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Collaborative Filtering (CF) approaches have been widely used in various applications of recommender systems. These methods are based on estimating the similarity between users/items by analyzing the ratings provided by users. The existing methods are often domain-specific and have not considered the time of the ratings being made in the calculation of the similarity. However, users’ preferences vary over time, and so their similarity. In this paper, a novel method is proposed by re-ranking the users/items neighborhood set considering their future similarity trend. The trend of similarity is predicted, and depending on increased/decreased trend, we update the final nearest neighbor sets that are used in CF formulation. This method can be applied on a broad range of CF methods that are based on similarities between users and/or items. We apply the proposed approach on a set of CF algorithms over two benchmark datasets and show that the proposed approach significantly improves the performance of the original CF recommenders. As the proposed method only re-ranks the neighborhood set, it can be applied to any existing non-temporal similarity-based CF recommenders to improve their performance.

Recommender systems use intelligent algorithms to learn a user’s preferences and provide them relevant suggestions. Lack of sufficient ratings – also known as data sparsity problem – often results in poor recommendation performance. The existing recommendation methods have mainly focused on designing recommenders with high accuracy without paying much attention to the reliability of the recommendations. On the other hand, the users’ preferences may vary over time and considering the time factor in the design process is crucial, which has been largely ignored in most of the existing recommenders. To deal with these issues, a novel recommendation method is proposed in this paper which incorporates temporal reliability and confidence measures into the recommendation process. First, the effectiveness of the users’ rating is measured using a probabilistic approach and ineffective rating profiles are enriched by adding some implicit ratings to them. The quality of the predictions is evaluated using a temporal reliability measure taking into account the changes of users’ preferences over time. Then, the ratings with low reliability values are recalculated using a novel procedure, which updates the target user’s neighborhood by removing ineffective users. This leads to a temporal confidence measure that is used to update the neighborhood to provide more reliable and accurate recommendations. The superiority of the proposed method over state-of-the-art recommendation methods is shown by conducting extensive experiments on three benchmark datasets.

Over the last few years, with significant growth of information on the web, users are swamped with a huge amount of information. Recommender system (RS) is an information retrieval technology that aims to provide relevant items to users by considering their preferences. Although several studies in the literature have proposed new RSs to improve the precision of recommendations, some studies have shown that increasing the diversity of recommendations is one way to increase the quality of the user’s experience and to solve the over-fitting problem. While some previous works have tried to make a trade-off between precision and diversity, they could not provide a linear and deterministic trade-off between precision and diversity. As such, these methods are either impractical or infeasible in a real RS. In this paper, we propose a new method to recommend items which are fit to users’ preferences. The method consists of following steps: i) finding users’ preferences using a limited user study, ii) proposing a probabilistic graph-based recommender system (PGBRS) to recommend items with a desirable level of precision and diversity by considering items that are likely to be preferred by the user in the future. PGBRS is a dataset-independent method which is significantly flexible to update with a low implementation complexity.

The last two decades have seen a surge of data on the Web which causes overwhelming users with huge amount of information. Recommender systems (RSs) help users to efficiently find desirable items among a pool of items. RSs often rely on collaborating filtering (CF), where history of transactions are analyzed in order to recommend items. High accuracy, and low time and implementation complexity are most important factors for evaluating the performance algorithms which current methods have the shortage of all or some of them. In this paper, a probabilistic graph-based recommender system (PGB) is proposed based on graph theory and Markov chain with improved accuracy and low complexity. In the proposed method, selecting each item for recommendation is conditioned by considering recommended items in the previous steps. This approach uses a probabilistic model to consider the items which are likely to be preferred by users in the future. Experimental results performed on two real-world datasets including Movielens and Jester, demonstrate that the proposed method significantly outperforms several traditional and state-of-the-art recommender systems.

Recommender systems have significant applications in both industry and academia. Neighbourhood-based collaborative Filtering methods are the most widely used recommenders in industrial applications. These algorithms utilize preferences of similar users to provide suggestions for a target user. Users’ preferences often vary over time and many traditional collaborative filtering algorithms fail to consider this important issue. In this paper, a novel recommendation method is proposed based on predicting similarity between users in the future and forecasting their similarity trends over time. The proposed method uses the sequence of users’ ratings to predict the similarities between users in the future and use the predicted similarities instead of the original ones to detect users’ neighbours. Experimental results on benchmark datasets show that the proposed method significantly outperforms classical and state-of-the-art recommendation methods.

Recommender systems attempt to suggest information that is of potential interest to users helping them to quickly find information relevant to them. In addition to historical user–item interaction data, such as users’ ratings on items, social recommendation methods use social relationships between users to improve the accuracy of recommendations. However, the available social relationships are often extremely sparse. Therefore, incorporating implicit relationships into the recommendation process can be effective to improve the performance of social recommender systems, especially for those users whose explicit relationships are insufficient to make accurate recommendations. The existing approaches have not considered reliability of the implicit relationships. In this paper, a social recommender system is proposed based on reliable implicit relationships. To this end, Dempster–Shafer theory is used as a powerful mathematical tool to calculate the implicit relationships. Moreover, a new measure is introduced to evaluate the reliability of predictions, where unreliable predictions are recalculated using a neighborhood improvement mechanism. This mechanism uses a confidence measure between the users to identify ineffective users in the neighborhood set of a target user. Finally, new reliable ratings are calculated by removing the identified ineffective neighbors. Extensive experiments are conducted on three well-known datasets, and the results demonstrate that our approach achieves superior performance to the state-of-the-art recommendation methods.

Recommender systems aim to suggest relevant items to users among a large number of available items. They have been successfully applied in various industries, such as e-commerce, education and digital health. On the other hand, clustering approaches can help the recommender systems to group users into appropriate clusters, which are considered as neighborhoods in prediction process. Although it is a fact that preferences of users vary over time, traditional clustering approaches fail to consider this important factor. To address this problem, a social recommender system is proposed in this paper, which is based on a temporal clustering approach. Specifically, the temporal information of ratings provided by users on items and also social information among the users are considered in the proposed method. Experimental results on a benchmark dataset show that the quality of recommendations based on the proposed method is significantly higher than the state-of-the-art methods in terms of both accuracy and coverage metrics.