Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Traditional recommendation approaches do not consider the changes of user preferences according to context. As a result, these approaches consider the user’s overall preferences, although the user preferences on items varies according to his/her context. However, in our context-aware approach, we take into account not only user preferences, but also context information. Our approach can be easily adopted for content-based and collaborative filtering based recommendations. To exploit raw context information in recommendation, we abstract the raw context information to a concept level. Moreover, by aggregating the context information, we can improve the quality of recommendation. The results of several experiments show that our method is more precise than the traditional recommendation approaches. <s> BIB001 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> It has been recognized that recommendation system is a very important and indispensable topic in E-commerce. Many famous E-commerce websites utilize recommendation systems to convert browsers into buyers. The forms of recommendation include suggesting products/services to the customer, providing personalized product/service information, summarizing community opinion, and providing community critiques. Personalized recommendation methods are mainly classified into content-based recommendation approach and collaborative filtering recommendation approach. Both recommendation approaches, however, have their own drawbacks. This study proposes the integrated contextual information as the foundation concept of multidimensional recommendation model, and uses the online analytical processing (OLAP) ability of data warehousing to solve the contradicting problems among hierarchy ratings. The evaluation studies show that by establishing additional customer profiles and using multidimensional analyses to find the key factors affecting customer perceptions, the proposed approach increases the recommendation quality. <s> BIB002 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Matrix factorization is a fundamental technique in machine learning that is applicable to collaborative filtering, information retrieval and many other areas. In collaborative filtering and many other tasks, the objective is to fill in missing elements of a sparse data matrix. One of the biggest challenges in this case is filling in a column or row of the matrix with very few observations. In this paper we introduce a Bayesian matrix factorization model that performs regression against side information known about the data in addition to the observations. The side information helps by adding observed entries to the factored matrices. We also introduce a nonpara-metric mixture model for the prior of the rows and columns of the factored matrices that gives a different regularization for each latent class. Besides providing a richer prior, the posterior distribution of mixture assignments reveals the latent classes. Using Gibbs sampling for inference, we apply our model to the Netflix Prize problem of predicting movie ratings given an incomplete user-movie ratings matrix. Incorporating rating information with gathered metadata information, our Bayesian approach outperforms other matrix factorization techniques even when using fewer dimensions. <s> BIB003 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Context has been recognized as an important factor to consider in personalized Recommender Systems. However, most model-based Collaborative Filtering approaches such as Matrix Factorization do not provide a straightforward way of integrating context information into the model. In this work, we introduce a Collaborative Filtering method based on Tensor Factorization, a generalization of Matrix Factorization that allows for a flexible and generic integration of contextual information by modeling the data as a User-Item-Context N-dimensional tensor instead of the traditional 2D User-Item matrix. In the proposed model, called Multiverse Recommendation, different types of context are considered as additional dimensions in the representation of the data as a tensor. The factorization of this tensor leads to a compact model of the data which can be used to provide context-aware recommendations. We provide an algorithm to address the N-dimensional factorization, and show that the Multiverse Recommendation improves upon non-contextual Matrix Factorization up to 30% in terms of the Mean Absolute Error (MAE). We also compare to two state-of-the-art context-aware methods and show that Tensor Factorization consistently outperforms them both in semi-synthetic and real-world data - improvements range from 2.5% to more than 12% depending on the data. Noticeably, our approach outperforms other methods by a wider margin whenever more contextual information is available. <s> BIB004 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> The potential benefit of integrating contextual information for recommendation has received much research attention recently, especially with the ever-increasing interest in mobile-based recommendation services. However, context based recommendation research is limited due to the lack of standard evaluation data with contextual information and reliable technology for extracting such information. As a result, there are no widely accepted conclusions on how, when and whether context helps. Additionally, a system often suffers from the so called cold start problem due to the lack of data for training the initial context based recommendation model. This paper proposes a novel solution to address these problems with automated information extraction techniques. We also compare several approaches for utilizing context based on