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 ong-tail lleviation ost-processing framework based on personalized diversity of ession 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.

中文翻译：

---

基于个性化多样性会话推荐的长尾缓解后处理框架

基于会话的推荐利用短期交互序列来预测用户最有可能点击的下一个项目。通常，在实际应用中，用户经常会在同一个会话中点击不同类型的项目，这使得序列中的项目呈现多样性，并且不同序列的多样性程度不同，即存在个性化多样性的现象。然而，现有的会话推荐模型大多数主要关注于提高推荐准确性，而忽视了个性化多样性的重要性，从而导致了过滤气泡的问题。为了缓解过滤气泡带来的长尾效应，推荐系统不仅要考虑推荐项的准确性，还要考虑用户对推荐项需求的多样性。针对这一问题，本文提出了一种模型，名为LAP-SR，是一种基于会话推荐个性化多样性的长尾缓解OST处理框架。通过选定的初始会话模型获得初始推荐列表后，LAP-SR模型根据会话内容生成长尾项目集和项目图。此外，模型从项目图中计算项目的嵌入表示，从中计算所有会话的多样性。通过结合获得的多样性和长尾项目集，对初始推荐列表进行软处理。在此基础上，模型产生最终的推荐列表，从而缓解了整体的长尾效应。我们验证了作为后处理框架，所提出的模型可以应用于各种会话推荐模型。对 Diginetica、天猫和 Yoochoose1_64 三个数据集的大量实验表明，LAP-SR 模型在缓解长尾效应方面比基线模型具有竞争优势。