## 1 Introduction  
With the rapid development of the Internet, data and information are being generated at an unprecedented rate. People cannot acquire useful information quickly and accurately from the huge amount of data around them. Further, the recommender system (RS) can provide personalized items for various types of users, and help users get useful information from large amounts of data. Hence, RSs have great commercial values and research potential and play a significant role in many practical applications and services [1; 2; 3; 4; 5; 6]. Collaborative filtering (CF) is one of the most successful approaches often used in implementing RSs [7; 8; 9; 10]. Item-based CF (IBCF) approach is a type of CF which supposes that an item will be preferred by a user if the item is similar to the one preferred by the user in the past [11]. IBCF is efficient and easy to implement and has good scalability, hence, it is widely applied in modern RSs, e.g., Amazon and Netflix [12]. One of the main problems of IBCF is the new item cold-start (NICS) problem [12; 13; 14; 15]. NICS is very common in practical RSs, i.e., hundreds of new items are introduced in modern RSs every day. Generally, NICS can be divided into complete new item cold-start (CNICS) where no rating record is available (e.g., \(i_{5}\) and \(i_{6}\) in Table 1), and incomplete new item cold-start (INICS) where only a small number of rating records are available (e.g., \(i_{3}\) and \(i_{4}\) in Table 1). In this paper, we focus on the problem of producing effective recommendations for new items with no ratings, i.e., CNICS problem. Traditional IBCF suffers from CNICS problem as it relies on previous ratings of the users. In addition, to generate effective recommendations, traditional IBCF requires that items are rated by a sufficient number of users. To solve CNICS problem of IBCF, a simple approach is to randomly present new items to the users in order to gather rating information about those new items. However, this approach tends to achieve low accuracy. On the other hand, additional information about items such as item profiles, tags, and keywords are used in related effective approaches [15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. However, some special additional information is often incomplete or unavailable. In addition, items in practical RSs often have a low amount of profile information (e.g., item attributes), but specific features of new items are difficult to be extracted using limited additional information. Therefore, researchers face difficulty in utilizing only easily obtained and a limited number of additional information in solving the CNICS problem of IBCF. In this paper, we propose a niche approach which applies interrelationship mining theory to the IBCF approach in order to address the CNICS problem of IBCF. Figure 1 indicates the flow chart of the proposed approach. The proposed approach utilizes interrelationship mining technique to extract new binary relations between item attributes and subsequently construct interrelated attributes according to the original item-attribute matrix. Afterward, the similarity of items is computed with respect to the interrelated attribute information. Next, items with the highest similarity will be selected to comprise the neighborhood of a new item, and rating information of the neighborhood will be used to predict rating score for the new item. Finally, items with the highest predicted rating scores will be recommended to a target user. Experimental results obtained show that our new approach can ameliorate the CNICS problem, and present new item recommendation with better accuracy and diversity at the same time. The academic contributions of this paper can be summarized as follows:  
* Different from related approaches that need special information which is often difficult to obtain [15; 16; 17; 18; 19; 20; 21; 22; 23; 24], the proposed approach can only utilize easily accessible and a limited number of additional information (e.g., item attributes) in solving the CNICS problem of IBCF.