a new data set collected using the proposed solution. The experimental results demonstrate that 1) IE-based techniques can help create a large scale context data with decent quality from online reviews, at least for restaurant recommendations; 2) context helps recommender systems rank items, however, does not help predict user ratings; 3) simply using context to filter items hurts recommendation performance, while a new probabilistic latent relational model we proposed helps. <s> BIB005 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Most recommender systems focus on the areas of leisure activities. As the Web evolves into omnipresent utility, recommender systems penetrate more serious applications such as those in online scientific communities. In this paper, we investigate the task of recommendation in online scientific communities which exhibit two characteristics: 1) there exists very rich information about users and items; 2) The users in the scientific communities tend not to give explicit ratings to the resources, even though they have clear preference in their minds. To address the above two characteristics, we propose matrix factorization techniques to incorporate rich user and item information into recommendation with implicit feedback. Specifically, the user information matrix is decomposed into a shared subspace with the implicit feedback matrix, and so does the item information matrix. In other words, the subspaces between multiple related matrices are jointly learned by sharing information between the matrices. The experiments on the testbed from an online scientific community (i.e., Nanohub) show that the proposed method can effectively improve the recommendation performance. <s> BIB006 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> In the past decade large scale recommendation datasets were published and extensively studied. In this work we describe a detailed analysis of a sparse, large scale dataset, specifically designed to push the envelope of recommender system models. The Yahoo! Music dataset consists of more than a million users, 600 thousand musical items and more than 250 million ratings, collected over a decade. It is characterized by three unique features: First, rated items are multi-typed, including tracks, albums, artists and genres; Second, items are arranged within a four level taxonomy, proving itself effective in coping with a severe sparsity problem that originates from the unusually large number of items (compared to, e.g., movie ratings datasets). Finally, fine resolution timestamps associated with the ratings enable a comprehensive temporal and session analysis. We further present a matrix factorization model exploiting the special characteristics of this dataset. In particular, the model incorporates a rich bias model with terms that capture information from the taxonomy of items and different temporal dynamics of music ratings. To gain additional insights of its properties, we organized the KddCup-2011 competition about this dataset. As the competition drew thousands of participants, we expect the dataset to attract considerable research activity in the future. <s> BIB007 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Context aware recommender systems (CARS) adapt the recommendations to the specific situation in which the items will be consumed. In this paper we present a novel context-aware recommendation algorithm that extends Matrix Factorization. We model the interaction of the contextual factors with item ratings introducing additional model parameters. The performed experiments show that the proposed solution provides comparable results to the best, state of the art, and more complex approaches. The proposed solution has the advantage of smaller computational cost and provides the possibility to represent at different granularities the interaction between context and items. We have exploited the proposed model in two recommendation applications: places of interest and music. <s> BIB008 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So far, the best performing method for context-aware rating prediction in terms of predictive accuracy is Multiverse Recommendation based on the Tucker tensor factorization model. However this method has two drawbacks: (1) its model complexity is exponential in the number of context variables and polynomial in the size of the factorization and (2) it only works for categorical context variables. On the other hand there is a large variety of fast but specialized recommender methods which lack the generality of context-aware methods. We propose to apply Factorization Machines (FMs) to model contextual information and to provide context-aware rating predictions. This approach results in fast context-aware recommendations because the model equation of FMs can be computed in linear time both in the number of context variables and the factorization size. For learning FMs, we develop an iterative optimization method that analytically finds the least-square solution for one parameter given the other ones. Finally, we show empirically that our approach outperforms Multiverse Recommendation in prediction quality and runtime. <s> BIB009 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems. <s> BIB010 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> The increasing amount of side information associated with the items in E-commerce applications has provided a very rich source of information that, once properly exploited and incorporated, can significantly improve the performance of the conventional recommender systems. This paper focuses on developing effective algorithms that utilize item side information for top-N recommender systems. A set of sparse linear methods with side information (SSLIM) is