Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> Problem Definition of Recommender Systems <s> Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the basis for collaborative filtering (CF) techniques, the dominant framework for recommender systems. Currently, new recommendation scenarios are emerging that offer promising new information that goes beyond the U-I matrix. This information can be divided into two categories related to its source: rich side information concerning users and items, and interaction information associated with the interplay of users and items. In this survey, we summarize and analyze recommendation scenarios involving information sources and the CF algorithms that have been recently developed to address them. We provide a comprehensive introduction to a large body of research, more than 200 key references, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. On the basis of this material, we identify and discuss what we see as the central challenges lying ahead for recommender system technology, both in terms of extensions of existing techniques as well as of the integration of techniques and technologies drawn from other research areas. <s> BIB001 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> Problem Definition of Recommender Systems <s> Abstract On the Internet, where the number of choices is overwhelming, there is need to filter, prioritize and efficiently deliver relevant information in order to alleviate the problem of information overload, which has created a potential problem to many Internet users. Recommender systems solve this problem by searching through large volume of dynamically generated information to provide users with personalized content and services. This paper explores the different characteristics and potentials of different prediction techniques in recommendation systems in order to serve as a compass for research and practice in the field of recommendation systems. <s> BIB002
Recommender systems act as skilled agents to assist users to conquer information overload while making selection decisions over items by providing customized recommendations. Users and items are general phrases denoting, respectively, entities actively browsing and making choices and entities being selected, such as goods and services. Formally, recommender systems leverage one or more of the following three information sources to discover user preferences and generate recommendations: user-item interactions, side information, and contexts. User-item interactions, or ratings, are collected explicitly by prompting users to provide numerical feedback on items and are acquired implicitly by tracking user behaviors such as clicks, browsing time, or purchase history. The data are commonly represented as a matrix that encodes the preferences of users and is naturally sparse, since users normally interact with a limited fraction of items. Side information is rich information attached to an individual user or item that depicts user characteristics such as education and job or item properties such as description and product categories. Side information can take diverse structures with rich meaning, ranging over numerical status, texts, and images to videos, locations, and networks. On the other hand, context refers to all the information collected when a user interacts with an item, such as timestamps, locations, or textual reviews. This contextual information usually serves as an additional information source appended to the user-item interaction matrix. The goal of recommender systems is to disclose unknown user preferences over items that users never interact with and recommend the most preferred items to them. In practice, recommender systems learn to generate recommendations based on three types of approaches: pointwise, pairwise, and listwise. The pointwise approach is the most common approach and demands recommendation systems to provide accurate numerical predictions on observed ratings. Items that a user never interacts with are then sorted by their rating predictions, and a number of items with the highest ratings are recommended to the user. On the other hand, a pairwise approach seeks to preserve the ordering of any pair of items based on ratings, while in the listwise approach, recommender systems aim to preserve the relative order of all rated items as a list for each user. The pairwise approach and listwise approach are together considered item rankings that only require recommender systems to output the ordering of items but not ratings for individual items. In most of the works covered in this paper, the task of attribute-aware recommendation is defined as predicting unknown ratings, that is: given N u users, N i items, a user-item rating matrix R ∈ R N u ×N i with only a small portion N r ratings observed (i.e., there are a total of N u × N i − N r missing ratings), side information of users X ∈ R K X ×N u (assuming each user has K X attributes), side information of items Y ∈ R K Y ×N i , and contexts Z ∈ R K Z ×N r , the goal is to build a model that is capable of predicting each of the unknown ratings in R. Then, given any specific user, a recommender system can make recommendations based on the predicted ratings. Normally, items with higher ratings are recommended first. Note that the dimension K X , K Y of side information attribute matrix X, Y might be zero, denoting that there is no side information about users or items. Likewise, if there is no contextual information about user-item interactions, K Z will be zero. The core techniques or algorithms for realizing recommender systems are generally classified into three categories: contentbased filtering, collaborative filtering, and hybrid filtering (Bobadilla et al., 2013; BIB001 BIB002 . Content-based filtering generates recommendations based on properties of items and user-item interactions. Content-based techniques exploit domain knowledge and seek to transform item properties in raw attribute structures such as texts, images, or locations into numerical item profiles. Each item is represented as a vector, and the matrix of side information of items Y is constructed. A representation of each user is then created by aggregating profiles of items that this user interacted with, and a similarity measure is leveraged to retrieve a number of the most similar items as recommendations. Note that contentbased filtering does not require information from any other user to make recommendations. Collaborative filtering strives to identify a group of users with similar preferences based on past user-item interactions and recommends items preferred by these users. Since discovering users with common preferences is generally based on user-item ratings R, collaborative filtering becomes the first choice when item properties are inadequate in describing their content, such as movies or songs. Hybrid filtering is the extension or combination of content-based and collaborative filtering. Examples of this are building an ensemble of the two techniques, using the item rating history of collaborative filtering as part of the item profiles for contentbased filtering or extending collaborative filtering to incorporate user characteristics X or item properties Y. This review focuses on attribute-aware recommender systems that shed light not only on user-item interactions R but also on the side information of users or items X, Y and contexts Z, which is a subset of hybrid filtering.