## 2 Related Work  
RSs use filtering methods to acquire information on entities (i.e., users or items). Therefore, the researchers provided many similarity measures for CF-RSs to establish a recommendation's accuracy at the necessary level. Utilizing entity-side information is a frequently used technique for resolving RS issues and obtaining appropriate recommen  
Figure 1: Example of user preferences for movie genres. dations [22]. In the following, we summarize some of the studies that used entities' side information to enhance RS accuracy. In [25], the authors used location information in recommendation algorithms to create a mobile learning application focused on presenting users with favorable recommendations. At the same time, the authors of [29] planned differently to increase the accuracy of RSs. They suggest approach that based on the addition of contextual information to establish relationships between users or products. As a result, they suggested many mathematical methods for information filtering and modeling, which demonstrated through experimental results that contextual information is critical for receiving correct recommendations. Following this, item genres were incorporated into an RS to increase the RS's accuracy. Several studies have concentrated on utilizing the movie genre feature to collect user input and enhance suggestions, particularly for massive datasets [30; 31]. Others improved an RS called \"Taste Weights\" [32]; analyzing a user's Facebook profile identified their preferred music genre. Then, the semantic web resource \"DBedia\" was utilized to discover new artists performing music in the same genre as the active user's favorite. Such music was recommended to other users. The authors of [33] presented a music recommendation system that uses users' behavioral features in the model and exploits a user's overall mood and sentiment to produce recommendations. In addition, they blended CB recommendations with user personality qualities on social networking sites, behavior, interests, and requirements and examined several methods for inferring user personality qualities. In [28], a genre-based algorithm was developed using the notion of item genres rather than the items themselves. The algorithm picked neighbors based on their genre preference, comparable to those of others in the community. This research used user data to determine item similarity, and CF sparsity was alleviated. In [34], a clustering method using an item's genre information was presented. This model focuses on establishing a hybrid approach, combining the CB clustering method and CF, instituted by the item genre's investment. The model improved the rating prediction operation with a less time-consuming model. Differently, in [35], the authors introduced a user similarity model based on genre preferences to present a weighted link prediction model according to complex network modeling and a detected user community. Researchers have continued to exploit item genres to create a recommender system; in [36], novel user-based similarity measures based on a vector to hold user similarity using item genres was presented. The model finds the global, local, and meta similarities between users to create a similarity vector and reveals and estimates user associations under different scenarios. They emphasized that user similarities might be defined differently depending on the various item genres. The work in [37] focused on employing item genres to raise predicted rating accuracies and overcome the sparsity problem. The algorithm involves creating a matrix containing the user's item weight by combining two vectors. The first vector is the user's taste vector, extracted from the user's ratings for items. The second vector contains the degree of items belonging to a specific type, which results from the exploitation of the collective ratings of a particular item. Finally, the resulting matrix was used to get the prediction of the desired item.