[1]LI Hui,MA Xiaoping,HU Yun,et al.Social network recommendaton system mixing contex information[J].CAAI Transactions on Intelligent Systems,2015,10(2):293-300.[doi:10.3969/j.issn.1673-4785.201406017]
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Social network recommendaton system mixing contex information

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 2 Page number: 293-300 Column: 学术论文—自然语言处理与理解 Public date: 2015-04-25
Title:
Social network recommendaton system mixing contex information
Author(s):
LI Hui1; 2;  MA Xiaoping1;  HU Yun2;  SHI Jun2
1. School of Information & Electrical Engineering, China University of Mining & Technology, Xuzhou 221008, China;
2. Department of Computer Science, Huaihai Institute of Technology, Lianyungang 222005, China
Keywords:
context; information; social network; matrix factorization; recommendation; collaborative filtering
CLC:
TP391
DOI:
10.3969/j.issn.1673-4785.201406017
Abstract:
Contexts and social network information is valuable information for building an accurate recommender system. The merging of such information could further improve accuracy of the system and user satisfaction. This paper proposes the context and social (CS) network, which is novel context-aware recommender system incorporating elaborately processed social network information, in order to increase the user satisfaction on the recommendation system. The contextual information happens by applying random decision trees to partition the original user-item-rating matrix such that the ratings with similar contexts are together. The matrix factorization functionality is to predict missing preference of a user for an item using the partitioned matrix. An enhanced recommendation model aided by social relationships considering the context information is proposed. A trust-based Pearson Correlation Coefficient is proposed to measure user similarity. Real datasets based experiments showed that CS enhances its performance compared with traditional recommendation algorithms based on context and social networks.
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