[Objective] This paper tries to construct a deep collaborative filtering model that can capture local relevance as well as explicit / implicit feedbacks. [Methods] In the explicit recommendation tasks, we embedded similar groups found by implicit feedback search. Then, we create models for user-item group, user-similar-item group, and item-similar-user group with Multi-Layer Perceptron. [Results] We examined the new algorithm with MovieLens datasets. Compared with existing methods, the MAE and RMSE of our model were reduced by 10.94% and 11.79% respectively. [Limitations] More research is needed to identify the optimal number of the nearest neighbors for different datasets. [Conclusions] The new model could more effectively generate the recommendation results.
李振宇, 李树青. 嵌入隐式相似群的深度协同过滤算法*[J]. 数据分析与知识发现, 2021, 5(11): 124-134.
Li Zhenyu, Li Shuqing. Deep Collaborative Filtering Algorithm with Embedding Implicit Similarity Groups. Data Analysis and Knowledge Discovery, 2021, 5(11): 124-134.
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