Unsupervised Extractive Summarization-Based Representations for Accurate and Explainable Collaborative Filtering (2021)

Abstract: We pioneer the first extractive summarization-based collaborative filtering model called ESCOFILT. Our proposed model specifically produces extractive summaries for each item and user. Unlike other types of explanations, summary-level explanations closely resemble real-life explanations. The strength of ESCOFILT lies in the fact that it unifies representation and explanation. In other words, extractive summaries both represent and explain the items and users. Our model uniquely integrates BERT, K-Means embedding clustering, and multilayer perceptron to learn sentence embeddings, representation-explanations, and user-item interactions, respectively. We argue that our approach enhances both rating prediction accuracy and user/item explainability. Our experiments illustrate that ESCOFILT’s prediction accuracy is better than the other state-of-the-art recommender models. Furthermore, we propose a comprehensive set of criteria that assesses the real-life explainability of explanations. Our explainability study demonstrates the superiority of and preference for summary-level explanations over other explanation types.

## introduction collaborative filtering (cf) approaches are the most dominant and outstanding models in recommender systems literature. cf mainly focuses on learning accurate representations of users and items, denoting user preferences and item characteristics, respectively @xcite . the earliest cf models learned such representations based on user-given numeric ratings, but employing them is an oversimplification of user preferences and item characteristics @xcite . in this regard, review texts have been utilized to alleviate this issue. reviews received by the 'journaling bible' item 1. i was not expecting this bible to be so beautiful when i pre-ordered it 5 months ago, but it arrived in the mail today and it is just gorgeous! i love the concept of bible journaling, but was always a bit intimidated by where/how to start. this removes that concern through some beautifully done artwork and lettering. i am ecstatic at the quality of this bible! 2. i brought this as i wanted a separate bible to do bible journaling. it is very beautiful and has many images that can be coloured. the pages are similar to bible paper and cream in colour. overall a wonderful bible to do journaling and meditate god's word. ## methodology escofilt, whose architecture is illustrated in figure 1 , has two parallel components that learn summarization-based user and item representations. from sections 3.2 to 3.3, we will only discuss the item modeling process as it is nearly identical to user modeling, with their inputs as the only difference. ## conclusion and future work in this study, unifying representations and explanations, in the form of extractive summaries, have further enhanced collaborative filtering accuracy and explainability. we have successfully developed a model that uniquely integrates bert, embedding clustering, and mlp. our experiments on various datasets verify escofilt's predictive capability, and the human judges' assessments validate its explainability in real life. in the future, we shall consider expanding our model's explainability capability by possibly incorporating other nlp principles such as abstractive summarization and natural language generation.