Recommendation systems are widely used by different user service providers specially those who have interactions with the large community of users. This paper introduces a recommender system based on community detection. The recommendation is provided using the local and global similarities between users. The local information is obtained from communities, and the global ones are based on the ratings. Here, a new fuzzy community detection using the personalized PageRank metaphor is introduced. The fuzzy membership values of the users to the communities are utilized to define a similarity measure. The method is evaluated by using two well-known datasets: MovieLens and FilmTrust. The results show that our method outperforms recent recommender systems.
Ghavipour and Meybodi @cite_9 has introduced a fuzzy method in which, the level of users trust to each other is defined as fuzzy. For this purpose, they propose a method to adjust membership functions of fuzzy trust and distrust in recommender systems by using learning automata.
@cite_9
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We propose a learning automata-based method for optimizing membership functions.The proposed method adjusts the number and the position of membership functions.The proposed method can be used without any change in any fuzzy recommender system.The performance of proposed method is tested on well-known datasets.The results show that the proposed method improves the recommendation accuracy. Incorporating trust and distrust information into collaborative recommender systems alleviates data sparsity and cold start problems. Since trust and distrust are a gradual phenomenon, they can be stated more naturally by fuzzy logic. Finding the most appropriate fuzzy sets which cover the domains of trust and distrust is not an easy task. Existing research on fuzzy modelling of trust and distrust has not considered the optimization of membership functions. In this paper, we address this issue and propose a continuous action-set learning automata (CALA)-based method to adjust membership functions of fuzzy trust and distrust during the lifetime of recommender system in terms of recommendation error. By assigning a CALA to the centre parameter of each triangular membership function, the proposed method optimizes the number and the position of fuzzy sets. To the best of our knowledge, this is the first effort in this direction. The experimental results indicate that using the proposed method in fuzzy recommender systems improves the recommendation accuracy. The need for trading off interpretability and accuracy is intrinsic to the use of fuzzy systems. The obtaining of accurate but also human-comprehensible fuzzy systems played a key role in Zadeh and Mamdani's seminal ideas and system identification methodologies. Nevertheless, before the advent of soft computing, accuracy progressively became the main concern of fuzzy model builders, making the resulting fuzzy systems get closer to black-box models such as neural networks. Fortunately, the fuzzy modeling scientific community has come back to its origins by considering design techniques dealing with the interpretability-accuracy tradeoff. In particular, the use of genetic fuzzy systems has been widely extended thanks to their inherent flexibility and their capability to jointly consider different optimization criteria. The current contribution constitutes a review on the most representative genetic fuzzy systems relying on Mamdani-type fuzzy rule-based systems to obtain interpretable linguistic fuzzy models with a good accuracy. People generate information or get it from the others. When one gets information from the others it is important to get it from trusted ones. Each individual in a society can get the information he needs form his trusted friends but there are also many other people in the society that he or she indirectly trusts and can benefit from their information. The idea of benefiting from the indirectly trusted people can well be employed in social networks where finding trusted people can be automated. There should be a feature for users to specify how much they trust a friend and a mechanism to infer the trust in the society trust graph from one user to another that is not directly a friend of the user so that a recommender site can benefit from these inferred trust ratings for showing trustworthy information to each user from her or his point of view from not only her or his directly trusted friends but also the other indirectly trusted users. A problem that is faced in inference in such a large network is contradictory information. This work suggests using fuzzy linguistic terms to specify trust to other users and proposes an algorithm for inferring trust from a person to another person that may be not directly connected in the trust graph of a social network. The algorithm is implemented and compared to the previous one that models trust as numbers in a range. While according to the imprecise nature of trust concept, writing and reading a linguistic expression for trust is much more natural than a number for users, the fuzzy composing strategy performs better than the previous algorithm in integrating conflicting beliefs and conveying the conflict in the inferred result. In this paper, we present a clique-based method for mining fuzzy graph patterns of money laundering and financing terrorism. The method will contribute to a new generation of intelligent anti-money laundering systems that incorporate comprehensive information from various information sources as well as from human subject matter experts. A fuzzy degree of confidence can therefore be associated to each relation between any two actors in a graph of transactions. We reduce the problem of fuzzy subgraph isomorphism to the problem of finding a fuzzy set of maximal cliques in a fuzzy extension of a mathematical construct that is similar to Vizing's modular product of graphs.