1145/192844.192905Google Scholar
- Ruslan Salakhutdinov and Andriy Mnih. 2008. Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In ICML (ACM International Conference Proceeding Series), Vol. 307. ACM, 880--887. DOI:https://doi.org/10.1145/1390156.1390267Google Scholar
Digital Library
- Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In WWW. ACM, 285--295. DOI:https://doi.org/10.1145/371920.372071Google Scholar
- Sylvain Senecal and Jacques Nantel. 2004. The influence of online product recommendations on consumers’ online choices. J. Retail. 80, 2 (2004), 159--169.Google Scholar
Cross Ref
- Allan D. Shocker, Moshe Ben-Akiva, Bruno Boccara, and Prakash Nedungadi. 1991. Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions. Market. Lett. 2, 3 (1991), 181--197.Google Scholar
Cross Ref
- Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Adv. Artific. Intell. 2009 (2009), 421425:1--421425:19. DOI:https://doi.org/10.1155/2009/421425Google Scholar
- Tiffany Ya Tang, Pinata Winoto, and Keith C. C. Chan.2003. On the temporal analysis for improved hybrid recommendations. In Web Intelligence. IEEE Computer Society, 214--220.Google Scholar
- Hongning Wang, Yue Lu, and Chengxiang Zhai. 2010. Latent aspect rating analysis on review text data: A rating regression approach. In KDD. ACM, 783--792. DOI:https://doi.org/10.1145/1835804.1835903Google Scholar
- 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 KDD. ACM, 723--732. DOI:https://doi.org/10.1145/1835804.1835896Google Scholar
- Taebok Yoon, Seunghoon Lee, Kwang ho Yoon, Dongmoon Kim, and Jee-Hyong Lee. 2008. A personalized music recommendation system with a time-weighted clustering. 2008 4th International IEEE Conference Intelligent Systems 2 (2008), 10--48.Google Scholar
Cross Ref
- Sheng Zhang, Weihong Wang, James Ford, Fillia Makedon, and Justin D. Pearlman. 2005. Using singular value decomposition approximation for collaborative filtering. In CEC. IEEE Computer Society, 257--264. DOI:https://doi.org/10.1109/ICECT.2005.102Google Scholar
- Yongfeng Zhang, Guokun Lai, Min Zhang, Yi Zhang, Yiqun Liu, and Shaoping Ma. 2014. Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In SIGIR. ACM, 83--92. DOI:https://doi.org/10.1145/2600428.2609579Google Scholar
- Yongfeng Zhang, Min Zhang, Yi Zhang, Guokun Lai, Yiqun Liu, Honghui Zhang, and Shaoping Ma. 2015. Daily-aware personalized recommendation based on feature-level time series analysis. In WWW. ACM, 1373--1383. DOI:https://doi.org/10.1145/2736277.2741087Google Scholar
- Kaiqi Zhao, Gao Cong, Quan Yuan, and Kenny Q. Zhu. 2015. SAR: A sentiment-aspect-region model for user preference analysis in geo-tagged reviews. In ICDE. IEEE Computer Society, 675--686. DOI:https://doi.org/10.1109/ICDE.2015.7113324Google Scholar
Index Terms
Time-aspect-sentiment Recommendation Models Based on Novel Similarity Measure Methods
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