文章基本信息

标题：Application Domain and Functional Classification of Recommender Systems—A Survey
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作者：K. Nageswara Rao
期刊名称：DESIDOC Journal of Library & Information Technology
电子版ISSN：0976-4658
出版年度：2008
卷号：28
期号：3
页码：17-35
语种：English
出版社：DESIDOC, Ministry of Defence, India
摘要：The amount of scientific and technical information is growing exponentially. As a result, the scientific community has been overwhelmed by the information published in number of new books, journal articles,and conference proceedings. In addition to increasing number of publications, advances in informationtechnology have dramatically reduced the barriers in electronic publishing and distribution of informationover networks virtually anywhere in the world. As a result, the scientific community is facing the problem of locating relevant or interesting information. To address the problem of information overload and to sift all available information sources for useful information, recommender systems or filtering systems have emerged. Generally, recommender systems are used online to suggest items that users find interesting, thereby, benefiting both the user and merchant. Recommender systems benefit the user by making himsuggestions on items that he is likely to purchase and the business by increase of sales. Filtering information or generation of recommendations by the recommender systems mimic the process of information retrieval systems by incorporating advanced profile building techniques, item/user representation techniques, filtering and recommendation techniques, and profile adaptation techniques. This paper addresses the application domain analysis, functional classification, advantages anddisadvantages of various filtering and recommender systems.http://dx.doi.org/10.14429/djlit.28.3.174
关键词：Recommender system, filtering system, domain analysis, content-based filtering system