Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction (2021)

Abstract: Compliments and concerns in reviews are valuable for understanding users’ shopping interests and their opinions with respect to specific aspects of certain items. Existing review-based recommenders favor large and complex language encoders that can only learn latent and uninterpretable text representations. They lack explicit user-attention and item-property modeling, which however could provide valuable information beyond the ability to recommend items. Therefore, we propose a tightly coupled two-stage approach, including an Aspect-Sentiment Pair Extractor (ASPE) and an Attention-Property-aware Rating Estimator (APRE). Unsupervised ASPE mines Aspect-Sentiment pairs (AS-pairs) and APRE predicts ratings using AS-pairs as concrete aspect-level evidences. Extensive experiments on seven real-world Amazon Review Datasets demonstrate that ASPE can effectively extract AS-pairs which enable APRE to deliver superior accuracy over the leading baselines.

## introduction reviews and ratings are valuable assets for the recommender systems of e-commerce websites since they immediately describe the users' subjective feelings about the purchases. learning user preferences from such feedback is straightforward and efficacious. previous research on review-based recommendation has been fruitful @xcite @xcite . cutting-edge natural language processing (nlp) techniques are applied to extract the latent user sentiments, item properties, and the complicated interactions between the two components. however, existing approaches have disadvantages bearing room for improvement. firstly, they dismiss the phenomenon that users may hold different attentions toward various properties of the merchandise. an item property is the combination of an aspect of the item and the characteristic asso-ciated with it. users may show strong attentions to certain properties but indifference to others. the attended advantageous or disadvantageous properties can dominate the attitude of users and consequently, decide their generosity in rating. secondly, understanding user motivations in granular item properties provides valuable information beyond the ability to recommend items. it requires aspect-based nlp techniques to extract explicit and definitive aspects. however, existing aspect-based models mainly use latent or implicit aspects @xcite whose real semantics are unjustifiable. similar to latent dirichlet allocation @xcite , the semantics of the derived aspects (topics) are mutually overlapped @xcite . these models undermine the resultant aspect distinctiveness and lead to uninterpretable and sometimes counterintuitive results. the root of the problem is the lack of large review corpora with aspect and sentiment annotations. the existing ones are either too small or too domain-specific @xcite to be applied to general use cases. progress on sentiment term extraction @xcite @xcite takes advantage of neural networks and linguistic knowledge and partially makes it possible to use unsupervised term annotation to tackle the lack-of-huge-corpus issue. in this paper, we seek to understand how reviews and ratings are affected by users' perception ## related work our work is related to four lines of literature which are located in the overlap of absa and recommender systems. ## conclusion in this work, we propose a tightly coupled twostage review-based rating predictor, consisting of an aspect-sentiment pair extractor (aspe) and an attention-property-aware rating estimator (apre). aspe extracts aspect-sentiment pairs (aspairs) from reviews and apre learns explicit user attentions and item properties as well as implicit sentence semantics to predict the rating. extensive quantitative and qualitative experimental results demonstrate that aspe accurately and comprehensively extracts as-pairs without using domainspecific training data and apre outperforms the state-of-the-art recommender frameworks and explains the prediction results taking advantage of the extracted as-pairs. several challenges are left open such as fully or weakly supervised open domain as-pair extraction and end-to-end design for as-pair extraction and rating prediction. we leave these problems for future work. from reviews given by user u * . all aspects attended ().