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Deep Collaborative Filtering Algorithm with Embedding Implicit Similarity Groups |
Li Zhenyu,Li Shuqing() |
College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210023, China |
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Abstract [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.
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Received: 08 February 2021
Published: 23 December 2021
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Fund:Major Natural Science Research Projects of Colleges and Universities in Jiangsu Province of China(19KJA510011) |
Corresponding Authors:
Li Shuqing,ORCID:0000-0001-9814-5766
E-mail: leeshuqing@163.com
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