## 2 Background and Literature Survey  
### Recommendation Systems  
[PERSON] et al. throw light on CF-based RS in 1992 [9]. Several RS approaches have emerged due to the inspiration provided by their recent practical implementation. These techniques have often been divided into four broad categories:  
* Based on the presumption that people with comparable physical and personal traits, such as age, geography, gender, etc., possibly have common interests, the demographic filtering technique gives suggestions to the active user [10]. * An item is suggested and recommended to a user in the content-based filtering approach by examining the specifications of the item that they have previously selected [11; 12]. These systems do not care about the ratings customers offer the products. * The recommendations in the CF approach are based on the items' user ratings [11; 13]. In order to estimate user ratings for unrated products, users having similar ratings are used. * The benefits of collaborative filtered approach and content based are combined in the hybrid filtering technique to address the issues with overspecialization, sparsity, cold start [14; 15], and other issues in MeB CF [16]. CF, the most commonly used RS approach, can be categorized into neighbor-hood-based (memory-based) CF and model-based CF. In neighborhood-based CF, a user-item rating matrix is utilized to compute co-related users and rating predictions. In model-based CF approaches, a user-item rating database generates a learning, or statistical model [17]. Subsequently, it is used for predictions and does not require whole rating data when the model is completely built [1; 18]. Some approaches of model-based CF are more efficient than neighborhood-based CF in the prediction of rating [19; 20]. Neighborhood-based CF is a popular strategy in the e-commerce industry because: (i) It is easy to use, intuitive, and doesn't require any training [21]. (ii) It uses one parameter, namely the neighborhood's K-number, whereas a model-based approach requires various factors, including regularisation and learning parameters, etc. [22]. Related Work of CF-Based ReS  
Traditionally SeMs, including the Pearson correlation coefficient, and cosine similarity, are vital measures to calculate user similarities [23; 24]. Numerous SeMs variations are introduced to enhance the functionality of current CF-based RS. Examples include Constrained Pearson Correlation Coefficient and Adjusted Cosine similarity [25]. However, they are restricted by a sparse dataset that includes few or no co-rated items [26]. For the said metrics, the other drawback that can be identified includes frequently revealing high user similarity when there are fewer items [27]. Additionally, these computations exclude all user ratings for the specific pair, which are taken into consideration by the Jaccard SeM. Hence, it is affected with a few co-rated item problems when the dataset is highly sparse. To resolve the aforementioned problem and enhance the functionality of CF-based RS, different SeMs have been proposed in the literature. Among these traditional SeMs, PIP is the prominent favored measure in RS. The three key factors are popularity, impact, and proximity within two rating patterns, used in PIP measure [28]. The proximity factor is computed as the intra-arithmetic difference between two rating patterns of an item. The depiction of the impact factor indicates the strongness of how a preference is held or disliked by a user, whereas a rating is given importance by the popularity factor. To address the drawbacks of the conventional SeMs, [PERSON] et al. presented some of SeMs [29; 30]. They integrated Jaccard and Mean squared difference in their proposed technique (JMSD) [24]. They demonstrated that the JMSD-based CF surpassed the PIP-based CF on the basis of mean absolute error. A similar method, known as the Cosine-Jaccard-Mean Measure of Divergence (CJacMMD), has been produced by [PERSON] al. It combines the Mean Measure of Divergence, Jaccard, and Cosine [31].