proposed, which involve a regularized optimization process to learn a sparse aggregation coefficient matrix based on both user-item purchase profiles and item side information. This aggregation coefficient matrix is used within an item-based recommendation framework to generate recommendations for the users. Our experimental results demonstrate that SSLIM outperforms other methods in effectively utilizing side information and achieving performance improvement. <s> BIB011 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> In this paper, we tackle the problem of top-N context-aware recommendation for implicit feedback scenarios. We frame this challenge as a ranking problem in collaborative filtering (CF). Much of the past work on CF has not focused on evaluation metrics that lead to good top-N recommendation lists in designing recommendation models. In addition, previous work on context-aware recommendation has mainly focused on explicit feedback data, i.e., ratings. We propose TFMAP, a model that directly maximizes Mean Average Precision with the aim of creating an optimally ranked list of items for individual users under a given context. TFMAP uses tensor factorization to model implicit feedback data (e.g., purchases, clicks) with contextual information. The optimization of MAP in a large data collection is computationally too complex to be tractable in practice. To address this computational bottleneck, we present a fast learning algorithm that exploits several intrinsic properties of average precision to improve the learning efficiency of TFMAP, and to ensure its scalability. We experimentally verify the effectiveness of the proposed fast learning algorithm, and demonstrate that TFMAP significantly outperforms state-of-the-art recommendation approaches. <s> BIB012 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Recommender systems have been researched extensively by the Technology Enhanced Learning (TEL) community during the last decade. By identifying suitable resources from a potentially overwhelming variety of choices, such systems offer a promising approach to facilitate both learning and teaching tasks. As learning is taking place in extremely diverse and rich environments, the incorporation of contextual information about the user in the recommendation process has attracted major interest. Such contextualization is researched as a paradigm for building intelligent systems that can better predict and anticipate the needs of users, and act more efficiently in response to their behavior. In this paper, we try to assess the degree to which current work in TEL recommender systems has achieved this, as well as outline areas in which further work is needed. First, we present a context framework that identifies relevant context dimensions for TEL applications. Then, we present an analysis of existing TEL recommender systems along these dimensions. Finally, based on our survey results, we outline topics on which further research is needed. <s> BIB013 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Bayesian treatment of matrix factorization has been successfully applied to the problem of collaborative prediction, where unknown ratings are determined by the predictive distribution, inferring posterior distributions over user and item factor matrices that are used to approximate the user-item matrix as their product. In practice, however, Bayesian matrix factorization suffers from cold-start problems, where inferences are required for users or items about which a sufficient number of ratings are not gathered. In this paper we present a method for Bayesian matrix factorization with side information, to handle cold-start problems. To this end, we place Gaussian-Wishart priors on mean vectors and precision matrices of Gaussian user and item factor matrices, such that mean of each prior distribution is regressed on corresponding side information. We develop variational inference algorithms to approximately compute posterior distributions over user and item factor matrices. In addition, we provide Bayesian Cramer-Rao Bound for our model, showing that the hierarchical Bayesian matrix factorization with side information improves the reconstruction over the standard Bayesian matrix factorization where the side information is not used. Experiments on MovieLens data demonstrate the useful behavior of our model in the case of cold-start problems. <s> BIB014 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> In standard matrix completion theory, it is required to have at least O(n ln2 n) observed entries to perfectly recover a low-rank matrix M of size n x n, leading to a large number of observations when n is large. In many real tasks, side information in addition to the observed entries is often available. In this work, we develop a novel theory of matrix completion that explicitly explore the side information to reduce the requirement on the number of observed entries. We show that, under appropriate conditions, with the assistance of side information matrices, the number of observed entries needed for a perfect recovery of matrix M can be dramatically reduced to O(ln n). We demonstrate the effectiveness of the proposed approach for matrix completion in transductive incomplete multi-label learning. <s> BIB015 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Contexts and social network information have been proven to be valuable information for building accurate recommender system. However, to the best of our knowledge, no existing works systematically combine diverse types of such information to further improve recommendation quality. In this paper, we propose SoCo, a novel context-aware recommender system incorporating elaborately processed social network information. We handle contextual information