期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：2
DOI：10.14569/IJACSA.2020.0110267
出版社：Science and Information Society (SAI)
摘要：With the elevation of the online accessibility to almost everything, many logics, systems and algorithms have to be revised to match the pace of the trends among the socialized networks. One such system; recommendation system has become very important as far as the socialized networks are concerned . In such paced and vibrant environment of the online accessibility and availability to heavy and large amount of data uploaded to the internet such as, movies, books, research articles and much more. The method of recommendation where provides the socialized networks between the operators, at the same instance, it provides references for the users to asses other users that effects their socialized relation directly or indirectly. Collaborative filtering is the technique used for recommending the same taste of picks to that of the user, and it is accomplished by the user’s mutual collaboration, this technique is mostly used by the social networking sites. Nowadays this technique is not only popular but common for recommending the data to the user; meanwhile it also motivates the researchers to find the more effective system and algorithm so that the user’s satisfaction can be achieved by recommending them the data according to their search history. This paper suggests the CF (Collaborative Filtering) model that is based on the user’s truthful information applied by the FCM (Fuzzy C-means) clustering. This study proposes that the fuzzy truthful information of the user is to be combined with rating of the content by other users to produce a recommender system formula with a coupled coefficient with new parameters. To achieve the results the Data set of Movie Lens is included in the study which shows significant improvement in the recommendation subjected to the condition of cold start.
关键词：Recommender system; collaborating filtering; cold start problem; clustering; user based clustering
