Comparison of Recommendation Using Social Network Analysis with Collaborative Filtering in Social Network Sites
نویسندگان
چکیده
منابع مشابه
Social network collaborative filtering
This paper demonstrates that "social network collaborative filtering" (SNCF), wherein user-selected like-minded alters are used to make predictions, can rival traditional user-to-user collaborative filtering (CF) in predictive accuracy. Using a unique data set from an online community where users rated items and also created social networking links specifically intended to represent likeminded ...
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اندیشمندان و صاحب نظران علوم اجتماعی بر این باورند که مرحله تازه ای در تاریخ جوامع بشری اغاز شده است. ویژگیهای این جامعه نو را می توان پدیده هایی از جمله اقتصاد اطلاعاتی جهانی ، هندسه متغیر شبکه ای، فرهنگ مجاز واقعی ، توسعه حیرت انگیز فناوری های دیجیتال، خدمات پیوسته و نیز فشردگی زمان و مکان برشمرد. از سوی دیگر قدرت به عنوان موضوع اصلی علم سیاست جایگاه مهمی در روابط انسانی دارد، قدرت و بازتولید...
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Collaborative Filtering(CF) is a well-known technique in recommender systems. CF exploits relationships between users and recommends items to the active user according to the ratings of his/her neighbors. CF suffers from the data sparsity problem, where users only rate a small set of items. That makes the computation of similarity between users imprecise and consequently reduces the accuracy of...
متن کاملEffective Constraint based Clustering Approach for Collaborative Filtering Recommendation using Social Network Analysis
ISSN 2250 – 107X | © 2011 Bonfring Abstract--Recommender system helps people to find information or items that they needed. Collaborative Filtering (CF) is an eminent technique in recommender systems. CF uses relationships between users and recommends items to the active user based on the ratings of his/her neighbors. But, there are several drawbacks in CF like data sparsity problem, where user...
متن کاملSocial Network Collaborative Filtering: Preliminary Results
This paper reports on a preliminary empirical study comparing methods for collaborative filtering (CF) using explicit consumers’ social networks. As user-generated social networks become increasingly important and visible in technology-mediated consumer interactions, we can begin to ask how the rich associated information can be used to improve inference. Theories from social psychology have lo...
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ژورنال
عنوان ژورنال: Journal of the Korea society of IT services
سال: 2014
ISSN: 1975-4256
DOI: 10.9716/kits.2014.13.2.173