by applying random decision trees to partition the original user-item-rating matrix such that the ratings with similar contexts are grouped. Matrix factorization is then employed to predict missing preference of a user for an item using the partitioned matrix. In order to incorporate social network information, we introduce an additional social regularization term to the matrix factorization objective function to infer a user's preference for an item by learning opinions from his/her friends who are expected to share similar tastes. A context-aware version of Pearson Correlation Coefficient is proposed to measure user similarity. Real datasets based experiments show that SoCo improves the performance (in terms of root mean square error) of the state-of-the-art context-aware recommender system and social recommendation model by 15.7% and 12.2% respectively. <s> BIB016 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Probabilistic matrix factorization (PMF) is a powerful method for modeling data associ- ated with pairwise relationships, Finding use in collaborative Filtering, computational bi- ology, and document analysis, among other areas. In many domains, there are additional covariates that can assist in prediction. For example, when modeling movie ratings, we might know when the rating occurred, where the user lives, or what actors appear in the movie. It is difficult, however, to incorporate this side information into the PMF model. We propose a framework for incorporating side information by coupling together multi- ple PMF problems via Gaussian process priors. We replace scalar latent features with func- tions that vary over the covariate space. The GP priors on these functions require them to vary smoothly and share information. We apply this new method to predict the scores of professional basketball games, where side information about the venue and date of the game are relevant for the outcome. <s> BIB017 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Rich contextual information is typically available in many recommendation domains allowing recommender systems to model the subtle effects of context on preferences. Most contextual models assume that the context shares the same latent space with the users and items. In this work we propose CARS2, a novel approach for learning context-aware representations for context-aware recommendations. We show that the context-aware representations can be learned using an appropriate model that aims to represent the type of interactions between context variables, users and items. We adapt the CARS2 algorithms to explicit feedback data by using a quadratic loss function for rating prediction, and to implicit feedback data by using a pairwise and a listwise ranking loss functions for top-N recommendations. By using stochastic gradient descent for parameter estimation we ensure scalability. Experimental evaluation shows that our CARS2 models achieve competitive recommendation performance, compared to several state-of-the-art approaches. <s> BIB018 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Online social networking sites have become popular platforms on which users can link with each other and share information, not only basic rating information but also information such as contexts, social relationships, and item contents. However, as far as we know, no existing works systematically combine diverse types of information to build more accurate recommender systems. In this paper, we propose a novel context-aware hierarchical Bayesian method. First, we propose the use of spectral clustering for user-item subgrouping, so that users and items in similar contexts are grouped. We then propose a novel hierarchical Bayesian model that can make predictions for each user-item subgroup, our model incorporate not only topic modeling to mine item content but also social matrix factorization to handle ratings and social relationships. Experiments on an Epinions dataset show that our method significantly improves recommendation performance compared with six categories of state-of-the-art recommendation methods in terms of both prediction accuracy and recall. We have also conducted experiments to study the extent to which ratings, contexts, social relationships, and item contents contribute to recommendation performance in terms of prediction accuracy and recall. <s> BIB019 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Context-aware recommendation (CAR) can lead to significant improvements in the relevance of the recommended items by modeling the nuanced ways in which context influences preferences. The dominant approach in context-aware recommendation has been the multidimensional latent factors approach in which users, items, and context variables are represented as latent features in low-dimensional space. An interaction between a user, item, and a context variable is typically modeled as some linear combination of their latent features. However, given the many possible types of interactions between user, items and contextual variables, it may seem unrealistic to restrict the interactions among them to linearity. To address this limitation, we develop a novel and powerful non-linear probabilistic algorithm for context-aware recommendation using Gaussian processes. The method which we call Gaussian Process Factorization Machines (GPFM) is applicable to both the explicit feedback setting (e.g. numerical ratings as in the Netflix dataset) and the implicit feedback setting (i.e. purchases, clicks). We derive stochastic gradient descent optimization to allow scalability of the model. We test GPFM on five different benchmark contextual datasets. Experimental results demonstrate that GPFM outperforms state-of-the-art context-aware recommendation