Online Publication Recommendation Based on Weighted Features of User Multiple Interest Drift
Qian Cong1,2,Qi Jianglei1,Ding Hao1()
1School of Information Management, Nanjing University, Nanjing 210023, China 2Department of Chinese Language and Literature, Hetao University, Bayannur 015000, China
[Objective] This paper improves the Reinforced Latent Factor Model with user multi-adaptive preference feature temporal weighting, aiming to improve the accuracy of recommendations. [Methods] Building upon the Temporal Potential Factor Model, we further integrated user preferences from different periods, such as interest forgetting features, publication interest overlap, and semantic similarity of comments. The user rating matrix is weighted and decomposed based on preference weights to capture the multiple preference changes of users towards different publications at different times. [Results] We conducted comparison experiments with four baseline methods based on temporal matrix factorization with three datasets. The proposed model’s precision was 9.26% higher than TDMF, 17.35% higher than TMRevCo, 38.63% higher than BPTF, and 26.24% higher than TCMF. This demonstrates that the proposed model is more accurate in extracting user temporal features. [Limitations] The analysis of interest drift evolution depends on historical user data. When the data is too sparse, alternative user information is required for a cold start. [Conclusions] The proposed model considers user forgetting and comment evolution features, effectively capturing user temporal interest drift and reflecting the evolving relationship of users’ interest in publications. It improves the accuracy of recommendations.
钱聪, 齐江蕾, 丁浩. 基于用户多重兴趣漂移特征权重的网络出版物推荐研究*[J]. 数据分析与知识发现, 2023, 7(8): 119-127.
Qian Cong, Qi Jianglei, Ding Hao. Online Publication Recommendation Based on Weighted Features of User Multiple Interest Drift. Data Analysis and Knowledge Discovery, 2023, 7(8): 119-127.
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