## 2 Background and Related Work
### Traditional IBCF Approach and the Associated CNICS Problem  
First, we discuss some RS-related notations used in this paper. Given an RS, let \(U\) and \(I\) be finite sets of users and items, respectively. \(R\cup\{\ast\}\) denotes the set of possible item rating scores, the absence of a rating is indicated by an asterisk (\(\star\)). The rating score of a user \(u\in U\) for item \(i\in I\) is denoted by \(r_{u,i}\in R\cup\{\ast\}\). \(\theta\) is set as the threshold for rating scores, and items with \(r_{u,i}\geq\theta\) are regarded as items that are relevant to a user \(u\). Item attribute set is defined as \(AT=\{at_{1},at_{2},\ldots,at_{l}\}\), and \(AV=\{av_{1},av_{2},\ldots,av_{l}\}\) refers to a set of attribute values, where \(l\) is the number of attributes, and \(at_{l}(x)=av_{l}(x)\) means that the value of attribute \(at_{l}\) on item \(i_{x}\) is \(av_{l}\). The IBCF approach was first proposed by Sarwar [7]. It is one of the most successful approaches and has been employed in famous online marts such as Amazon Inc. To predict the rating score for the target item \(i_{it}\) in which the target user \(u_{tu}\) has not rated, first, the IBCF computes similarity between target item \(i_{ti}\) and all other items to identify the neighborhood that has most similar items. Afterward, a rating score is computed for target item \(i_{ti}\) using the ratings of the neighborhood made by the target user \(u_{tu}\) (Equation (1)). \[P_{tu,ti}=\frac{\sum_{q\in N_{ti}(L)\cap I_{tu}}Sim(ti,q)\ast r_{tu,d}}{\sum_{q \in N_{ti}(L)\cap I_{tu}}[Sim(ti,q)]}. \tag{1}\]  
Figure 1: The flow chart of proposed approach. Our approach comprises the following main phases: 1 extract item attribute matrix; 2 construct interrelated attributes by extending the given binary relations between each pair of attributes; 3 form item interrelated attribute matrix; 4 compute item-item similarity; 5 predict rating scores and recommend top \(N\) items to the target user. Here, \(P_{tu,ti}\) denotes the predicted rating made by the target user \(u_{tu}\) for the target item \(i_{ti}\), \(I_{tu}=\{i_{x}\in I|r_{tu,x}\
eq*\}\) represents the set of items rated by target user \(u_{tu}\), \(N_{li}(L)\) denotes the neighborhood of the target item \(i_{ti}\) comprising most \(L\) similar items. \(Sim(ti,q)\) denotes the similarity value between target item \(i_{ti}\) and item \(i_{q}\). After predicting the rating score for each un-rated item of the target user \(u_{tu}\). IBCF sorts all un-rated items in descending order with respect to the predicted rating scores, and the top \(N\) items are selected as recommendation for target user \(u_{tu}\). To obtain recommendations with satisfactory accuracy and diversity, the classical IBCF approach requires sufficient number of ratings from users on items [7]; however, if a new item just enters into the RSs, because it has not yet been evaluated by any user, it is crucial for the classical IBCF approach to produce high-quality recommendations for the new item, i.e., the traditional IBCF is susceptible to the CNICS problem. Presently, many approaches have been presented for solving the CNICS problem of IBCF. Gantner et al. [21] presented a framework for mapping item attributes to latent features of a matrix factorization model. The framework is applicable to both user and item attributes and can handle both binary and real-valued attributes. Kula [22] proposed a hybrid matrix factorization model which represents users and items as linear combinations of their content features' latent factors. Mantrach et al. [23] proposed a matrix factorization model that exploits items' properties and past user preferences while enforcing the manifold structure exhibited by the collective embedding. Sahoo et al. [24] showed that after forming a good/bad impression on one attribute of items, users will apply this impression to other items, so, item attribute information is often used to compute similarity among items.