A Personalized Recommendation Model with Time Series Fluctuation of User Interest

doi:10.11925/infotech.2096-3467.2021.0292

Data Analysis and Knowledge Discovery

2021, Vol. 5

Issue (11): 45-58 DOI: 10.11925/infotech.2096-3467.2021.0292

Current Issue | Archive | Adv Search

A Personalized Recommendation Model with Time Series Fluctuation of User Interest

Ding Hao^1,²,Ai Wenhua^1,²,Hu Guangwei^1,²(

),Li Shuqing³,Suo Wei⁴

¹School of Information Management, Nanjing University, Nanjing 210023, China
²Institute of Government Data Resources,Nanjing University,Nanjing 210023, China
³School of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China
⁴School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191,China

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Abstract

[Objective] This paper constructs a prediction model based on hybrid time series to improve the recommendation accuracy. [Methods] First, we constructed a trend prediction model using neural network and fuzzy clustering technique for interest fluctuations at different magnitudes. Then, we utilized neural network to extract and predict the sliding features of small fluctuation series. Finally, we used the membership degree of fuzzy clustering to divide the relationship for large fluctuation series data. [Results] User simulation tests with four groups of experimental data showed that extracted data features for different amplitudes of interest fluctuation yielded more accurate prediction results, which were 19.18% lower than other algorithms’ RMSE and 45.78% higher than other algorithms' Hit-Ratio. [Limitations] The analysis of time fluctuation relies on historical data, therefore, additional cold-start algorithm is needed to preprocess the sparse historical data. [Conclusions] This method could effectively process the fluctuation of interest, and improve the personalized information services.

Key words： Time Series Interest Fluctuation Neural Networks Fuzzy Clustering Hybrid Forecasting Personalized Recommendation

Received: 24 March 2021 Published: 23 December 2021

ZTFLH:

TP391

Fund:National Social Science Fund of China(20&ZD154);Innovation and Entrepreneurship Big Data and Theoretical Research Project of Nanjing University in 2021(NJU-DI2021004);Key University Science Research Project of Jiangsu Province(19KJA510011)

Corresponding Authors: Hu Guangwei,ORCID：0000-0003-1303-363X E-mail: hugw@nju.edu.cn

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Cite this article:

Ding Hao, Ai Wenhua, Hu Guangwei, Li Shuqing, Suo Wei. A Personalized Recommendation Model with Time Series Fluctuation of User Interest. Data Analysis and Knowledge Discovery, 2021, 5(11): 45-58.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0292 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I11/45

The Framework of Model

Structure Division of Project Compound Category

Schematic Diagram of Neural Network Model

Schematic Diagram of Time Series Fuzzy Clustering Model

Division of Evaluation Criteria for Fuzzy Time Series

Basic Information of MovieLens Dataset

Test Data Set Size

Percentage of Each Tag Data in MovieLens Dataset

Parameters α and δ Performance Verification Results in Different Test

时间单位	兴趣波动幅度 $α$ -取值区间比例
$α$ -区间	[0,0.05）	[0.05,0.10）	[0.10,0.15）	[0.15,0.20）	[0.20,0.25）
日	0.480 3	0.312 5	0.059 1	0.036 7	0.031 6
月	0.468 7	0.321 7	0.066 4	0.038 8	0.026 6
年	0.3991	0.340 4	0.062 2	0.053 3	0.036 8
$α$ -区间	[0.25,0.30）	[0.30,0.35）	[0.35,0.40）	[0.40,0.45）	[0.45,∞）
日	0.024 4	0.021 3	0.019 4	0.011 2	0.003 5
月	0.022 5	0.020 9	0.017 4	0.011 8	0.005 2
年	0.032 4	0.027 3	0.024 7	0.017 1	0.006 7

Overall Distribution of User Interest Fluctuation Range

Time Series Variance Change of User Interest Amplitude Fluctuation

The Relationship Between Hit Ratio and VAR

Fuzzy Relation Group of User Fuzzy Time Series （Some Examples）

Evaluation of Different Interest Fluctuation Prediction Methods

Iterative Training Prediction Performance of Different Models

Time and Space Consumption of Different Time Series Models

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