Abstract
With the dramatic expansion of international markets, consumers write reviews in different languages, which poses a new challenge for Recommender Systems (RSs) dealing with this increasing amount of multilingual information. Recent studies that leverage deep-learning techniques for review-aware RSs have demonstrated their effectiveness in modelling fine-grained user-item interactions through the aspects of reviews. However, most of these models can neither take full advantage of the contextual information from multilingual reviews nor discriminate the inherent ambiguity of words originated from the user’s different tendency in writing. To this end, we propose a novel Multilingual Review-aware Deep Recommendation Model (MrRec) for rating prediction tasks. MrRec mainly consists of two parts: (1) Multilingual aspect-based sentiment analysis module (MABSA), which aims to jointly extract aligned aspects and their associated sentiments in different languages simultaneously with only requiring overall review ratings. (2) Multilingual recommendation module that learns aspect importances of both the user and item with considering different contributions of multiple languages and estimates aspect utility via a dual interactive attention mechanism integrated with aspect-specific sentiments from MABSA. Finally, overall ratings can be inferred by a prediction layer adopting the aspect utility value and aspect importance as inputs. Extensive experimental results on nine real-world datasets demonstrate the superior performance and interpretability of our model.
- Zhiyong Cheng, Ying Ding, Lei Zhu, and Mohan Kankanhalli. 2018. Aspect-aware latent factor model: Rating prediction with ratings and reviews. In Proceedings of the World Wide Web Conference. International World Wide Web Conferences Steering Committee, 639--648.Google ScholarDigital Library
- Konstantin Bauman, Bing Liu, and Alexander Tuzhilin. 2017. Aspect-based recommendations: Recommending items with the most valuable aspects based on user reviews. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 717--725.Google ScholarDigital Library
- Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, and Tat-Seng Chua. 2019. Attentive aspect modeling for review-aware recommendation. ACM Trans. Info. Syst. 37, 3 (2019), 28.Google ScholarDigital Library
- Jin Yao Chin, Kaiqi Zhao, Shafiq Joty, and Gao Cong. 2018. ANR: Aspect-based neural recommender. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. ACM, 147--156.Google ScholarDigital Library
- Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, and Libing Wu. 2019. A capsule network for recommendation and explaining what you like and dislike. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 275--284.Google ScholarDigital Library
- Fedelucio Narducci, Pierpaolo Basile, Cataldo Musto, Pasquale Lops, Annalina Caputo, Marco de Gemmis, Leo Iaquinta, and Giovanni Semeraro. 2016. Concept-based item representations for a cross-lingual content-based recommendation process. Info. Sci. 374 (2016), 15--31.Google Scholar
- Pasquale Lops, Cataldo Musto, Fedelucio Narducci, Marco De Gemmis, Pierpaolo Basile, and Giovanni Semeraro. 2010. Mars: A multilanguage recommender system. In Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems. ACM, 24--31.Google ScholarDigital Library
- Bernardo Magnini and Carlo Strapparava. 2001. Improving user modelling with content-based techniques. In Proceedings of the International Conference on User Modeling. Springer, 74--83.Google ScholarCross Ref
- Sebastian Schmidt, Philipp Scholl, Christoph Rensing, and Ralf Steinmetz. 2011. Cross-lingual recommendations in a resource-based learning scenario. In Proceedings of the European Conference on Technology Enhanced Learning. Springer, 356--369.Google ScholarCross Ref
- Libing Wu, Cong Quan, Chenliang Li, and Donghong Ji. 2018. Parl: Let strangers speak out what you like. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 677--686.Google ScholarDigital Library
- Jim Keeler and David E. Rumelhart. 1992. A self-organizing integrated segmentation and recognition neural net. In Advances in Neural Information Processing Systems. MIT Press, 496--503.Google Scholar
- Nikolaos Pappas and Andrei Popescu-Belis. 2017. Explicit document modeling through weighted multiple-instance learning. J. Artific. Intell. Res. 58 (2017), 591--626.Google ScholarDigital Library
- Stefanos Angelidis and Mirella Lapata. 2018. Multiple instance learning networks for fine-grained sentiment analysis. Trans. Assoc. Comput. Linguist. 6 (2018), 17--31.Google ScholarCross Ref
- 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 11th ACM Conference on Recommender Systems. ACM, 297--305.Google ScholarDigital Library
- Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, and Guihai Chen. 2019. Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In Proceedings of the World Wide Web Conference. 2091--2102.Google ScholarDigital Library
- Donghua Liu, Jing Li, Bo Du, Jun Chang, and Rong Gao. 2019. Daml: Dual attention mutual learning between ratings and reviews for item recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 344--352.Google ScholarDigital Library
