skip to main content
10.1145/3428757.3429125acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiiwasConference Proceedingsconference-collections
research-article

Making Use of Reviews for Good Explainable Recommendation

Published: 27 January 2021 Publication History

Abstract

Reviews are used in generating explainable recommendation. However, the use of reviews has so far not been adequately addressed. In this paper, we examine methods that make use of reviews effectively. There is a trade-off between the number and quality of reviews to use, that is, we should like to use reviews as many as possible to generate explainable recommendation, however in a large number of reviews there can be low quality ones, which can cause low quality explainable recommendation generation. We discuss new methods that use not only reviews written by a user but also those utilized by the user to generate good explainable recommendation. Our methods can be applied to different explainable recommender approaches, which is shown by adopting two state-of-the-art explainable recommender approaches in this paper. Experimental results demonstrate that our methods can be of benefit to existing explainable recommender approaches as regards both recommendation and its explanation qualities.

References

[1]
Ramesh Baral, XiaoLong Zhu, S. S. Iyengar, and Tao Li. 2018. ReEL: Review Aware Explanation of Location Recommendation. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization (Singapore, Singapore) (UMAP '18). Association for Computing Machinery, New York, NY, USA, 23--32. https://doi.org/10.1145/3209219.3209237
[2]
Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural Attentional Rating Regression with Review-Level Explanations. In Proceedings of the 2018 World Wide Web Conference (Lyon, France) (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1583--1592. https://doi.org/10.1145/3178876.3186070
[3]
Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. 2016. Learning to Rank Features for Recommendation over Multiple Categories. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (Pisa, Italy) (SIGIR '16). Association for Computing Machinery, New York, NY, USA, 305--314. https://doi.org/10.1145/2911451.2911549
[4]
Xu Chen, Yongfeng Zhang, and Zheng Qin. 2019. Dynamic Explainable Recommendation Based on Neural Attentive Models. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. AAAI Press, 53--60. https://doi.org/10.1609/aaai.v33i01.330153
[5]
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 (Melbourne, Australia) (CIKM '15). Association for Computing Machinery, New York, NY, USA, 1661--1670. https://doi.org/10.1145/2806416.2806504
[6]
Yu Hong, Jun Lu, Jianmin Yao, Qiaoming Zhu, and Guodong Zhou. 2012. 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 (Portland, Oregon, USA) (SIGIR '12). Association for Computing Machinery, New York, NY, USA, 495--504. https://doi.org/10.1145/2348283.2348351
[7]
Alexandros Karatzoglou, Xavier Amatriain, Linas Baltrunas, and Nuria Oliver. 2010. Multiverse Recommendation: N-Dimensional Tensor Factorization for Context-Aware Collaborative Filtering. In Proceedings of the Fourth ACM Conference on Recommender Systems (Barcelona, Spain) (RecSys '10). Association for Computing Machinery, New York, NY, USA, 79--86. https://doi.org/10.1145/1864708.1864727
[8]
Tamara G. Kolda and Brett W. Bader. 2009. Tensor Decompositions and Applications. SIAM Rev. 51, 3 (2009), 455--500. https://doi.org/10.1137/07070111X
[9]
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (Aug. 2009), 30--37. https://doi.org/10.1109/MC.2009.263
[10]
Yue Lu, Malu Castellanos, Umeshwar Dayal, and Cheng Xiang Zhai. 2011. Automatic Construction of a Context-Aware Sentiment Lexicon: An Optimization Approach. In Proceedings of the 20th International Conference on World Wide Web (Hyderabad, India) (WWW '11). Association for Computing Machinery, New York, NY, USA, 347--356. https://doi.org/10.1145/1963405.1963456
[11]
Yichao Lu, Ruihai Dong, and Barry Smyth. 2018. Why I like It: Multi-Task Learning for Recommendation and Explanation. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys '18). Association for Computing Machinery, New York, NY, USA, 4--12. https://doi.org/10.1145/3240323.3240365
[12]
Yue Lu, Panayiotis Tsaparas, Alexandros Ntoulas, and Livia Polanyi. 2010. Exploiting Social Context for Review Quality Prediction. In Proceedings of the 19th International Conference on World Wide Web (Raleigh, North Carolina, USA) (WWW '10). Association for Computing Machinery, New York, NY, USA, 691--700. https://doi.org/10.1145/1772690.1772761
[13]
