Cross-Domain Recommendation Algorithm Based on Deep Fusion of Side Information

doi:10.3778/j.issn.1002-8331.2107-0493

Abstract

Abstract: Collaborative filtering has been successfully used to provide users with personalized products and services. However, it faces the data sparsity problem as well as the cold start problem. One solution is to incorporate side information and the other is to learn knowledge from related fields. In this paper, a cross-domain recommendation algorithm based on deep fusion of side information CICDR is proposed by considering both, which integrates collective matrix factorization and deep transfer learning. The algorithm uses Semi-SDAE and matrix factorization（MF） to model in both source and target domain to learn the effective feature vectors fromrating information and side information, and makes more accurate recommendation by using the user’s implicit feedback information. In this way, the user and project potential factors learned in the two fields retain more semantic information for recommendation. Then, the incomplete orthogonal nonnegative matrix tri-factorization（IONMTF） is used to generate a common potential factor bridging the two related fields to alleviate the cold start problem and data sparsity problem in the target domain. The comparison with four classic algorithms on three real data sets verifies the effectiveness of the proposed algorithm and further improves the recommendation accuracy and user satisfaction.

Key words: deep learning, side information, implicit feedback, matrix factorization, cross-domain recommendation

摘要： 协同过滤已成功用于为用户提供个性化的产品和服务，然而它面临数据稀疏和冷启动的问题。一种解决方案是结合辅助信息，另一种是从相关领域学习知识。综合考虑了这两个方面，提出一种深度融合辅助信息的跨域推荐算法CICDR，它集成了集体矩阵分解和深度迁移学习。该算法通过Semi-SDAE和矩阵分解（MF）在源域和目标域中进行建模，学习评分信息和辅助信息中的有效特征向量，并利用用户的隐式反馈信息来做出更准确的推荐。通过这种方式，在两个领域中学习到的用户和项目潜在因素为推荐保留了更多的语义信息。通过非完备正交非负矩阵三分解（IONMTF）产生桥接两个相关领域的公共潜在因素，以缓解目标域中的冷启动和数据稀疏问题。在三个真实数据集上与四种经典算法进行对比，验证了提出算法的有效性，进一步提高了推荐精度和用户满意度。

关键词: 深度学习, 辅助信息, 隐式反馈, 矩阵分解, 跨域推荐

LU Yongqian, SHENG Jiagen. Cross-Domain Recommendation Algorithm Based on Deep Fusion of Side Information[J]. Computer Engineering and Applications, 2022, 58(24): 90-96.

陆永倩, 生佳根. 深度融合辅助信息的跨域推荐算法[J]. 计算机工程与应用, 2022, 58(24): 90-96.

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