Abstract
In the area of recommendation systems, one of the fundamental tasks is rating prediction. Most existing neural network methods independently extract user’s and item’s review features utilizing a parallel convolutional neural network(CNN) and use them as the representation of users and items to predict rating scores. There are two main drawbacks of these methods: (1) They typically only leverage user or item reviews but ignore the latent information provided by user-item interactions. (2) The historical rating scores are not integrated into the representation of users and items, they are simply used as labels to train models. Thus the rating information is not adequately utilized, leading to the prediction performance of these methods is not superior. To remedy these drawbacks mentioned above, in this paper, we build a unified graph convolutional network(GCN) to capture the interaction information between user and item, also obtain additional information provided by reviews and rating scores. As both reviews and ratings carry interactive messages among users and items, they would magnify the learning performance of user-item features. Specifically, we first construct a multi-attributed bipartite graph(MA-bipartite graph) to represent users, items, and their interactions through reviews and ratings. Then we divide the MA-bipartite graph into two sub-graphs according to the attributes of the edge types which represent the user-item interaction in review domain and item domain respectively. Next, an attributed GCN model is explicitly designed to learn latent features of users and items by incorporating review embeddings and rating score weights. Finally, the attention mechanism is carried to fuse user and item features dynamically to conduct the rating prediction. We conduct our experiments on two real-world datasets. The results demonstrate that the proposed model achieved the state-of-the-art performance, which increases the prediction accuracy by more than 3%, compared with baseline methods.
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Notes
https://jmcauley.ucsd.edu/data/amazon/ 5-core.
https://cseweb.ucsd.edu/ jmcauley/datasets.html.
https://www.amazon.com/.
https://shopping.com/.
References
Ahmed BH, Ghabayen AS (2020) Review rating prediction framework using deep learning. J Am Intel Hum Comput pp 1–10
Bao Y, Fang H, Zhang J (2014) Topicmf: simultaneously exploiting ratings and reviews for recommendation. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence 14:2–8
Berg R, Kipf TN, Welling M (2017) Graph convolutional matrix completion. Preprint at https://arxiv.org/abs/1706.02263
Bruna J, Zaremba W, Szlam A, et al (2013) Spectral networks and locally connected networks on graphs. Preprint at https://arxiv.org/abs/1312.6203
Cai C, He R, McAuley J (2017) Spmc: socially-aware personalized markov chains for sparse sequential recommendation. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 1476–1482
Catherine R, Cohen W (2017) Transnets: learning to transform for recommendation. In: proceedings of the eleventh ACM conference on recommender systems, 288–296
Chaudhari S, Polatkan G, Ramanath R, et al (2019) An attentive survey of attention models. Preprint at https://arxiv.org/abs/1904.02874
Chen C, Zhang M, Liu Y, et al (2018) Neural attentional rating regression with review-level explanations. In: proceedings of the 2018 world wide web Conference, 1583–1592
Cheng Z, Chang X, Zhu L et al (2019) Mmalfm: Explainable recommendation by leveraging reviews and images. ACM Trans Inform Syst (TOIS) 37(2):1–28
Collobert R, Weston J, Bottou L et al (2011) Natural language processing (almost) from scratch. J Machine Learn Res 12(2011):2493–2537
Cui Y, Chen Z, Wei S, et al (2016) Attention-over-attention neural networks for reading comprehension. Preprint at https://arxiv.org/abs/1607.04423
Gojali S, Khodra ML (2016) Aspect based sentiment analysis for review rating prediction. In:2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA), 1-6
Guan X, Cheng Z, He X et al (2019) Attentive aspect modeling for review-aware recommendation. ACM Trans Inform Syst (TOIS) 37(3):1–27
Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: proceedings of the 31st International Conference on Neural Information Processing Systems, 1024–1034
He X, Deng K, Wang X, et al (2020) Lightgcn: simplifying and powering graph convolution network for recommendation. Preprint at https://arxiv.org/abs/2002.02126
Jin Z, Li Q, Zeng DD, et al (2016) Jointly modeling review content and aspect ratings for review rating prediction. In: proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, 893–896
Kim D, Park C, Oh J, et al (2016) Convolutional matrix factorization for document context-aware recommendation. In: proceedings of the 10th ACM conference on recommender systems, 233–240
Kim Y (2014) Convolutional neural networks for sentence classification. Preprint at https://arxiv.org/abs/1408.5882
Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In:Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 426–434
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37
Lin Z, Feng M, Santos CNd, et al (2017) A structured self-attentive sentence embedding. Preprint at https://arxiv.org/abs/1703.03130
Ling G, Lyu MR, King I (2014) Ratings meet reviews, a combined approach to recommend. In: proceedings of the 8th ACM conference on recommender systems, 105–112
Liu D, Li J, Du B, et al (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
McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In:Proceedings of the 7th ACM conference on Recommender systems, 165–172
McAuley J, Targett C, Shi Q, et al (2015) Image-based recommendations on styles and substitutes. In:Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, 43–52
Mikolov T, Chen K, Corrado G, et al (2013) Efficient estimation of word representations in vector space. Preprint at https://arxiv.org/abs/1301.3781
Mnih A, Salakhutdinov RR (2007) Probabilistic matrix factorization. Adv Neural Inform Process syst 20:1257–1264
Pero Š, Horváth T (2013) Opinion-driven matrix factorization for rating prediction. In: International conference on user modeling, adaptation, and personalization, 1–13
Rendle S (2010) Factorization machines. In:2010 IEEE International conference on data mining, 995–1000
Sachdeva N, McAuley J (2020) How useful are reviews for recommendation a critical review and potential improvements. In: proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, 1845–1848
Sarwar B, Karypis G, Konstan J, et al (2001) Item-based collaborative filtering recommendation algorithms. In:Proceedings of the 10th international conference on World Wide Web, 285–295
Seo S, Huang J, Yang H, et al (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, 297–305
Song W, Xiao Z, Wang Y, et al (2019) Session-based social recommendation via dynamic graph attention networks. In: proceedings of the Twelfth ACM International conference on web search and data mining, 555–563
Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Machine Learn Res 15(1):1929–1958
Tan Y, Zhang M, Liu Y, et al (2016) Rating-boosted latent topics: Understanding users and items with ratings and reviews. In:Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), 2640–2646
Tay Y, Luu AT, Hui SC (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
Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In:Proceedings of the 31st International Conference on Neural Information Processing Systems, 6000-6010
Veličković P, Cucurull G, Casanova A, et al (2017) Graph attention networks. Preprint at https://arxiv.org/abs/1710.10903
Vinod N, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In:Proceedings of the 27th International Conference on Machine Learning (ICML-10), 807-814
Wang C, Blei DM (2011) Collaborative topic modeling for recommending scientific articles. In:Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 448–456
Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. In:Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 1235–1244
Wang X, Yu L, Ren K, et al (2017) Dynamic attention deep model for article recommendation by learning human editors’ demonstration. In:Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining, 2051–2059
Wang X, He X, Wang M, et al (2019a) Neural graph collaborative filtering. In:Proceedings of the 42nd international ACM SIGIR conference on Research and development in Information Retrieval, 165–174
Wang X, Ji H, Shi C, et al (2019b) Heterogeneous graph attention network. In:The 2019 World Wide Web Conference, 2022–2032
Wu L, Quan C, Li C et al (2019) A context-aware user-item representation learning for item recommendation. ACM Trans Inform Syst (TOIS) 37(2):1–29
Wu S, Tang Y, Zhu Y et al (2019) Session-based recommendation with graph neural networks. Proc Conf Artif Intel 33:346–353
Wu Y, Lian D, Jin S, et al (2019c) Graph convolutional networks on user mobility heterogeneous graphs for social relationship inference. In:Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence Main track, 3898-3904
Xing S, Wang Q, Zhao X et al (2019) A hierarchical attention model for rating prediction by leveraging user and product reviews. Neurocomputing 332:417–427
Xu K, Ba J, Kiros R, et al (2015) Show, attend and tell: Neural image caption generation with visual attention. In:International conference on machine learning, 2048–2057
Ying R, He R, Chen K, et al (2018) Graph convolutional neural networks for web-scale recommender systems. In:Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 974–983
Zhang S, Tay Y, Yao L, et al (2018) Next item recommendation with self-attention. Preprint at https://arxivorg/abs/180806414
Zhang W, Wang J (2016) Integrating topic and latent factors for scalable personalized review-based rating prediction. IEEE Trans Knowl Data Eng 28(11):3013–3027
Zheng L, Noroozi V, Yu PS (2017) Joint deep modeling of users and items using reviews for recommendation. In:Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 425–434
Zhou C, Bai J, Song J, et al (2018) Atrank: An attention-based user behavior modeling framework for recommendation. In:Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 4564–4571
Zhu Q, Zhou X, Song Z et al (2019) Dan: deep attention neural network for news recommendation. Proc AAAI Conf Artif Intel 33:5973–5980
Acknowledgements
This work is sponsored by the National Natural Science Foundation of China (61976103, 61872161), the Scientific and Technological Development Program of Jilin Province (20190302029GX, 20180101330JC, 20180101328JC) and Tianjin Synthetic Biotechnology Innovation Capability Improvement Program (no. TSBICIP-CXRC-018).
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Zhang, Y., Zuo, W., Shi, Z. et al. Integrating reviews and ratings into graph neural networks for rating prediction. J Ambient Intell Human Comput 14, 8703–8723 (2023). https://doi.org/10.1007/s12652-021-03626-7
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DOI: https://doi.org/10.1007/s12652-021-03626-7