Social relations have been widely incorporated into recommender systems to
alleviate data sparsity problem. However, raw social relations don't always
benefit recommendation due to their inferior quality and insufficient quantity,
especially for inactive users, whose interacted items are limited. In this
paper, we propose a novel social recommendation method called LSIR
(\textbf{L}earning \textbf{S}ocial Graph for \textbf{I}nactive User
\textbf{R}ecommendation) that learns an optimal social graph structure for
social recommendation, especially for inactive users. LSIR recursively
aggregates user and item embeddings to collaboratively encode item and user
features. Then, graph structure learning (GSL) is employed to refine the raw
user-user social graph, by removing noisy edges and adding new edges based on
the enhanced embeddings. Meanwhile, mimic learning is implemented to guide
active users in mimicking inactive users during model training, which improves
the construction of new edges for inactive users. Extensive experiments on
real-world datasets demonstrate that LSIR achieves significant improvements of
up to 129.58\% on NDCG in inactive user recommendation. Our code is available
at~https://github.com/liun-online/LSIR.

本文提出了一种名为 LSIR（学习非活跃用户社交图推荐）的新颖社交推荐方法，通过学习最优社交图结构，尤其针对非活跃用户，优化了推荐效果，并且通过去除噪声边、增加新边和模仿学习等技术进一步提升了模型构建。实验结果表明，LSIR 在非活跃用户推荐中 NDCG 指标上取得了高达 129.58% 的显著改善。

学习非活跃用户推荐的社交关系图

Learning Social Graph for Inactive User Recommendation

There has been growing attention on fairness considerations recently,
especially in the context of intelligent decision making systems. Explainable
recommendation systems, in particular, may suffer from both explanation bias
and performance disparity. In this paper, we analyze different groups of users
according to their level of activity, and find that bias exists in
recommendation performance between different groups. We show that inactive
users may be more susceptible to receiving unsatisfactory recommendations, due
to insufficient training data for the inactive users, and that their
recommendations may be biased by the training records of more active users, due
to the nature of collaborative filtering, which leads to an unfair treatment by
the system. We propose a fairness constrained approach via heuristic re-ranking
to mitigate this unfairness problem in the context of explainable
recommendation over knowledge graphs. We experiment on several real-world
datasets with state-of-the-art knowledge graph-based explainable recommendation
algorithms. The promising results show that our algorithm is not only able to
provide high-quality explainable recommendations, but also reduces the
recommendation unfairness in several respects.

本文分析了不同活跃度用户的推荐性能，发现不同群体之间存在偏差，特别是不活跃用户会更容易受到不公平的对待。为了减少不公平现象，提出了通过启发式重新排序的公平性约束方法，实验结果表明该方法能提供高质量的可解释推荐，并降低了几个方面的推荐不公平性。