| || Application Domain and Functional Classification of Recommender Systems—A Survey |
Author :Rao, K. Nageswara;Talwar, V.G.
Source: DESIDOC Journal of Library and Information Technology :28(3) ; 2008 ; pp.17-35
Abstract : 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 information technology have dramatically reduced the barriers in electronic publishing and distribution of information over 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 him suggestions 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 and disadvantages of various filtering and recommender systems.
Keywords : Recommender system;Filtering system;Domain analysis;content based filtering system;Collaborative Filtering Systems;Demographic Filtering Systems