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 (Hong Kong, China) (RecSys '13). Association for Computing Machinery, New York, NY, USA, 165--172. https://doi.org/10.1145/2507157.2507163
[14]
Samaneh Moghaddam, Mohsen Jamali, and Martin Ester. 2012. ETF: Extended Factorization Model for Personalizing Prediction of Review Helpfulness. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (Seattle, Washington, USA) (WSDM '12). Association for Computing Machinery, New York, NY, USA, 163--172. https://doi.org/10.1145/2124295.2124316
[15]
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller. 2018. Methods for interpreting and understanding deep neural networks. Digital Signal Processing 73 (2018), 1--15. https://doi.org/10.1016/j.dsp.2017.10.011
[16]
Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (Montreal, Quebec, Canada) (UAI '09). AUAI Press, Arlington, Virginia, USA, 452--461. https://arxiv.org/abs/1205.2618
[17]
Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu. 2017. Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction. In Proceedings of the Eleventh ACM Conference on Recommender Systems (Como, Italy) (RecSys '17). Association for Computing Machinery, New York, NY, USA, 297--305. https://doi.org/10.1145/3109859.3109890
[18]
Jiliang Tang, Huiji Gao, Xia Hu, and Huan Liu. 2013. Context-Aware Review Helpfulness Rating Prediction. In Proceedings of the 7th ACM Conference on Recommender Systems (Hong Kong, China) (RecSys '13). Association for Computing Machinery, New York, NY, USA, 1--8. https://doi.org/10.1145/2507157.2507183
[19]
Nava Tintarev and Judith Masthoff. 2007. Effective Explanations of Recommendations: User-Centered Design. In Proceedings of the 2007 ACM Conference on Recommender Systems (Minneapolis, MN, USA) (RecSys '07). Association for Computing Machinery, New York, NY, USA, 153--156. https://doi.org/10.1145/1297231.1297259
[20]
Nava Tintarev and Judith Masthoff. 2012. Evaluating the effectiveness of explanations for recommender systems - Methodological issues and empirical studies on the impact of personalization. User Model. User Adapt. Interact. 22, 4-5 (2012), 399--439. https://doi.org/10.1007/s11257-011-9117-5
[21]
Nan Wang, Hongning Wang, Yiling Jia, and Yue Yin. 2018. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval (Ann Arbor, MI, USA) (SIGIR '18). Association for Computing Machinery, New York, NY, USA, 165--174. https://doi.org/10.1145/3209978.3210010
[22]
Xiang Wang, Xiangnan He, Fuli Feng, Liqiang Nie, and Tat-Seng Chua. 2018. TEM: Tree-Enhanced Embedding Model for Explainable Recommendation. In Proceedings of the 2018 World Wide Web Conference (Lyon, France) (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1543--1552. https://doi.org/10.1145/3178876.3186066
[23]
Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, and Yongfeng Zhang. 2019. Reinforcement Knowledge Graph Reasoning for Explainable Recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR '19). Association for Computing Machinery, New York, NY, USA, 285--294. https://doi.org/10.1145/3331184.3331203
[24]
Runlong Yu, Yunzhou Zhang, Yuyang Ye, Le Wu, Chao Wang, Qi Liu, and Enhong Chen. 2018. Multiple Pairwise Ranking with Implicit Feedback. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (Torino, Italy) (CIKM '18). Association for Computing Machinery, New York, NY, USA, 1727--1730. https://doi.org/10.1145/3269206.3269283
[25]
Yongfeng Zhang and Xu Chen. 2020. Explainable Recommendation: A Survey and New Perspectives. Foundations and Trends® in Information Retrieval 14, 1 (2020), 1--101. https://doi.org/10.1561/1500000066
[26]
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 Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (Gold Coast, Queensland, Australia) (SIGIR '14). Association for Computing Machinery, New York, NY, USA, 83--92. https://doi.org/10.1145/2600428.2609579

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
iiWAS '20: Proceedings of the 22nd International Conference on Information Integration and Web-based Applications & Services
November 2020
492 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

  • Johannes Kepler University, Linz, Austria

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 January 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. recommendation quality
  2. recommender systems
  3. review utilization
  4. sentiment analysis

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

iiWAS '20

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 193
    Total Downloads
  • Downloads (Last 12 months)10
  • Downloads (Last 6 weeks)0
Reflects downloads up to 22 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media