Nowadays, we deal with a large volume of information that we may have wrong choices without appropriate guidance. To this end, Recommender systems are proposed which are a type of information filtering system that acts as a filter and displays information that is useful and close to the user's interests. They reduce the volume of the retrieved information and help users to select relevant products from millions of choices available on the internet. However, since these systems use explicitly and implicitly collected information about the user's interests for different items to predict the user's favorite items, the adversaries due to their openness nature might attack them. Therefore, identifying them is essential to improve the quality of the recommendations. For this purpose, in this paper, a method based on two criteria of a maximum number of users with the equal length and the degree of novelty of their profiles is presented and finally, the DBSCAN clustering algorithm is used to distinguish genuine users from fake users. In order to improve the DBSCAN algorithm, we proposed a new method to determine the values of Eps and MinPts automatically. The results of the proposed method are compared with a new comparative study on shilling detection methods for trustworthy recommendations, which shows that the proposed method independent of the type of attack can identify fake users in most cases with accuracy close to 1.

Trust-based Recommender systems use trust relationships between users to improve the quality of recommendations. One of the most important features of trust is context-dependency. Despite the importance of context-dependency, this feature has been largely neglected in the current literature. In this paper, we propose a new approach that considers the semantic context of items to infer trust relationships between users. In this approach, the level of trust between two users varies depending on different contexts. Therefore, the trustworthy neighbors of an active user will be different for different target items, and these neighbors are determined according to the target context. The focus on context-specific ratings instead of all ratings results in fewer online computations, thus increasing the efficiency of the system as well as the accuracy of recommendations. Experimental results on a real-world data set show the higher accuracy and efficiency of the proposed approach compared to its counterparts.

As science and technology advance rapidly, vast amounts of structured, semi-structured, and unstructured data are generated daily from various sources. This data, produced by diverse users, often exhibits common patterns that can be filtered and analyzed to offer valuable recommendations for products or services that interest these users. Recommender systems emerged in the mid-1990s and gained significant attention following the Netflix Prize. Today, these systems are applied in diverse fields, such as movie recommendations (Netflix), book suggestions (Amazon), and music selections (Spotify). Recommender systems (RS) are software applications and methods created to suggest items that may be valuable or relevant to users. This study aims to identify, evaluate, and synthesize research on the application of Recommender systems in finance. To achieve this objective, we employed the bibliometric method, a robust approach for collecting research data. All relevant articles in this field were initially gathered from the Scopus database. Subsequently, we conducted an analysis using the bibliometrix package in R software to process the collected articles. In this study, we review the historical background of research conducted on Recommender systems, explore their applications in the financial domain, and elaborate on the inputs and outputs of such systems. Additionally, we introduce different Recommender systems and discuss their advantages, disadvantages, and challenges. Finally, we offer suggestions for the implementation of this method. The findings of this research serve as a valuable toolkit to assist researchers in their work within this area of study.