The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
An important goal of a recommender system is to solve the top-k recommendation problem, however, there is no perfect ranking list for any recommender algorithm. Much work has been done on the recommendation list to improve user experience. In this paper, we focus on the technique of dithering which can be used in an online recommendation situation and be neglected in most academic research, and propose...
Collaborative Filtering (CF) is widely applied to personalized recommendation systems. Traditional collaborative filtering techniques make predictions through a user-item matrix of ratings which explicitly presents user preference. With the increasingly growing number of users and items, insufficient rating data still leads to the decreasing predictive accuracy with traditional collaborative filtering...
According to the recommendation quality is not high and cold start problem of the recommendation system in the case of sparse data, a collaborative filtering algorithm based on the combination of matrix decomposition technique and social network trust model is proposed. First of all, in the degree of trust computing, expert node method is introduced to determine the existence of multiple paths of...
Online music radios, such as Last.fm and Douban.fm, which provide users with free music, have gained much popularity in recent years. In online music radios, music recommendation plays a central role in recommending the most relevant music to users who are most likely to listen to. Different from traditional on-demand music service, online music radios have only users' listening records instead of...
Finding appropriate adslots to display ads is an important step to achieve high conversion rates in online display advertising. Previous work on ad recommendation and conversion prediction often focuses on matching between adslots, users and ads simultaneously for each impression at micro level. Such methods require rich attributes of users, ads and adslots, which might not always be available, especially...
With an ever-increasing amount of information made available via the Internet, it is getting more and more difficult to find the relevant pieces of information. Recommender systems have thus become an essential part of information technology. Although a lot of research has been devoted to this area, the factors influencing the quality of recommendations are not completely understood. This paper examines...
Contextual information is proven helpful to recommender system. And context-aware recommender system(CARS) has been applied in various applications. To improve the accuracy of context-aware recommendation and make recommender application development easier, we develop a lightweight software framework named ConRec, which introduces a dynamic context oriented approach to extend traditional reduction...
As the clustering-based model has better scalability than typical collaborative filtering methods, it has become one of the most successful approaches for recommender systems. However, since clustering-based algorithms often result in nearby users being divided into different clusters, they only recommend items being rated by users belonging to the same cluster with the active user, and recommendation...
With the increasing numbers of Web services and service users on World Wide Web, predicting QoS(Quality of Service) for users will greatly aid service selection and discovery. Due to the different backgrounds and experiences of users, they have different QoS experiences when interacting with the same service. Even two users who have similar experiences on some services can have diverging views when...
Taking into account continuously growing content wealth of pervasive environments generally, user needs assistance to find what he want in short time. Specifically in pervasive learning environment where learners are surrounded by numerous suppliers and the rich resources offered by the learning platform, the personalized recommender systems seems important for providing user by convenience and fulfil...
Ranking problems arise in a wide range of real world applications where an ordering on a set of examples is preferred to a classification model. These applications include collaborative filtering, information retrieval and ranking components of a system by susceptibility to failure. In this paper, we present an ongoing project to rank the underground primary feeders of New York City's electrical grid...
Collaborative filtering (CF) is one of the most effective types of recommender systems. As data sparsity remains a significant challenge for CF, we consider basing predictions on imputed data, and find this often improves performance on very sparse rating data. In this paper, we propose two imputed neighborhood based collaborative filtering (INCF) algorithms: imputed nearest neighborhood CF (INN-CF)...
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.