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| Improved HOSVD Recommendation Algorithm Taking into Account User’s Emotions |
| GUO Qiang1,YUE Qiang1,LI Rende1,LIU Jianguo2
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1.Complex Systems Science Research Center, University of Shanghai for Science and Technology, Shanghai 200093, China;
2.Institute of Financial Technology Laboratory, Shanghai University of Finance and Economics, Shanghai 200433, China |
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Abstract Traditional 3rd order Singular Value Decomposition (HOSVD) recommendation algorithm is based on mining the potential relationship among users, item labels and items. However, this method does not take the user's emotions into account. Based on the preference of user emotions extracted from the emoji expressions in the comments, this paper proposes a HOSVD recommendation algorithm that introduces the user's emotional preferences. This method classifies emoji expressions, weights them, and calculates the weighted sum of the emoji expressions of different types to represent the user's emotions. 3rd order tensor model is introduced to store three tuple data of user, user emotion and item, and HOSVD decomposition is applied personalized recommendations. In this paper, a numerical experiment is conducted on an empirical dataset of online Internet education. The results show that this method improves the accuracy and recall performance indexes better than the collaborative filtering algorithm based on items, PersonalRank algorithm and HOSVD algorithm based on user-tag-item triple information. When Top-1 recommendation is made, the accuracy and recall rate can reach 0.353 and 0.281. The work of this paper provides a reference for the personalized recommendation of mobile.
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Published: 16 May 2019
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