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个性化图书推荐系统通过对用户借阅行为的统计分析,获取用户的兴趣特征,实现由原来的人找书到书找人一对一的个性化图书推荐。现有的图书推荐系统各有侧重,图书推荐算法及评价标准各具优、缺点。未来,图书推荐的研究热点及难点将集中在借阅记录的稀疏性、新图书问题、高校新生问题、用户统计学信息、根据《中国图书馆分类法》计算图书相似性、副本数及借阅规章制度等方面。
Personalized book recommendation system through the user’s borrowing behavior of statistical analysis to obtain the user’s interest characteristics, to find the original person to find books to find one-on-one personalized book recommendation. The existing book recommendation system has its own focus, book recommendation algorithms and evaluation criteria each have advantages and disadvantages. In the future, the research hot spots and difficulties in book recommendation will focus on the sparseness of borrowing records, new book issues, freshmen issues and user statistics information, and calculate the similarity of books, the number of copies and the borrowing rules and regulations according to “China Library Taxonomy” etc.