- Yang Bao, Hui Fang, and Jie Zhang. 2014. Topicmf: Simultaneously exploiting ratings and reviews for recommendation. In Proceedings of the 28th AAAI Conference on Artificial Intelligence.Google Scholar
- Chong Chen, Min Zhang, Yiqun Liu, and Shaoping Ma. 2018. Neural attentional rating regression with review-level explanations. In Proceedings of the World Wide Web Conference. International World Wide Web Conferences Steering Committee, 1583--1592.Google ScholarDigital Library
- Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. 2016. Convolutional matrix factorization for document context-aware recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 233--240.Google ScholarDigital 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
- Dongmin Hyun, Chanyoung Park, Min-Chul Yang, Ilhyeon Song, Jung-Tae Lee, and Hwanjo Yu. 2018. Review sentiment-guided scalable deep recommender system. In Proceedings of the 41st International ACM SIGIR Conference on Research 8 Development in Information Retrieval. ACM, 965--968.Google ScholarDigital Library
- Zhiyong Cheng, Xiaojun Chang, Lei Zhu, Rose C Kanjirathinkal, and Mohan Kankanhalli. 2019. MMALFM: Explainable recommendation by leveraging reviews and images. ACM Trans. Info. Syst. 37, 2 (2019), 1--28.Google ScholarDigital Library
- Lei Zheng, Vahid Noroozi, and Philip S. Yu. 2017. Joint deep modeling of users and items using reviews for recommendation. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining. ACM, 425--434.Google Scholar
- Rose Catherine and William Cohen. 2017. Transnets: Learning to transform for recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems. ACM, 288--296.Google ScholarDigital 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 ScholarDigital Library
- Yao Wu and Martin Ester. 2015. Flame: A probabilistic model combining aspect-based opinion mining and collaborative filtering. In Proceedings of the 8th ACM International Conference on Web Search and Data Mining. ACM, 199--208.Google ScholarDigital Library
- Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2017. An unsupervised neural attention model for aspect extraction. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 388--397.Google ScholarCross Ref
- 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 and Development in Information Retrieval. ACM, 83--92.Google ScholarDigital Library
- 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. ACM, 305--314.Google ScholarDigital Library
- 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. 1661--1670.Google ScholarDigital Library
- Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, and Mohan Kankanhalli. 2018. A3NCF: An adaptive aspect attention model for rating prediction. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18). International Joint Conferences on Artificial Intelligence Organization, 3748--3754. DOI:http://dx.doi.org/10.24963/ijcai.2018/521Google Scholar
- Carmen Martinez-Cruz, Carlos Porcel, Juan Bernabé-Moreno, and Enrique Herrera-Viedma. 2015. A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Info. Sci. 311 (2015), 102--118.Google Scholar
- Atsuhiro Takasu. 2010. Cross-lingual keyword recommendation using latent topics. In Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems. ACM, 52--56.Google ScholarDigital Library
- Patrik Lambert. 2015. Aspect-level cross-lingual sentiment classification with constrained SMT. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 781--787.Google ScholarCross Ref
- Roman Klinger and Philipp Cimiano. 2015. Instance selection improves cross-lingual model training for fine-grained sentiment analysis. In Proceedings of the 19th Conference on Computational Natural Language Learning. 153--163.Google ScholarCross Ref
- Mariana S. C. Almeida, Cláudia Pinto, Helena Figueira, Pedro Mendes, and André F. T. Martins. 2015. Aligning opinions: Cross-lingual opinion mining with dependencies. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 408--418.Google Scholar
- Jeremy Barnes, Patrik Lambert, and Toni Badia. 2016. Exploring distributional representations and machine translation for aspect-based cross-lingual sentiment classification. In Proceedings of the 26th International Conference on Computational Linguistics (COLING’16). 1613--1623.Google Scholar
- Jordan Boyd-Graber and David M. Blei. 2009. Multilingual topic models for unaligned text. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. AUAI Press, 75--82.Google ScholarDigital Library
- Duo Zhang, Qiaozhu Mei, and ChengXiang Zhai. 2010. Cross-lingual latent topic extraction. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 1128--1137.Google ScholarDigital Library
- Jordan Boyd-Graber and Philip Resnik. 2010. Holistic sentiment analysis across languages: Multilingual supervised latent Dirichlet allocation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 45--55.Google Scholar
- Zheng Lin, Xiaolong Jin, Xueke Xu, Weiping Wang, Xueqi Cheng, and Yuanzhuo Wang. 2014. A cross-lingual joint aspect/sentiment model for sentiment analysis. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. ACM, 1089--1098.Google ScholarDigital Library
