Serendipity-Oriented Recommender System with Dynamic Unexpectedness Prediction
Yu Tokutake, Kazushi Okamoto · 2023
With unexpectedness as a component of serendipity, many previous studies on serendipity-oriented recommender systems have quantified the degree of unexpectedness of items for users as a score. A user's browsing and rating history is necessary for the score calculation. These studies either treated all histories as equal or used only the most recent histories. However, these calculation methods cannot cope with unexpectedness in the case of constant change owing to fluctuations in user preferences and public popularity. In this study, we propose a serendipity-oriented recommender system that sequentially calculates the unexpectedness score and predicts the score in the recommendation time. The proposed system consists of a reranking algorithm that reranks a recommendation list generated by accuracy-oriented recommender systems. It also introduces a parameter to adjust users' ability to accept unexpectedness and aims for serendipitous recommendations that are in line with user preferences. The experiment on two benchmark datasets showed two results: the first is the proposed system improved the serendipitous metric by 0.061 points compared with accuracy-oriented systems before reranking and by 0.045 points compared with them without using the acceptance parameter. The second is the error between the predicted and ground truth of the unexpectedness score was smaller for a large dataset.