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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (10): 1-8    DOI: 10.11925/infotech.2096-3467.2021.1464
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Recommendation Method for Potential Factor Model Based on Time Series Drift
Ding Hao1,2,Hu Guangwei1,2(),Wang Ting1,2,Suo Wei3
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
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Abstract  

[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.

Key wordsTime Series      Interest Drift      Latent Factor      Recommendation System      Matrix Decomposition     
Received: 28 December 2021      Published: 16 November 2022
ZTFLH:  TP391  
Fund:National Social Science Fund of China(20&ZD154)
Corresponding Authors: Hu Guangwei,ORCID:0000-0003-1303-363X      E-mail: hugw@nju.edu.cn

Cite this article:

Ding Hao, Hu Guangwei, Wang Ting, Suo Wei. Recommendation Method for Potential Factor Model Based on Time Series Drift. Data Analysis and Knowledge Discovery, 2022, 6(10): 1-8.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1464     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I10/1

Latent Factor Model Based on NMF
Potential Factor Decomposition Model Based on Time Series Drift
数据集 用户数量 项目数量 评分数量
Appliances 249 587 20 134 1 834 523
Books 578 858 61 379 4 159 480
VideoGames 421 256 35 880 2 535 441
Description of Experimental Data set size
The Relationship Between the Value of Time Influence Factor and Precision
NDCG Between TDMF and Baseline Models with Different TopN
Precision Between TDMF and Baseline Models with Different TopN
TimeSVD++ P@100 F1@100 NDCG@100 P@200 F1@200 NDCG@200 R@100 R@200
Appliances 0.359 2 0.341 6 0.311 9 0.339 7 0.332 8 0.350 5 0.325 6 0.326 2
Books 0.566 1 0.492 9 0.492 8 0.554 5 0.492 0 0.525 8 0.436 5 0.442 1
Video Games 0.502 7 0.501 9 0.426 3 0.500 8 0.496 5 0.451 4 0.501 2 0.492 2
TDMF P@100 F1@100 NDCG@100 P@200 F1@200 NDCG@200 R@100 R@200
Appliances 0.599 8 0.535 5 0.556 6 0.581 5 0.530 2 0.559 1 0.483 6 0.487 2
Books 0.831 5 0.807 9 0.816 6 0.768 5 0.778 7 0.828 9 0.785 6 0.7891
Video Games 0.779 0 0.729 3 0.751 1 0.765 2 0.728 6 0.748 3 0.685 6 0.695 4
TCMF P@100 F1@100 NDCG@100 P@200 F1@200 NDCG@200 R@100 R@200
Appliances 0.545 2 0.496 7 0.501 7 0.533 5 0.494 1 0.521 6 0.456 2 0.460 1
Books 0.801 3 0.779 9 0.785 6 0.754 3 0.757 8 0.797 9 0.759 6 0.761 3
Video Games 0.712 7 0.686 6 0.683 9 0.703 3 0.686 8 0.709 4 0.662 3 0.671 1
TMRevCo P@100 F1@100 NDCG@100 P@200 F1@200 NDCG@200 R@100 R@200
Appliances 0.513 9 0.461 3 0.470 6 0.508 7 0.464 7 0.488 9 0.418 5 0.427 7
Books 0.749 3 0.685 9 0.696 9 0.644 0 0.643 8 0.713 4 0.632 5 0.643 5
Video Games 0.696 7 0.646 0 0.672 6 0.691 1 0.652 6 0.686 7 0.602 1 0.618 2
Detailed Indicators Between TDMF and Baseline Models
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