Collaborative filtering (CF) is the basic method for recommendation with implicit feedback. Recently, various state-of-the-art CF integrates graph neural networks. However, they often suffer from popularity bias, causing recommendations to deviate from users' genuine preferences. Additionally, several contrastive learning methods based on the in-batch sample strategy have been proposed to train the CF model effectively, but they are prone to suffering from sample bias. To address this problem, debiased contrastive loss has been employed in the recommendation, but instead of personalized debiasing, it treats each user equally. In this paper, we propose a popularity-aware debiased contrastive loss for CF, which can adaptively correct the positive and negative scores based on the popularity of users and items. Our approach aims to reduce the negative impact of popularity and sample bias simultaneously. We theoretically analyze the effectiveness of the proposed method and reveal the relationship between popularity and gradient, which justifies the correction strategy. We extensively evaluate our method on three public benchmarks over balanced and imbalanced settings. The results demonstrate its superiority over the existing debiased strategies, not only on the entire datasets but also when segmenting the datasets based on item popularity.

Personalized recommendation plays a central role in various online applications. To provide quality recommendation service, it is of crucial importance to consider multi-modal information associated with users and items, e.g., review text, description text, and images. However, many existing approaches do not fully explore and fuse multiple modalities. To address this problem, we propose a multi-modal contrastive pre-training model for recommendation. We first construct a homogeneous item graph and a user graph based on the relationship of co-interaction. For users, we propose intra-modal aggregation and inter-modal aggregation to fuse review texts and the structural information of the user graph. For items, we consider three modalities: description text, images, and item graph. Moreover, the description text and image complement each other for the same item. One of them can be used as promising supervision for the other. Therefore, to capture this signal and better exploit the potential correlation of intra-modalities, we propose a self-supervised contrastive inter-modal alignment task to make the textual and visual modalities as similar as possible. Then, we apply inter-modal aggregation to obtain the multi-modal representation of items. Next, we employ a binary cross-entropy loss function to capture the potential correlation between users and items. Finally, we fine-tune the pre-trained multi-modal representations using an existing recommendation model. We have performed extensive experiments on three real-world datasets. Experimental results verify the rationality and effectiveness of the proposed method.

Currently the collaborative filtering based recommender system has become more and more indispensable due to its capability in providing users with personalised suggestions. Despite its advances in term of efficiency, easy implementation and robustness, traditional collaborative filtering techniques suffer from several challenges such as cold-start and data sparsity. To overcome these limitations, external information is expected to help improve the overall effectiveness. Among the diverse context information, trust relationships is a widely utilised mechanism. Meanwhile, researchers also found distrust relationships is unavoidable in social network and recommender systems can benefit from distrust information. However, most existed distrusted oriented methods do not take the property of multi-facets in distrust relationships into consideration. In this paper, we exploit distrust relationships in a multi-faceted perspective and proposed a matrix factorization based model with integration of different distrust relationship of quality user between different people. Experimental study on well-known dataset has shown promising result and it is expected that this work could provide insight for researchers in this domain to further discuss the distrust in recommender systems.