Sequentially Diversified and Accurate Recommendations in Chronological Order for a Series of Users

When we sequentially recommend top-k items to users, how can we recommend them diversely while maintaining accuracy? Aggregate-level diversity is an important topic in recommender system since it is essential to maximize the potential profit of platforms by exposing a variety of items to users. However, previous studies do not consider the order of users receiving recommendations and assume that all users receive recommendations at once. In reality, users do not simultaneously receive recommendations so the preferences of the latter users are not given during recommending to the former users. In this work, we introduce the problem of sequentially diversified recommendation and propose SAPID, an accurate method to address the problem. SAPID removes the popularity bias from the model through a negative sampling mechanism based on temporal popularities. Then, SAPID collects candidate items to recommend based on the distribution of preference scores. Finally, SAPID decides which items to recommend immediately or later according to their estimated exposure opportunities. Extensive experiments show that SAPID shows the state-of-the-art performance in real-world datasets by achieving up to 61.0% increased diversity with 38.9% higher accuracy compared to the second-best competitor.