本論文提出一個基於面向學習概念的模型，用來進行商品評分預測及對評分的解釋文本生成，稱為LARGE (Learning Aspects-representation for Rating and Generating Explanation)模型。在模型的編碼器中我們設計可學習多面向特徵空間的神經層，由商品評論文內容及使用者嵌入向量學習出對應的面向特徵向量，除了用以提供評分預測，並將商品的面向特徵向量融入每次的解碼狀態，引導生成的評分解釋文本能聚焦於商品所具有的面向。LARGE模型採多任務學習方式進行訓練，透過結合兩個不同目標任務的損失函數進行整體參數優化，且在評分預測的損失函數，加入權重調整策略，以降低推薦系統中評分資料分布不均對預測效能的影響。本論文採用亞馬遜資料集中三個不同商品類別的資料進行測試，實驗結果顯示，LARGE模型比相關研究所提出的代表性模型NRT，有效提升在評分預測及文本解釋生成的效能。此外，LARGE在解釋文本敘述中的類別型面向詞涵蓋率，比需輸入指定面向詞的NRT擴展模型有更高的涵蓋率。

In this paper, we proposed a recommendation model, called LARGE (Learning Aspects-representation for Rating and Generating Explanation), to perform aspect-based representation learning for both rating prediction and explanation generation. In the encoder, we designed a neural layer to learn multi-aspect representations from item reviews and user embedding for the task of rating prediction. In the task of explanation generation, we fused the learned aspect-based representation of items into each decoding state in order to guide explanation generation focus on the specific aspects of the item. The LARGE model is trained by a multi-task learning approach, where the parameters are tuned by optimizing a linear combination of the loss on the two target tasks. In addition, to reduce the bias of model training due to data unbalance, a weight adjustment strategy is applied to the loss function of rating prediction. The experiments are performed on 3 categories selected from the Amazon review dataset. The result of the experiments shows that the LARGE model significantly outperforms NRT on both tasks. Furthermore, to compare with an extension model of NRT using a given aspect word as model input, the rating explanation generated by LARGE has higher coverage on aspect words.

[1] 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.
[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 on World Wide Web. International World Wide Web Conferences Steering Committee, 1583–1592.
[3] Zhiyong Cheng, Ying Ding, Lei Zhu, and Mohan Kankanhalli. 2018. Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews. In WWW ’18. 639–648.
[4] Felipe Costa, Sixun Ouyang, Peter Dolog, and Aonghus Lawlor. 2017. “Automatic Generation of Natural Language Explanations,” In Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion. ACM, 57.
[5] Li Dong, Shaohan Huang, Furu Wei, Mirella Lapata, Ming Zhou, and Ke Xu. 2017. Learning to Generate Product Reviews from Attributes. In EACL, Vol. 1. 623–632.
[6] Piji Li, Zihao Wang, Zhaochun Ren, Lidong Bing, and Wai Lam. 2017. Neural rating regression with abstractive tips generation for recommendation. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 345–354.
[7] Chen, H., X. Chen, S. Shi, and Y. Zhang. 2019. “Generate natural language explanations for recommendation”. In Proceedings of the SIGIR 2019 Workshop on Explainable Recommendation and Search
[8] Lei Li, Yongfeng Zhang, and Li Chen. 2020. Generate neural template explanations for recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 755–764.
[9] Jin Yao Chin, Kaiqi Zhao, Shafiq Joty, and Gao Cong. 2018. ANR: Aspect-based Neural Recommender. In CIKM. 147–156.
[10] Wang, N., H. Wang, Y. Jia, and Y. Yin . 2018. “Explainable recommendation via multi-task learning in opinionated text data”. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. ACM.
[11] Ni, J., J. Li, and J. McAuley (2019). “Justifying recommendations using distantly-labeled reviews and fine-grained aspects”. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 188–197.
[12] Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (Aug. 2009), 30–37.
[13] Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted
collaborative filtering model. In SIGKDD. 426–434.
[14] 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 RecSys ’17. ACM, New York, NY, USA, 297–305.
[15] Lei Zheng, Vahid Noroozi, and Philip S. Yu. 2017. Joint Deep Modeling of Users and Items Using Reviews for Recommendation. In WSDM ’17. ACM, 425–434
[16] Rose Catherine and William Cohen. 2017. TransNets: Learning to Transform for Recommendation. In RecSys ’17. ACM, New York, NY, USA, 288–296.
[17] 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. 193–202.
[18] Yao Wu and Martin Ester. 2015. FLAME: A Probabilistic Model Combining Aspect Based Opinion Mining and Collaborative Filtering. In WSDM ’15. ACM, New York, NY, USA, 199–208.
[19] Yi Tay, Anh Tuan Luu, and Siu Cheung Hui. 2018. Multi-Pointer Co-Attention Networks for Recommendation. In KDD. 2309–2318.
[20] Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (WWW ’17). 173–182.
[21] Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, and How Jing. 2017. Recurrent Recommender Networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, WSDM 2017, Cambridge, United Kingdom, February 6-10, 2017. 495–503.
[22] Nan Wang, Hongning Wang, Yiling Jia, and Yue Yin. 2018. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data. In SIGIR. 165–174.
[23] Konstantin Bauman, Bing Liu, and Alexander Tuzhilin. 2017. Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews. In KDD ’17. ACM, 717–725.