## 1 Introduction  
Due to the growth of websites and e-commerce sites, online users and items have increased substantially. As a competitive operation, e-commerce requires obtaining solutions that may help to improve sales, competitive advantages, and customer satisfaction. Recommendation systems (RS) aid these marketplaces by providing customers with recommendations based on their prior preferences. As a result, recommendation algorithms are commonly coupled with various domains of knowledge [1]. Based on earlier studies, RSs can be classified into the following types [2]: collaborative filtering (CF), content-based filtering (CB), demographic filtering (DF), knowledge-based filtering (KB), and hybrid. The most common methods for recommending items to users are based on CF [3]. Typically, CF algorithms are categorized into user-based and item-based models [4]. In the user-based CF algorithm, recommender systems collect similar users into groups and recommend highly-rated items to similar users [5]. On the other hand, in the item-based CF system, the similarity between objects is determined based on the items' ratings. Then, groups of similar items can be created. Finally, if a user rated a specific item highly, similar items might be recommended [6]. Despite numerous studies and analyses on the user-user and item-item CF similarity measures, these measures do not infer sufficient similarity in some cases. Traditional CF algorithms offer recommendations only user ratings for items, regardless of the effects that many features on user similarity [7] and item similarity [8]. Therefore, it is essential to find a similarity measure based on the actual preferences of users rather than user rating values. Therefore, researchers are considering the effect of item characteristics on the recommendation accuracy of RSs [9; 10; 11]. A model that mixes item-based CF information content was introduced in [12]. The authors created a clustering method based on mixed information. The \(k\)-mean clustering model and weighted deviations were used to determine the closest neighbors, and then the rating prediction for unrated items was calculated. In [13], the authors formed a special RS by analyzing the item genre relationship and user-preferred genres. The Pearson correlation coefficient (PCC) and clustering approaches were used to calculate genre similarity. Then, the model can suggest a recommended genre to an active user. However, CF algorithms suffer from several drawbacks, and the common ones are data sparsity and user cold-start. Data sparsity intimates an insufficient number of ratings for items from users; sparseness in the <user x item> matrix, limiting the CF algorithm's ability to pick a suitable set of similar users [14; 15; 16]. In parallel, the cold-start problem is a significant issue in recommender systems; this problem is divided into cold users and cold items. The problem with cold users occurs due to a lack of information about the user's preferences [15; 17]. Depending on the CF, a user cannot receive any recommendations from the system [18; 19]. An increased number of new users and multiple less-active users joining e-commerce sites in all applications cause real problems for the existing recommendation algorithms [20]. At the same time, the problem with cold items occurs from the lack of ratings for a new item [15; 17]. Acceptable recommendation quality cannot be obtained when little or no information is available [21]. Recent literature indicates that many RS problems have been approached through various hybrid methods, including side information to determine user interests and purchasing habits [22]. A previous study [23] presented a method based on exploiting collaborative tagging as supporting information to extract users' tastes for items. Their research overcame the user cold-start and sparsity problems.