1School of Information Management, Nanjing University, Nanjing 210023, China 2Institute of Government Data Resources,Nanjing University,Nanjing 210023, China 3School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
[Objective] This paper proposes a decomposition model for potential factors based on time series drift, aiming to capture the characteristics of changing user interests and improve the recommendation accuracy. [Methods] First, we built a model combining the temporal dynamic evolution of user preferences and the impacts of their previous behaviors on current ones. Then, we constructed an auxiliary matrix to capture the evolution of users. Finally, we introduced a time impact factor to balance the influence of current and past behaviors. [Results] We examined our model with three experimental datasets. Compared with the baseline method, the accuracy was improved by 40.02%, 3.75% and 19.81% on average. [Limitations] The evolution analysis of interest drift relies on historical data. When the amount of historical data is too sparse, other user information needs to be used for a cold start. [Conclusions] The proposed model has stronger generalization ability to process the characteristics of interest fluctuation, which accurately analyzes user interest evolution, and effectively improves the recommendation performance of enterprises.
丁浩, 胡广伟, 王婷, 索炜. 基于时序漂移的潜在因子模型推荐方法*[J]. 数据分析与知识发现, 2022, 6(10): 1-8.
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