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1. Explanation as a Defense of Recommendation

   https://doi.org/10.1145/3437963.3441726  

   Yang, Aobo; Wang, Nan; Deng, Hongbo; Wang, Hongning (March 2021, Proceedings of the 14th ACM International Conference on Web Search and Data Mining)

   null (Ed.)

   Textual explanations have proved to help improve user satisfaction on machine-made recommendations. However, current mainstream solutions loosely connect the learning of explanation with the learning of recommendation: for example, they are often separately modeled as rating prediction and content generation tasks. In this work, we propose to strengthen their connection by enforcing the idea of sentiment alignment between a recommendation and its corresponding explanation. At training time, the two learning tasks are joined by a latent sentiment vector, which is encoded by the recommendation module and used to make word choices for explanation generation. At both training and inference time, the explanation module is required to generate explanation text that matches sentiment predicted by the recommendation module. Extensive experiments demonstrate our solution outperforms a rich set of baselines in both recommendation and explanation tasks, especially on the improved quality of its generated explanations. More importantly, our user studies confirm our generated explanations help users better recognize the differences between recommended items and understand why an item is recommended. 

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2. When Sentiment Analysis Meets Social Network: A Holistic User Behavior Modeling in Opinionated Data

   https://doi.org/10.1145/3219819.3220120  

   Gong, Lin; Wang, Hongning (August 2018, KDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining)

   User modeling is critical for understanding user intents, while it is also challenging as user intents are so diverse and not directly observable. Most existing works exploit specific types of behavior signals for user modeling, e.g., opinionated data or network structure; but the dependency among different types of user-generated data is neglected. We focus on self-consistence across multiple modalities of user-generated data to model user intents. A probabilistic generative model is developed to integrate two companion learning tasks of opinionated content modeling and social network structure modeling for users. Individual users are modeled as a mixture over the instances of paired learning tasks to realize their behavior heterogeneity, and the tasks are clustered by sharing a global prior distribution to capture the homogeneity among users. Extensive experimental evaluations on large collections of Amazon and Yelp reviews with social network structures confirm the effectiveness of the proposed solution. The learned user models are interpretable and predictive: they enable more accurate sentiment classification and item/friend recommendations than the corresponding baselines that only model a singular type of user behaviors. 

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3. The FacT: Taming Latent Factor Models for Explainability with Factorization Trees

   https://doi.org/10.1145/3331184.3331244  

   Tao, Yiyi; Jia, Yiling; Wang, Nan; Wang, Hongning (July 2019, Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval)

   Latent factor models have achieved great success in personalized recommendations, but they are also notoriously difficult to explain. In this work, we integrate regression trees to guide the learning of latent factor models for recommendation, and use the learnt tree structure to explain the resulting latent factors. Specifically, we build regression trees on users and items respectively with user-generated reviews, and associate a latent profile to each node on the trees to represent users and items. With the growth of regression tree, the latent factors are gradually refined under the regularization imposed by the tree structure. As a result, we are able to track the creation of latent profiles by looking into the path of each factor on regression trees, which thus serves as an explanation for the resulting recommendations. Extensive experiments on two large collections of Amazon and Yelp reviews demonstrate the advantage of our model over several competitive baseline algorithms. Besides, our extensive user study also confirms the practical value of explainable recommendations generated by our model. 

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4. Comparative Explanations of Recommendations

   https://doi.org/10.1145/3485447.3512031  

   Yang, Aobo; Wang, Nan; Cai, Renqin; Deng, Hongbo; Wang, Hongning (April 2022, Proceedings of the ACM Web Conference 2022)

   As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i.e., comparative explanations about the recommended items. Ideally, after reading the explanations, a user should reach the same ranking of items as the system’s. Unfortunately, little research attention has yet been paid on such comparative explanations. In this work, we develop an extract-and-refine architecture to explain the relative comparisons among a set of ranked items from a recommender system. For each recommended item, we first extract one sentence from its associated reviews that best suits the desired comparison against a set of reference items. Then this extracted sentence is further articulated with respect to the target user through a generative model to better explain why the item is recommended. We design a new explanation quality metric based on BLEU to guide the end-to-end training of the extraction and refinement components, which avoids generation of generic content. Extensive offline evaluations on two large recommendation benchmark datasets and serious user studies against an array of state-of-the-art explainable recommendation algorithms demonstrate the necessity of comparative explanations and the effectiveness of our solution. 

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5. On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance

   https://doi.org/10.1145/3569423  

   Li, Lei; Zhang, Yongfeng; Chen, Li (April 2023, ACM Transactions on Intelligent Systems and Technology)

   Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS). However, existing explainable RS usually consider explanation as a side output of the recommendation model, which has two problems: (1) It is difficult to evaluate the produced explanations, because they are usually model-dependent, and (2) as a result, how the explanations impact the recommendation performance is less investigated. In this article, explaining recommendations is formulated as a ranking task and learned from data, similarly to item ranking for recommendation. This makes it possible for standard evaluation of explanations via ranking metrics (e.g., Normalized Discounted Cumulative Gain). Furthermore, this article extends traditional item ranking to an item–explanation joint-ranking formalization to study if purposely selecting explanations could reach certain learning goals, e.g., improving recommendation performance. A great challenge, however, is that the sparsity issue in the user-item-explanation data would be inevitably severer than that in traditional user–item interaction data, since not every user–item pair can be associated with all explanations. To mitigate this issue, this article proposes to perform two sets of matrix factorization by considering the ternary relationship as two groups of binary relationships. Experiments on three large datasets verify the solution’s effectiveness on both explanation ranking and item recommendation. 

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