Abstract
Recommender system (RS) analyzes the purchase behavior of existing users and predicts relevant item(s) to a new user. In RS, collaborative filtering (CF) is a popular approach to suggest item(s) that are most similar to the new user’s interests using item/user similarity. Generally, in CF-RS top rated items are recommended. However, in this approach the low and average rated items are neglected which may be liked by the user. As a result, the CF-RS approach is unable to improve the user satisfaction. In this paper, we propose an approach called Serendipitous Recommender System (SRS) to recommend the items which are liked by the users; however, the items need not be top-rated. Generally, the user ratings may not express the true opinions of the users. We observe that the users express their opinions in user-reviews through the emotional words and they may contain surprise emotion. These emotional words are considered to update the user-rating such that the items will be available for recommendations. We use the updated user-ratings for the final recommendations using the user-based and item-based CF approaches. We call such recommended items as serendipity items. This allows us to provide the recommendations that are nearer to the users’ intent. We conducted experiments on real-world datasets, Amazon and Yelp. We evaluated the proposed approach using precision, recall, F1-Score, and unexpectedness metrics. The results show that the proposed approach performed better in recommending surprise items.
Recommended Citation
Aramanda, Amarajyothi and Swamy, M. Kumara, "Improve the Serendipity in Recommender Systems" (2023). Proceedings of the 2023 Pre-ICIS SIGDSA Symposium. 13.
https://aisel.aisnet.org/sigdsa2023/13