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Recommender systems are designed in such a way that they sort through massive amounts of data so as to help users in finding their preferred items. Currently much research on recommender systems focus on improving the prediction or classification accuracy of the respective algorithms while behavioral aspects are often overlooked. In this paper we focus on a particular behavioral property called monotonicity...
Nowadays, recommender systems occupy an increasingly important position in people's lives. Recommender systems are widely applied in e-commerce websites, they discover users' potential consuming habits by analyzing their behaviors, and then recommend users with what they may purchase. However, recommender systems on e-commerce sites are facing the problem of data sparsity. Data sparsity may cause...
The Slope One Predictor is suitable for predicting the online rating-base collaborative filtering which is used for analyzing data related to persons' likes or interests in the menu which are variously diverse and the menus are plenty. The system is considered a Personalized Recommender System by using collaborative filtering of satisfaction that the researchers consider the menu fit for the data...
We propose a personalized electronic program guide (EPG) for IPTV. It uses memory-based collaborative filtering with a fast and accurate similarity method. The user's explicit interests-based proposed method predicts user's ratings on unexperienced content not previously rated by the user, and then arranges the content in order of highest ratings and classifies them according to their attributes....
With its unique advantages, the traditional collaborative filtering algorithm has been one of the original and most successful methods in product recommendation. However, the intensified sparsity of the matrix has caused a decreasing precision of particular recommendations, which the traditional CF algorithm is of little help. By making statistical analysis about users' selected items, this paper...
With the development of personalized recommendation systems, the research of collaborative filtering reached a bottleneck. Neither algorithm accuracy nor computational complexity can be improved significantly. In this paper, we present our statistics and analysis on some recognized datasets. The analysis shows that the real rating features of the users cannot follow even distribution while most current...
Recommendation systems represent personalized services that aim at predicting users' interest on information items available in the application domain. The computation of the neighbor set of users or resources is the most important step of the personalized recommendation system, and the key to this step is the calculation of similarity. This paper analyzes three main similarity algorithms and finds...
Recommender system has a long history as a successful application in artificial intelligence. A growth in the number of products, which has been offered by different e-commerce platforms, leads to a technology which can help customers to choose and buy products. Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user...
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