research-article

Time-aspect-sentiment Recommendation Models Based on Novel Similarity Measure Methods

Authors:

Nguyen Quoc Viet Hung,

Guanfeng LiuAuthors Info & Claims

ACM Transactions on the Web (TWEB), Volume 14, Issue 2

Article No.: 5, Pages 1 - 26

https://doi.org/10.1145/3375548

Published: 07 February 2020 Publication History

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.

References

[1]

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.

[2]

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.

Digital Library

[3]

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.

[4]

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.

[5]

Robert M. Bell and Yehuda Koren. 2007. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In ICDM. IEEE Computer Society, 43--52.

[6]

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.

[7]

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.

Digital Library

[8]

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.

[9]

Yi Ding and Xue Li. 2005. Time weight collaborative filtering. In CIKM. ACM, 485--492.

[10]

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.

[11]

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.

[12]

Chen Jian, Yin Jian, and Huang Jin. 2005. Automatic content-based recommendation in e-Commerce. In EEE. IEEE Computer Society, 748--753.

[13]

Yohan Jo and Alice H. Oh. 2011. Aspect and sentiment unification model for online review analysis. In WSDM. ACM, 815--824.

[14]

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.

[15]

Yehuda Koren. 2009. Collaborative filtering with temporal dynamics. Commun. In KDD. ACM, 447--456.

[16]

Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. IEEE Comput. 42, 8 (2009), 30--37.

Digital Library

[17]

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.

[18]

Bing Liu. 2012. Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers.

Digital Library

[19]

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.

[20]

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.

[21]

Julian J. McAuley and Jure Leskovec. 2013 Hidden factors and hidden topics: Understanding rating dimensions with review text. In RecSys. ACM, 165--172.

[22]

Anusree Mitra. 1995. Advertising and the stability of consideration sets over multiple purchase occasions. Int. J. Res. Market. 12, 1 (1995), 81--94.

[23]

Samaneh Moghaddam, Mohsen Jamali, and Martin Ester. 2012. ETF: Extended tensor factorization model for personalizing prediction of review helpfulness. In WSDM. ACM, 163--172.

[24]

Raymond J. Mooney and Loriene Roy. 2000. Content-based book recommending using learning for text categorization. In ACM DL. ACM, 195--204.

[25]

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.

[26]

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.

Digital Library

[27]

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.

Digital Library

[28]

Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In WWW. ACM, 811--820.

[29]

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.

[30]

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.

Digital Library

[31]

Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In WWW. ACM, 285--295.

[32]

Sylvain Senecal and Jacques Nantel. 2004. The influence of online product recommendations on consumers’ online choices. J. Retail. 80, 2 (2004), 159--169.

[33]

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.

[34]

Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Adv. Artific. Intell. 2009 (2009), 421425:1--421425:19.

[35]

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.

[36]

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.

[37]

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.

[38]

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.

[39]

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.

[40]

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.

[41]

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.

[42]

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.

Cited By

Wang YChen XFang JMeng ZLiang S(2023)Enhancing Conversational Recommendation Systems with Representation FusionACM Transactions on the Web10.1145/357703417:1(1-34)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1145/3577034
Liu GLi LLiu GWu X(2022)Social Group Query Based on Multi-Fuzzy-Constrained Strong SimulationACM Transactions on Knowledge Discovery from Data10.1145/348164016:3(1-27)Online publication date: 30-Jun-2022
https://dl.acm.org/doi/10.1145/3481640
An JLi GJiang W(2022)ThCoReInformation Sciences: an International Journal10.1016/j.ins.2022.05.023605:C(317-332)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.05.023
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Index Terms

Time-aspect-sentiment Recommendation Models Based on Novel Similarity Measure Methods
1. Information systems
  1. World Wide Web
    1. Web searching and information discovery
      1. Social recommendation

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Published In

cover image ACM Transactions on the Web

ACM Transactions on the Web Volume 14, Issue 2

May 2020

149 pages

ISSN:1559-1131

EISSN:1559-114X

DOI:10.1145/3382502

Editor:
Brian D. Davison
Lehigh University, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 February 2020

Accepted: 01 December 2019

Revised: 01 August 2019

Received: 01 March 2018

Published in TWEB Volume 14, Issue 2

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Cited By

Wang YChen XFang JMeng ZLiang S(2023)Enhancing Conversational Recommendation Systems with Representation FusionACM Transactions on the Web10.1145/357703417:1(1-34)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1145/3577034
Liu GLi LLiu GWu X(2022)Social Group Query Based on Multi-Fuzzy-Constrained Strong SimulationACM Transactions on Knowledge Discovery from Data10.1145/348164016:3(1-27)Online publication date: 30-Jun-2022
https://dl.acm.org/doi/10.1145/3481640
An JLi GJiang W(2022)ThCoReInformation Sciences: an International Journal10.1016/j.ins.2022.05.023605:C(317-332)Online publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.05.023
Ahmadian SJoorabloo NJalili MAhmadian M(2022)Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approachExpert Systems with Applications10.1016/j.eswa.2021.115849187(115849)Online publication date: Jan-2022
https://doi.org/10.1016/j.eswa.2021.115849
Ali WKumar JShao J(2022)Inferring context with reliable collaborators: a novel similarity estimation method for recommender systemsApplied Intelligence10.1007/s10489-022-03162-752:12(13883-13901)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s10489-022-03162-7
Dey ATian YGel Y(2022)Community detection in complex networksWIREs Computational Statistics10.1002/wics.156614:2Online publication date: 17-Mar-2022
https://dl.acm.org/doi/10.1002/wics.1566

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