Causal neural fuzzy inference modeling of missing data in implicit recommendation system

作者：Zhang, Weina; Zhang, Xingming^*; Chen, Dongpei

来源：Knowledge-Based Systems, 2021, 222: 106678.

DOI：10.1016/j.knosys.2020.106678

• Implicit recommendation
• Fuzzy inference
• Causal recommendation
• Matrix Factorization

Implicit feedback data has many interaction form, such as clicking, collection and play count. These interactions can be tracked automatically, which can alleviate the problem of data sparsity caused by the difficulty of obtaining explicit ratings. Therefore, implicit recommendation attracts many scholars' attentions. However, the uncertainty of the implicit feedback data takes a great challenge to the recommendation. Especially for the negative feedback (missing data), we could not be sure whether the users dislike or just have not seen the items. If we regard them as negative feedback, it may lead to bias of predictions. In this paper, we propose Causal Neural Fuzzy Inference (CNFI) algorithm to model the missing data in implicit recommendation. First, we give the mixed recommendation model based on CNFI. Specially, we analyze the impact factors of users exposure to items from causal perspective. Technically, fuzzy set theory is used for representing the impact factors. Causal neural fuzzy inference network is applied to learn the weights of fuzzy rules based on the fuzzy representation of impact factors. Finally, CNFI and Matrix Factorization (MF) perform joint learning to make recommendations. Extensive experiments on three realistic datasets verify the effectiveness and advancement of the proposed algorithm.