Abstract
Latent factor model is better at capturing global information, but not local information. Therefore, many models combine the nearest neighbor information in the latent factor model to improve the performance in recommender system. However, the current fusion models pay no attention to the relationship between the accuracy of local information and model performance. In addition, the expression of local information is different between explicit neighbors and implicit neighbors, so that explicit feedback and implicit feedback have different values. Current fusion models usually utilize only one kind of feedback to capture the local information, which leads to the insufficiency and inaccuracy for using local information. These exited methods do not effectively learn the deep correlation between the similar users and similar items which all can represent the local information. Utilizing the recommender system of deep learning, we propose a fusion MLP model based on Joint Similar Groups, which utilizes both explicit feedback and implicit feedback in local information and learn the deep correlation between similar users and similar items. Experiments on two datasets show that our modes outperform state-of-art algorithms in explicit recommendation task.
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Acknowledgements
This work has been supported by Major Natural Science Research Projects of Colleges and Universities in Jiangsu Province of China (19KJA510011), National Natural Science Foundation of China (71871109).
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Li, Z., Li, S., Liu, Y. et al. Application of MLP Based on Joint Similar Groups in User Interest Expression and Recommendation Service. SN COMPUT. SCI. 3, 309 (2022). https://doi.org/10.1007/s42979-022-01187-w
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DOI: https://doi.org/10.1007/s42979-022-01187-w