智能系统学报Issue(2)：293-300,8.DOI:10.3969/j.issn.1673-4785.201406017
融合上下文信息的社会网络推荐系统
Social network recommendaton system mixing contex information
摘要
Abstract
Contexts and social network information is valuable information for building an accurate recommender sys⁃tem. 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 e⁃laborately 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⁃rat⁃ing 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 com⁃pared with traditional recommendation algorithms based on context and social networks.关键词
上下文/信息/社会网络/矩阵因式分解:推荐/协同过滤Key words
context/information/social network/matrix factorization/recommendation/collaborative filtering分类
信息技术与安全科学引用本文复制引用
李慧,马小平,胡云,施珺..融合上下文信息的社会网络推荐系统[J].智能系统学报,2015,(2):293-300,8.基金项目
国家自然科学基金资助项目（61403156，61403155）；江苏省高校自然科学基金资助项目（13KJB520002，14KJB520005）． （）
