A personalized recommendation strategy based on emotional analysis and forgetting rule

Zhubing Lu; Ma Xiaoqin; Wu Wenjuan

doi:10.1117/12.2640990

2 December 2022 A personalized recommendation strategy based on emotional analysis and forgetting rule

Zhubing Lu, Ma Xiaoqin, Wu Wenjuan

Proceedings Volume 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022); 122881L (2022) https://doi.org/10.1117/12.2640990
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 2022, Zhuhai, China

Abstract

In traditional collaborative filtering recommendation system, drawbacks such as interest drift and data sparseness always exist, which has weakened performance of the system. In this paper, a novel approach from the perspective of emotion analysis and forgotten rules was proposed to offset the influence on recommendation accuracy caused by interest migration. Firstly, user online comment has been analyzed to extract emotional words and the rated object using optimized DP algorithm. Secondly, the emotional words about the rated object have been appropriate quantified so that we can take advantage of the user emotional tendency to generate interest model. Thirdly, the Ebbinghaus forgetting curve is also introduced into the recommendation process to overcome the problem that caused by the change of user interest, which can effectively increase the accuracy and offer user better service when interest move. The simulation experiment shows that the strategy could greatly reduce MAE and effectively improve coverage.

Citation Download Citation

Zhubing Lu, Ma Xiaoqin, and Wu Wenjuan "A personalized recommendation strategy based on emotional analysis and forgetting rule", Proc. SPIE 12288, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2022), 122881L (2 December 2022); https://doi.org/10.1117/12.2640990

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