- Zheng Lin, Xiaolong Jin, Xueke Xu, Yuanzhuo Wang, Xueqi Cheng, Weiping Wang, and Dan Meng. 2015. An unsupervised cross-lingual topic model framework for sentiment classification. IEEE/ACM Trans. Audio, Speech, Lang. Process. 24, 3 (2015), 432--444.Google Scholar
- Armand Joulin, Piotr Bojanowski, Tomas Mikolov, Hervé Jégou, and Edouard Grave. 2018. Loss in translation: Learning bilingual word mapping with a retrieval criterion. Retrieved from https://arxiv.org/abs/1804.07745.Google Scholar
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. Retrieved from https://arxiv.org/abs/1409.0473.Google Scholar
- Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, and Xiaodong He. 2018. Stacked cross attention for image-text matching. In Proceedings of the European Conference on Computer Vision (ECCV’18). 201--216.Google ScholarCross Ref
- Orhan Firat, Kyunghyun Cho, and Yoshua Bengio. 2016. Multi-way, multilingual neural machine translation with a shared attention mechanism. Retrieved from https://arxiv.org/abs/1601.01073.Google Scholar
- Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, and Chengqi Zhang. 2018. Disan: Directional self-attention network for rnn/cnn-free language understanding. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence.Google Scholar
- Jiasen Lu, Jianwei Yang, Dhruv Batra, and Devi Parikh. 2016. Hierarchical question-image co-attention for visual question answering. In Advances in Neural Information Processing Systems. MIT Press, 289--297.Google Scholar
- Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2018. Multi-pointer co-attention networks for recommendation. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2309--2318.Google ScholarDigital Library
- Libing Wu, Cong Quan, Chenliang Li, Qian Wang, Bolong Zheng, and Xiangyang Luo. 2019. A context-aware user-item representation learning for item recommendation. ACM Trans. Info. Syst. 37, 2 (2019), 1--29.Google ScholarDigital Library
- Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. 811--820.Google ScholarDigital Library
- Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015. Learning hierarchical representation model for nextbasket recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 403--412.Google ScholarDigital Library
- Md Hijbul Alam, Woo-Jong Ryu, and SangKeun Lee. 2016. Joint multi-grain topic sentiment: Modeling semantic aspects for online reviews. Info. Sci. 339 (2016), 206--223.Google Scholar
- Maria Pontiki, Dimitrios Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq et al. 2016. Semeval-2016 task 5: Aspect-based sentiment analysis. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval’16).Google ScholarCross Ref
- Cai-Nicolas Ziegler, Sean M. McNee, Joseph A. Konstan, and Georg Lausen. 2005. Improving recommendation lists through topic diversification. In Proceedings of the 14th International Conference on World Wide Web. 22--32.Google ScholarDigital Library
- Katja Niemann and Martin Wolpers. 2013. A new collaborative filtering approach for increasing the aggregate diversity of recommender systems. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 955--963.Google ScholarDigital Library
- Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30--37.Google ScholarDigital Library
- Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. 2018. Nais: Neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30, 12 (2018), 2354--2366.Google ScholarDigital Library
- Yequan Wang, Minlie Huang, Xiaoyan Zhu, and Li Zhao. 2016. Attention-based LSTM for aspect-level sentiment classification. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. 606--615.Google ScholarCross Ref
- Mengting Hu, Shiwan Zhao, Li Zhang, Keke Cai, Zhong Su, Renhong Cheng, and Xiaowei Shen. 2018. CAN: Constrained attention networks for multi-aspect sentiment analysis. Retrieved from https://arxiv.org/abs/1812.10735.Google Scholar
- Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP’14). 1532--1543.Google Scholar
Index Terms
- Multilingual Review-aware Deep Recommender System via Aspect-based Sentiment Analysis
Recommendations
ANR: Aspect-based Neural Recommender
CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge ManagementTextual reviews, which are readily available on many e-commerce and review websites such as Amazon and Yelp, serve as an invaluable source of information for recommender systems. However, not all parts of the reviews are equally important, and the same ...
A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users' Reviews
RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender SystemsIn this paper we propose a multi-criteria recommender system based on collaborative filtering (CF) techniques, which exploits the information conveyed by users' reviews to provide a multi-faceted representation of users' interests.
To this end, we ...
Short Review of Sentiment-Based Recommender Systems
DTUC '18: Proceedings of the 1st International Conference on Digital Tools & Uses CongressSentiment analysis is a trendy domain of Machine Learning which has developed considerably in the last several years. It helps to determine the sentiment of a user in an utterance, a document or a review. Some systems can extract the target of the ...
Comments