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Collaborative filtering algorithms are successfully used in personalized recommender systems for their simplicity and high recommending quality. However, significant vulnerabilities have recently been identified in collaborative filtering recommender systems. Malicious users can inject a large number of biased profiles into such a system in order to make recommendations that favor or disfavor given...
Recommendation systems can help people to find interesting things and they are widely used in our life with the development of the Internet. Collaborative filtering technique has been proved to be one of the most successful techniques in recommendation systems in recent years. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of source data set is the major...
Recommender systems help to overcome the problem of information overload on the Internet by providing personalized recommendations to the users. Content-based filtering and collaborative filtering are usually applied to predict these recommendations. Among these two, Collaborative filtering is the most common approach for designing e-commerce recommender systems. Two major challenges for CF based...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.