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Scott Golder, B. Huberman (2006)
Usage patterns of collaborative tagging systemsJournal of Information Science, 32
Ling Luo, Haoran Xie, Yanghui Rao, Fu Wang (2019)
Personalized recommendation by matrix co-factorization with tags and time informationExpert Syst. Appl., 119
R. Sinha (2005)
A cognitive analysis of tagging
Xu Chen, Zheng Qin, Yongfeng Zhang, Tao Xu (2016)
Learning to Rank Features for Recommendation over Multiple CategoriesProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval
Shouxian Wei, Xiaolin Zheng, Deren Chen, Chaochao Chen (2016)
A hybrid approach for movie recommendation via tags and ratingsElectron. Commer. Res. Appl., 18
Vladimir Mic, David Novak, P. Zezula (2018)
Binary Sketches for Secondary FilteringACM Transactions on Information Systems (TOIS), 37
Konstantin Bauman, B. Liu, A. Tuzhilin (2017)
Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User ReviewsProceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Sungyong Seo, Jing Huang, Hao Yang, Yan Liu (2017)
Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating PredictionProceedings of the Eleventh ACM Conference on Recommender Systems
A. Krohn-Grimberghe, Lucas Drumond, C. Freudenthaler, L. Schmidt-Thieme (2012)
Multi-relational matrix factorization using bayesian personalized ranking for social network data
Wen Zhou, Wenbo Han (2019)
Personalized recommendation via user preference matchingInf. Process. Manag., 56
Computational Linguistics, 1
Xiangnan He, Tao Chen, Min-Yen Kan, Xiao Chen (2015)
TriRank: Review-aware Explainable Recommendation by Modeling AspectsProceedings of the 24th ACM International on Conference on Information and Knowledge Management
Y. Koren, Robert Bell, C. Volinsky (2009)
Matrix Factorization Techniques for Recommender SystemsComputer, 42
Advances in Neural Information Processing Systems
Weike Pan, Qiang Yang, Wanling Cai, Yaofeng Chen, Qing Zhang, Xiaogang Peng, Zhong Ming (2019)
Transfer to Rank for Heterogeneous One-Class Collaborative FilteringACM Transactions on Information Systems (TOIS), 37
Shuai Yu, Min Yang, Qiang Qu, Ying Shen (2019)
Contextual-boosted deep neural collaborative filtering model for interpretable recommendationExpert Syst. Appl., 136
Julian McAuley, J. Leskovec (2013)
Hidden factors and hidden topics: understanding rating dimensions with review textProceedings of the 7th ACM conference on Recommender systems
G. Adomavicius, A. Tuzhilin (2005)
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensionsIEEE Transactions on Knowledge and Data Engineering, 17
Li Chen, Feng Wang (2017)
Explaining Recommendations Based on Feature Sentiments in Product ReviewsProceedings of the 22nd International Conference on Intelligent User Interfaces
Lei Zheng, V. Noroozi, Philip Yu (2017)
Joint Deep Modeling of Users and Items Using Reviews for RecommendationProceedings of the Tenth ACM International Conference on Web Search and Data Mining
Zan Huang, Wingyan Chung, Hsinchun Chen (2004)
A graph model for E-commerce recommender systemsJ. Assoc. Inf. Sci. Technol., 55
G. Adomavicius, N. Manouselis, YoungOk Kwon (2011)
Multi-Criteria Recommender Systems
Pigi Kouki, J. Schaffer, J. Pujara, J. O'Donovan, L. Getoor (2017)
User Preferences for Hybrid ExplanationsProceedings of the Eleventh ACM Conference on Recommender Systems
Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, Tat-Seng Chua (2018)
Attentive Aspect Modeling for Review-Aware RecommendationACM Transactions on Information Systems (TOIS), 37
Zhiyong Cheng, Xiaojun Chang, Lei Zhu, R. Catherine, M. Kankanhalli (2018)
MMALFMACM Transactions on Information Systems (TOIS), 37
Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation.Design/methodology/approachFirst, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-systemFindingsExperimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system.Originality/valueThis paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.
The Electronic Library – Emerald Publishing
Published: Jul 17, 2021
Keywords: Aspects; Interpretable recommendations; Negative preferences; Positive preferences; Top-N recommendations; Positive and negative preferences
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