methods. <s> BIB020 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Among different hybrid recommendation techniques, network-based entity recommendation methods, which utilize user or item relationship information, are beginning to attract increasing attention recently. Most of the previous studies in this category only consider a single relationship type, such as friendships in a social network. In many scenarios, the entity recommendation problem exists in a heterogeneous information network environment. Different types of relationships can be potentially used to improve the recommendation quality. In this paper, we study the entity recommendation problem in heterogeneous information networks. Specifically, we propose to combine heterogeneous relationship information for each user differently and aim to provide high-quality personalized recommendation results using user implicit feedback data and personalized recommendation models. In order to take full advantage of the relationship heterogeneity in information networks, we first introduce meta-path-based latent features to represent the connectivity between users and items along different types of paths. We then define recommendation models at both global and personalized levels and use Bayesian ranking optimization techniques to estimate the proposed models. Empirical studies show that our approaches outperform several widely employed or the state-of-the-art entity recommendation techniques. <s> BIB021 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <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> BIB022 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> In this paper, we propose a generic framework to learn context-aware latent representations for context-aware collaborative filtering. Contextual contents are combined via a function to produce the context influence factor, which is then combined with each latent factor to derive latent representations. We instantiate the generic framework using biased Matrix Factorization as the base model. A Stochastic Gradient Descent (SGD) based optimization procedure is developed to fit the model by jointly learning the weight of each context and latent factors. Experiments conducted over three real-world datasets demonstrate that our model significantly outperforms not only the base model but also the representative context-aware recommendation models. <s> BIB023 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features' latent factors. The model outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios (using both user and item metadata), and performs at least as well as a pure collaborative matrix factorisation model where interaction data is abundant. Additionally, feature embeddings produced by the model encode semantic information in a way reminiscent of word embedding approaches, making them useful for a range of related tasks such as tag recommendations. <s> BIB024 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Matrix completion methods can benefit from side information besides the partially observed matrix. The use of side features that describe the row and column entities of a matrix has been shown to reduce the sample complexity for completing the matrix. We propose a novel sparse formulation that explicitly models the interaction between the row and column side features to approximate the matrix entries. Unlike early methods, this model does not require the low rank condition on the model parameter matrix. We prove that when the side features span the latent feature space of the matrix to be recovered, the number of observed entries needed for an exact recovery is O(log N) where N is the size of the matrix. If the side features are corrupted latent features of the matrix with a small perturbation, our method can achieve an ε-recovery with O(log N) sample complexity. If side information is useless, our method maintains a O(N3/2) sampling rate similar to classic methods. An efficient linearized Lagrangian algorithm is developed with a convergence guarantee. Empirical results show that our approach outperforms three state-of-the-art methods both in simulations and on real world datasets. <s> BIB025 </s> Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification <s> INTRODUCTION <s> Recommender systems have been widely studied in the literature as they have real world impacts in many E-commerce platforms and social networks. Most previous systems are based on the user-item recommendation matrix, which contains users' history recommendation activities on items. In this paper, we propose a novel predictive collaborative filtering approach that exploits both the partially observed user-item recommendation matrix and the item-based side information to produce top-N recommender systems. The proposed approach automatically identifies the most interesting items for each user from his or her non-recommended item pool by aggregating over his or her recommended items via a low-rank coefficient matrix. Moreover, it also simultaneously builds linear regression models from the item-based side information such as item reviews to predict the item recommendation scores for the users. The proposed approach is formulated as a rank constrained joint minimization problem with integrated least squares losses, for which an efficient analytical solution can be derived. To evaluate the proposed learning technique, empirical evaluations on five recommendation tasks are conducted. The experimental results demonstrate the efficacy of the proposed approach comparing to the competing methods. <s> BIB026
