Abstract:

A knowledge-aware recommendation model called Fusion of Denoising Strategies and Multi-View Contrastive learning （FDSMVC）， was proposed to address the issues of poor noise reduction， inadequate extraction of semantic information between items， and imbalanced utilization of information in the Knowledge Graph （KG）-based recommendation models. Firstly， noise reduction was performed on the user-item interaction graph and the knowledge graph through dropping edges selectively and masking low-weight triplets with a weighted function， respectively. Secondly， random Singular Value Decomposition （SVD）， cosine similarity， k-Nearest Neighbors （kNN） sparsity， and path-based graph attention network were used to construct collaborative view， semantic view between items， and structural view， respectively. Thirdly， intra-graph， local， and global contrastive learnings were applied to multiple views. Finally， a multi-task strategy was applied to optimize the recommendation task and the contrastive learning task jointly， resulting in probability of user-item interactions. Experimental results show that on five real-world datasets： Book-Crossing， MovieLens-1M， Last.FM， Alibaba-iFashion， and Yelp2018， compared to the best baseline model， FDSMVC model achieves improvements of 1.06%-2.04% in Area Under the Curve （AUC） and 1.52%-2.06% in F1 score， and has the Recall@K also better than the best baseline model.

Key words: recommender system, Knowledge Graph (KG), contrastive learning, Graph Neural Network (GNN), self-supervised learning

摘要：

针对基于知识图谱（KG）的推荐模型中存在的降噪效果不佳、项目间语义信息提取不足和信息利用不平衡的问题，提出一种融合降噪策略与多视图对比学习（FDSMVC）的知识感知推荐模型。首先，分别以选择性丢边和加权函数掩盖低权重三元组的方式对用户项目交互图与知识图进行降噪；其次，分别采用随机奇异值分解（SVD）、余弦相似度与k-最近邻（kNN）稀疏法和基于路径的图注意力网络构建协同视图、项目间的语义视图和结构视图；再次，将多个视图进行图内、局部和全局这3种对比学习；最后，利用多任务策略联合优化推荐任务和对比学习任务，从而得到用户与项目交互的可能性。实验结果表明，相较于最优的基线模型，在Book-Crossing、MovieLens-1M、Last.FM、Alibaba-iFashion和Yelp2018共5个真实数据集上，FDSMVC模型的曲线下面积（AUC）和F1分数分别提升了1.06%~2.04%和1.52%~2.06%，且Recall@K也优于最优的基线模型。

关键词: 推荐系统, 知识图谱, 对比学习, 图神经网络, 自监督学习