## 1 Introduction  
The excessive increase of data on various web applications has made the process of extracting useful information very difficult because of the information overload. Various information retrieval techniques are proposed to handle this overwhelming amount of data [1]. Collaborative filtering (CF) has become the vital tool of information retrieval, due to its efficiency and simplicity. Memory-based (MeB) CF is the most prevalent approach to predict the interests of a user automatically and more precisely. For that, it collects preferences or tastes findings from an extensive number of users. Given a set of users' likes or dislikes about different movies, a CF-based recommendation system (RS) for movie preferences could infer assumptions regarding which movie a user would like [2, 3]. MeB CF utilizes a user-item rating dataset to determine how similar users or items are. An example of MeB CF is item-based and user-based Top-N recommendations. However, in practise, consumers typically assess a small number of things, making the datasets used in many commercial RS sparse. In a sparse circumstance where there are fewer co-rated items, prevalent similarity metrics/measures (SeMs) have significant accuracy problems. Only a few studies address the aforementioned problem, despite the fact that numerous works have been done to increase the CF's accuracy in sparse datasets. [PERSON] et al. proposed a novel SeM to address the above issue by exploiting the Bhattacharyya coefficient (BC) [4]. When calculating similarity, the ratings produced by the two users are taken into account. The final similarity value is calculated using both local and global information. However, this approach failed to compute the similarity between two disjoint rating vectors, as Jaccard similarity in such cases will be zero. The following table explains the conditions when SeM using BC is also unable to determine the similarity value between users as with in similar fashion. Table 1 signifies the rating information of 3 users on six movies, whereas all related genres of each movie are represented in Table 2. As mentioned in Table 1, all widely used SeMs are unable to find the similarity between users in such a situation. Furthermore, categorical attributes of the item, suggested by [PERSON] et al., have been used to obtain the similarity [5]. The performance of CF using categorical attributes can be improved by using a modified prediction approach. The categorical attributes of items are utilized in the proposed methodology since they naturally stimulate users' interest [6; 7; 8]. A user's bias for a certain attribute of an item is possible. For instance, if a user gave an action movie a rating 'of 5', we can infer from the categorical attributes that users are interested in an action movie. Therefore, we consider a reasonable approach to alleviate the aforementioned issue. The proposed methodology is based on the following assumptions: (i) If two users' tastes/rating patterns are identical, they will enjoy items with same features, (ii) a user may like the product liked by the _'most'_ similar neighbor. The motivation behind the above assumptions is twofold: (i) it does not require a co-rated item to obtain the closest neighbors and (ii) the prediction result will be more accurate in a situation where the similarity value of Top-N neighbors varies significantly. The important contributions to this manuscript are listed below. * Utilizing the item's categorical features, a modified similarity measure has been employed. * The most similar neighbor is used to predict the rating in the prediction approach. * The comparison between the proposed method utilizing the modified similarity measure and prediction approach and the traditional collaborative filtering algorithms has been done based on MAE and RMSE. The remaining structure of this paper is as follows: The Background and Literature Survey is represented in Section 2, where we provide the background of RS and some prevalent works of CF-based RS.