融合社交影响扩散的长尾物品推荐模型

Long-tail Recommendation Method Incorporating Social Influence Diffusion Model

摘要

在推荐系统中，由于用户评分和交互频次存在长尾分布特性，导致长尾物品特征提取困难，现有的方法或者过多关注尾部物品，忽略其与头部物品的联系，或者忽略社交网络对用户偏好的影响，从而影响了推荐效果。因此，本文提出一种融合社会影响扩散的长尾推荐模型LoSidi(Long-tail Recommendation method Incorporating Social Influence Diffusion)。首先对于每个用户，通过对目标用户各层社交邻居的采样聚合，并结合其已交互的热门物品生成用户兴趣嵌入。其次通过计算长尾物品与用户已交互头部物品间的相似性挖掘长尾物品的潜在特征。最终，LoSidi模型构建用户与长尾物品间的联系，对长尾物品进行评分预测和推荐。在常用数据集上的实验结果表明，本文提出的模型可显著提高用户推荐列表的新颖性和多样性。

Abstract

In recommendation systems, the long-tail distribution of user ratings and interaction frequencies poses challenges for extracting the features of long-tail items. Existing methods either overly focus on tail items while neglecting their connections with head items or disregard the influence of social networks on user preferences, thereby impacting recommendation performance. To address these issues, this paper proposes a novel long-tail recommendation model called LoSidi (Long-tail Recommendation Method Incorporating Social Influence Diffusion). Firstly, for each user, the model aggregates samples of social neighbors at various layers and integrates these with the popular items the user has interacted with to generate user interest embeddings. Secondly, the potential features of long-tail items are mined by calculating the similarity between long-tail items and the head items the user has interacted with. Finally, the LoSidi model establishes links between users and long-tail items, predicting scores and generating recommendations for these items. Experimental results on widely-used datasets demonstrate that the proposed model significantly improves the novelty and diversity of users' recommendation lists.

Graphical abstract

关键词

推荐系统 / 社会推荐 / 长尾问题 / 图神经网络

Key words

recommender system / social recommendation / long tail problem / graph neural network

基金资助

国家自然科学基金(62076154)

山西省科技重大专项计划“揭榜挂帅”项目(202101020101019)