Hybrid recommendation by incorporating the sentiment of product reviews.

Hybrid recommender systems utilize advanced algorithms capable of learning heteroge-neous sources of data and generating personalized recommendations for users. The data can range from user preferences (e.g., ratings or reviews) to item content (e.g., description or category).Prior studies in the field of recommender systems have primarily relied on "ratings" as the user feedback, when building user profiles or evaluating the quality of the recommen-dation. While ratings are informative, they may still fail to represent a comprehensive pic-ture of actual user preferences. In contrast, there are other types of feedback data that differently or complementarily represent users and their preferences, including the reviews and the sentiments encapsulated within them. Such data can reveal important parts of a user's profile that are not necessarily correlated with user ratings, and hence, they potentially reflect a different side of the user's profile.In this paper, we propose a novel form of hybrid recommender system, capable of ana-lyzing the reviews and extracting their sentiments that are incorporated into the recom-mendation process. We used advanced algorithms to generate recommendations for users capable of incorporating additional data, such as the review sentiment. We con-ducted analyses and showed that sentiments of user reviews are not always highly corre-lated with the ratings (e.g., in music domain). This might mean that sentiment can be indicative of a different aspect of user preferences and can be used as an alternative signal of user feedback. Hence, we have used both ratings and sentiments of reviews when eval-uating our proposed hybrid recommender system. We selected two common datasets for the evaluation, Amazon Digital Music and Amazon Video Games, and showed the superior performance of the proposed hybrid recommender system compared to different baselines. The comparison were made in two evaluation scenarios, namely, when the ratings were considered the user feedback and when sentiments of the review were considered the user feedback. (c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).