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Recommender systems have been accompanied by many applications in both academia and industry. Among different algorithms used to construct a recommender system, collaborative filtering methods have attracted much attention and been used in many commercial applications. Incorporating the time into the recommendation algorithm can greatly enhance its performance. In this paper, we propose a novel time-aware model-based recommendation system. We show that future ratings of a user can be inferred from his/her rating history. We assume that there is cascade of information between the items such that rating an item can lead to other items being rated. There is indeed a hidden network structure among the items and each user tracks a sequence of items in this network. The dependencies between the items are modeled based on statistical diffusion models and the parameters are obtained through maximum-likelihood estimation. We show that under some mild assumptions, the estimation task becomes a convex optimization problem. A major advantage of the proposed method over classical recommender systems is the ability to include novel items in its recommendation lists besides providing accurate recommendations. The proposed model also results in personalized and diverse recommendations. Experimental evaluations show that the model can be trained based on the ratings of a limited number of users. Furthermore, the proposed model outperforms classical recommendation algorithms in terms of both accuracy and novelty. [ABSTRACT FROM AUTHOR]

Recommender systems are in the center of network science, and they are becoming increasingly important in individual businesses for providing efficient, personalized services and products to users. Previous research in the field of recommendation systems focused on improving the precision of the system through designing more accurate recommendation lists. Recently, the community has been paying attention to diversity and novelty of recommendation lists as key characteristics of modern recommender systems. In many cases, novelty and precision do not go hand in hand, and the accuracy--novelty dilemma is one of the challenging problems in recommender systems, which needs efforts in making a trade-off between them. In this work, we propose an algorithm for providing novel and accurate recommendation to users. We consider the standard definition of accuracy and an effective self-information--based measure to assess novelty of the recommendation list. The proposed algorithm is based on item popularity, which is defined as the number of votes received in a certain time interval. Wavelet transform is used for analyzing popularity time series and forecasting their trend in future timesteps. We introduce two filtering algorithms based on the information extracted from analyzing popularity time series of the items. The popularity-based filtering algorithm gives a higher chance to items that are predicted to be popular in future timesteps. The other algorithm, denoted as a novelty and population-based filtering algorithm, is to move toward items with low popularity in past timesteps that are predicted to become popular in the future. The introduced filters can be applied as adds-on to any recommendation algorithm. In this article, we use the proposed algorithms to improve the performance of classic recommenders, including item-based collaborative filtering and Markov-based recommender systems. The experiments show that the algorithms could significantly improve both the accuracy and effective novelty of the classic recommenders. [ABSTRACT FROM AUTHOR]