Recommendation Method for Potential Factor Model Based on Time Series Drift

被引：0
|
作者：
Hao D. [1 ,2 ]
Guangwei H. [1 ,2 ]
Ting W. [1 ,2 ]
Wei S. [3 ]
机构：
[1] School of Information Management, Nanjing University, Nanjing
[2] Institute of Government Data Resources, Nanjing University, Nanjing
[3] School of Automation Science and Electrical Engineering, Beihang University, Beijing
来源：
Data Analysis and Knowledge Discovery | 2022年 / 6卷 / 10期
关键词：
Interest Drift; Latent Factor; Matrix Decomposition; Recommendation System; Time Series;
D O I：
10.11925/infotech.2096-3467.2021.1464
中图分类号：
学科分类号：
摘要：
[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. © 2022, Chinese Academy of Sciences. All rights reserved.
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页码：1 / 8
页数：7
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