融合用户微博兴趣挖掘与协同过滤的新闻推荐
News Recommendation Combining User Microblog Interests Mining and Collaborative Filtering
DOI: 10.12677/CSA.2019.91005, PDF,  被引量    国家科技经费支持
作者: 张 帅*, 陈平华, 陈靖宇：广东工业大学计算机学院，广东 广州
关键词: 冷启动；兴趣挖掘；微博；新闻推荐；协同过滤；Cold Start； Interest Mining； Microblog； News Recommendation； Collaborative Filtering
摘要: 针对基于内容的新闻推荐中存在的多样性不足、潜在兴趣缺失等问题和协同过滤的推荐方法存在的冷启动问题，提出了一种融合微博用户兴趣挖掘与协同过滤的新闻推荐算法。通过分析了微博的数据结构，使用UI-FP挖掘方法挖掘用户微博上的用户兴趣，将此挖掘兴趣集和用户历史浏览新闻纪录兴趣集作为新闻推荐的元数据，结合协同算法来实现微博数据和新闻数据的融合，从而解决前述的冷启动和潜在兴趣缺失问题。实验结果表明，该方法符合预期。
Abstract: In order to solve the problems of insufficient diversity and potential interest in content-based news recommendation and the cold start problem of collaborative filtering recommendation methods, a news recommendation algorithm combining microblog user interest mining and collaborative filtering is proposed. By analyzing the data structure of Weibo, UI-FP mining method is used to mine user interest on user Weibo, and this user interest set and user history browsing news record are used as user model interest set as metadata for news recommendation, combined with collaboration. The algorithm implements the fusion of microblog data and news data to solve the aforementioned cold start and potential lack of interest. Experimental results show that the method is in line with expectations.
文章引用：张帅, 陈平华, 陈靖宇. 融合用户微博兴趣挖掘与协同过滤的新闻推荐[J]. 计算机科学与应用, 2019, 9(1): 38-45. https://doi.org/10.12677/CSA.2019.91005

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