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| Recommendation Model Combining Implicit Influence of Trust with Trust Degree |
| ZHANG Binqi1, REN Lifang2, WANG Wenjian1,3 |
1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006;
2. School of Information, Shanxi University of Finance and Economics, Taiyuan 030006;
3. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006 |
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Abstract Some methods alleviate the cold start problem in recommender systems by combining traditional recommendation techniques and social information. However, the effect is poor due to the less available social information. Therefore, a recommendation model combining implicit influence of trust and trust degree(RIITD) is proposed in this paper. On the premise of introducing the trust relationship in social information, both the explicit behavior data of the user in the trust relationship and the implicit influence of trust relationship, such as the potential feature vector of trusted users, are taken into account to obtain the preference characteristics of cold start users. Consequently, the problem of inaccurate recommen-dation for the cold start users caused by less social information is alleviated. Moreover, the compre-hensive trust degree is introduced to reflect the different social influences between the target user and the trusted users, make the trusted users play a positive impact and improve the performance of the recommender system. Experiments on 3 commonly used datasets show that the proposed method can achieve high accuracy.
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Received: 26 November 2021
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| Fund:National Natural Science Foundation of China(No.62076154), Key Project of National Natural Science Foundation of China Regional Innovation and Development Joint Fund(No.U21A20513), Natural Science Foundation of Shanxi Province(No.201901D211175), Key R&D Program of Shanxi Province International Cooperation(No.201903D421050), Special Foundation from the Central Finance to Support the Development of Local University(No.YDZX20201400001224) |
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Corresponding Authors:
WANG Wenjian, Ph.D., professor. Her research interests include machine learning, data mining and re-commendation algorithms.
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| About author:: ZHANG Binqi, master student. Her research interests include recommendation algorithms. REN Lifang, Ph.D., lecturer. Her research interests include service calculation and recommendation algorithms. |
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2022, Vol. 35 
