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Recommender systems are becoming the crystal ball of the Internet because they can anticipate what the users may want, even before the users know they want it. However, the machine-learning algorithms typically involved in the training of such systems can be computationally expensive, and often may require several days for retraining. Here, we present a distributed approach for load-balancing the...
Collaborative filtering is one of the most widely-used algorithms in recommendation systems. In user-based collaborative filtering algorithm, current users' nearest neighbors are used to recommend items because they have similar preference, but users' preference varies with time, which often affects the accuracy of the recommendation. As a result of the varying users' preference, many researches about...
In this paper, we propose a new recommender algorithm based on Slope One algorithm and new similarity measurements. We incorporate additional sources of information about the users to relieve the cold start problem. Users generate a large number of interactions while browsing a website. These users' interactions are considered accurate enough to make recommendation. Then, we propose to take into account...
The Slope One class of algorithms have been shown to lead, although being relatively simple, to accuracies that are very close to the more commonly utilised memory-based Collaborative Filtering (CF) algorithms. In addition, this class of algorithms is highly scalable in comparison to memory-based algorithms. A recently observed phenomenon are profile injection attacks on recommendation algorithms...
In this paper, we propose a new recommender algorithm based on multi-dimensional users behavior and new measurements. It's used in the framework of our recommender system that use knowledge discovery techniques to the problem of making product recommendations during a live user interaction. Most of Collaborative filtering algorithms based on user's rating or similar item that other users bought, we...
In the recommendation of the bipartite networks, researchers have mainly dedicated to improve the accuracy of the recommendation, but neglected the fact that the entire history information which can be redundant or even misleading to the performance of recommendation. In this paper, we set unique weight to every link according to their temporal information and topology information. Then, we remove...
This paper focuses on the measure of recommendation stability, which reflects the consistency of recommender system predictions. Stability is a desired property of recommendation algorithms and has important implications on users’ trust and acceptance of recommendations. Prior research has reported that some popular recommendation algorithms can suffer from a high degree of instability. In this study,...
Collaborative Recommender Systems suggest items to a user based on other users past behaviour (items they once bought, viewed or selected and/or ratings they gave to those items). They are very effective in generating meaningful recommendations to a group of users for products or items that might interest them. However, since Collaborative filtering techniques depend on outside sources of information...
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...
We present a new recommender system developed for the Russian interactive radio network FMhost based on a previously proposed model. The underlying model combines a collaborative user-based approach with information from tags of listened tracks in order to match user and radio station profiles. It follows an adaptive online learning strategy based on the user history. We compare the proposed algorithms...
Collaborative filtering has been considerably successful in improving recommender systems both in the literature and commercial applications. Most of the algorithms designed up to now consider users' ratings equally and do not pay attention to the fact that users' interests or requirements might change over the time. In this paper a collaborative filtering based recommender system is designed which...
In this paper we focus on the algorithm for prediction task involves predicting whether or not a user will follow an item that has been recommended to the user in social networking services. Items can be person, organizations or groups, which is sponsored by Ten cent Weibo as KDD Cup 2012. We evaluate a range of different profiling and recommendation strategies, based on a subset of large dataset...
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 rating. To address this problem, active learning methods have been proposed to acquire those ratings from users, that will help most in determining their interests. The optimal...
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...
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