A long-tail alleviation post-processing framework based on personalized diversity of session recommendation.

Session-based recommendation leverages the short-term interaction sequence to predict the next item a user is most likely to click on. Generally, in real applications, users often click on different types of items in the same session, which makes the items in the sequence present diversity, and degree of diversity vary with different sequences, that is, there is a phenomenon of personalized diversity. However, the majority of existing session recommendation models primarily focus on improving recommendation accuracy and overlooking the importance of personalized diversity, which leads to the issue of filtering bubbles. In order to alleviate the long-tail effect caused by the filtering bubble, the recommendation system should not only consider the accuracy of recommended items, but also the diversity of users' demands for recommended items. To address this issue, this paper proposes a model, named as LAP-SR, a L ong-tail A lleviation P ost-processing framework based on personalized diversity of S ession R ecommendation. After obtaining the initial recommendation list through the selected initial session model, LAP-SR model generates the long-tail item set and item graph according to session contents. Besides, from the item graph, the model calculates the embedding representation of items, from which the diversity of all sessions is calculated. By combining the obtained diversity and the long-tail item set, the initial recommendation list is soft processed. On this basis, the model yields the final recommendation list, which alleviates the overall long-tail effect. We verified that as a post-processing framework, the proposed model can be applied to various session recommendation models. Extensive experiments on the three datasets of Diginetica, Tmall and Yoochoose1_64 demonstrate that the LAP-SR model has a competitive advantage over the baseline models in mitigating the long-tail effect. • LAP-SR is based on personalized diversity to alleviate the long-tail effect. • The model consists of an initial recommendation model and a post-processing model. • LAP-SR is a universal post-processing model for session-based recommendations. • LAP-SR can significantly improve the exposure of long-tail items. [ABSTRACT FROM AUTHOR]