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Collaborative filtering (CF) predicts user preferences in item selection based on the known user ratings of items. As one of the most common approach to recommender systems, CF has been proved to be effective for solving the information overload problem. CF can be divided into two main branches: memory-based and model-based. Most of the present researches improve the accuracy of Memory-based algorithms...
Recommendation can be reduced to a sub-problem of link prediction, with specific nodes (users and items) and links (similar relations among users/items, and interactions between users and items). However, the previous link prediction algorithms need to be modified to suit the recommendation cases since they do not consider the separation of these two fundamental relations: similar or dissimilar and...
Recommender systems help Web users to address information overload. However, their performance depends on the number of provided ratings by users. This problem is amplified for a new user because he/she has not provided any ratings. In this paper, we consider the new user problem as an optimization problem and propose a non-myopic active learning method to select items to be queried from the new user...
Context prediction approaches forecast future contexts based on known context patterns to adapt e.g., services in advance. In the case of the user's context history not providing suitable context information for the observed context pattern, to the best of our knowledge context prediction algorithms will fail to forecast the appropriate future context. To overcome the gap of missing context information...
This paper presents a probabilistic co-clustering approach to pattern discovery in preference data. We extended the original formulation of the block mixture model to handle rating data, the resulting model allows the simultaneous clustering of users and items in homogeneous user communities and item categories. The parameter of the model are determined using a variational approximation and a two-phase...
In many practical recommender systems, it is found difficult to obtain explicit feedback from users about the preference for a specific item, such as music, book, movie, etc. Most researches up to this point has focused on tracking various sources of implicit feedback from user behavior including purchase history, browsing patterns, and watching habits, in order to model user preference. In this paper,...
Recommender systems have made significant progress over the last decade and several industrial-strength systems have been developed. Typically, recommender systems try to predict people's preferences and use accuracy indices such as mean absolute error to judge the performance of the algorithms. Recently, the diversity index is widely accepted as another metric. However, the ability of a recommendation...
In recent years, collaborative filtering becomes one of the most successful recommender systems. Its key technique is to predict new ratings from the known ratings. Unfortunately, in the previous research, the temporal information was rarely applied. That is to say, the ratings at different time were considered the same. However, from our point of view, not only the mean values of ratings in different...
This study devotes to improve the prediction accuracy of prediction algorithms in recommender systems which one is collaborative filtering algorithm to estimate user's preference to items transacted on the web. From the our experiment, data scarcity problem is critical factor for decreasing prediction accuracy so the method for reducing data scarcity is meaningful way to increase prediction accuracy...
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...
Collaborative filtering (CF) is one of the most successful technologies in recommender systems, and widely used in many personalized recommender areas, such as e-commerce, digital library and so on. However, most collaborative filtering algorithms suffer from data sparsity which leads to inaccuracy of recommendation. In this paper, we focus on nearest-neighbor CF algorithms and propose a new collaborative...
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