# Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems
## 1 Introduction  
The advent of the Internet has exacerbated the information overload problem, which increasingly complicates the retrieval of useful information from the Internet. A recommender system (RS) [1; 2; 3] is a powerful tool for solving the information overload problem in many online applications, and the use of RS has steadily increased in recent years. Typically, an RS is designed to recommend proper items to proper users based on their ratings or on their online behavior such as purchasing behavior [2]. Current RS methods can be classified as content-based filtering methods [4; 5] and collaborative filtering methods [6; 7; 8]. Content-based RSs generate recommendations after analyzing item descriptions and user interest profiles, whereas collaborative filtering RSs recommend items that are of particular interest to the active user by identifying similarities between users or between items. Collaborative filtering is among the most effective and widely used RS methods in many domains due to its easy implementation, e.g., it does not require specific domain knowledge. The major task of collaborative filtering is to compute the distances between users or between items based on how users have interacted with the system in the past [2; 3]. Since users or new users rarely interact extensively in the system, calculating the precise distances and similarities betweenusers or items is difficult however because user ratings and user feedback are limited. This problem is known as the \"cold start\" problem, or the \"data sparsity\" problem [8; 9; 10; 11]. Most collaborative filtering RSs substantially reduce performance in a cold start scenario. A common solution for the cold start problem is to consider additional information sources. For example, recommendation performance can be improved by considering information about the social relations of users, such as their social network friendships [12], or by considering the context of information [13]. For example, in an E-commerce scenario, the purchasing decisions made by users are usually affected by the opinions of their friends. Proposed methods of using information about social relationships to improve the prediction accuracy of an RS and to alleviate the cold start problem include SoRec [14], RSTE [15], SoReg [16], and TrustSVD [17]. The objective of these methods is to identify users who are representative of the actual user because of their implicit social relationships with the actual user. Previous studies of RS design [18; 19; 20; 21; 22; 23] have focused on using rating information to improve prediction accuracy. However, this approach can limit recommendation results and introduce the long tail recommendation problem [24; 25]. For example, if a movie RS is initially used to get recommendations for comedy movies, the RS may tend to continue recommending the same genre of movies in the future. Thus, the user may soon begin to disregard the recommendations. A recommendation list that has high diversity, markedly increases the interest of users [26]. Therefore, an effective RS requires not only high accuracy, but also high diversity. To enhance the diversity of a recommendation list, researchers have attempted to develop methods of maximizing the range of interests considered by the RS [27]. Since increasing diversity reduces prediction accuracy, the optimal trade-off [28; 29] between diversity and accuracy must be determined. To address the above problems, this study proposes a novel diversity optimization method: Diversity Balancing for Two-Stage collaborative filtering (DBTS). In the first stage of the filtering process, DBTS uses both the social relationships of the user and user-based recommendations to predict missing ratings. In the second stage, the rating recommendation list is generated by leveraging user interests and social influences.