Collaborative filtering is arguably the most effective method for building a recommender system. It assumes that a user's preferences regarding items can be inferred collaboratively from other users' preferences. In practice, users' past records regarding items, such as explicit ratings or implicit feedback (e.g., binary access records), are typically used to infer similarity of taste among users for the purposes of recommendation. In the past decade, matrix factorization (MF) has become a widely adopted method of collaborative filtering. Specifically, MF learns a latent representation vector for a user and an item and computes their inner products as the predicted rating. The learned latent user/item factors are supposed to embed specific information about the user/item. That is, two users with similar latent representations will have similar tastes regarding items with similar latent vectors. In the big data era, classical MF using only ratings suffers a serious drawback, as such a method is unable to exploit other accessible information such as the attributes of users/items/ratings. For instance, data could contain the location and time that a user rated an item. These rating-relevant attributes, or contexts, could be useful in determining the scale of user liking for an item. The side information or attributes relevant to users or items (e.g., the demographic information of users or the item genre) can also reveal useful information. Such side information is particularly useful for situations where the ratings of a user or an item are sparse, which is known as the cold-start problem for recommender systems. Therefore, researchers have formulated attribute-aware recommender systems (see Figure 1 ) aiming at leveraging not only the rating information but also the attributes associated with ratings/users/items to improve the quality of recommendation. Researchers have proposed different methods to extend existing collaborative filtering models in recent years, such as factorization machines, probabilistic graphical models, kernel tricks, and models based on deep neural networks. We notice that those papers can also be categorized based on the type of attributes incorporated into the models. A class of recommender systems considers relevant side information, such as age, gender, the occupation of users, or the expiration and price of items when predicting ratings (e.g., BIB017 BIB003 BIB006 BIB011 Zhou et al., 2012; BIB014 BIB015 BIB007 BIB025 BIB026   Zhou T. et al., 2017) . On the other hand, context-aware recommender systems (e.g., BIB001 BIB004 BIB005 BIB008 BIB009 Tikk, 2012, 2016; BIB012 BIB018 BIB016 BIB019 BIB020 BIB023 enhance themselves by considering the attributes appended to each rating (e.g., rating time, rating location). Other terms may be used to indicate attributes interchangeably such as metadata BIB024 , features , taxonomy (Koenigstein et al., 2011) , entities BIB021 , demographic data , categories , contexture information BIB002 , etc. The above setups all share the same mathematical representation; thus, technically, we do not distinguish them in this paper. That is, we regard whichever information is associated with a user/item/rating as user/item/rating attributes, regardless of its nature. Therefore, a CF model that takes advantage of ratings as well as associated attributes is called an attribute-aware recommender in this paper. Note that the attribute-aware recommender systems discussed in this paper are not equivalent to hybrid recommender systems. The former treat additional information as attributes, while the latter emphasize the combination of collaborative filteringbased methods and content-based methods. To be more precise, this review covers only works that assume unstructured and independent attributes, either in binary or numerical format, for each user, item, or rating. The reviewed models do not have prior knowledge of the dependency between attributes, such as the adjacent terms in a document or user relationships in a social network. This review covers more than one hundred papers in this area in the past decade. We find that the majority of the works propose an extension of matrix factorization to incorporate attribute information in collaborative filtering. The main contribution of this paper is to not only provide a comprehensive review, but also provide a means to classify these works into four categories: (I) discriminative matrix factorization, (II) generative matrix factorization, (III) generalized factorization, and (IV) heterogeneous graphs. For each category, we provide the probabilistic interpretation of the models. The major distinction of these four categories lies in their representation of the interactions between users, items, and attributes. Discriminative matrix factorization models extend the traditional MF by treating the attributes as prior knowledge to learn the latent representation of users or items. Generative matrix factorization further considers the distributions of attributes and learns such, together with the rating distributions. Generalized factorization models view the user/item identity simply as a kind of attribute, and various models have been designed for determining the lowdimensional representation vectors for rating prediction. The last category of models proposes to represent the users, items, and attributes using a heterogeneous graph, where a recommendation task can be cast into a link-prediction task on the heterogeneous graph. In the following sections, we will elaborate on general mathematical explanations of the four types of model designs and discuss the similarities/differences among the models. There have been four prior reviews BIB010 BIB013 Bobadilla et al., 2013; BIB022 introducing attribute-aware recommender systems. We claim three major differences between our work and the existing papers. First, previous review papers mainly focused on grouping different types of attributes and discussing the distinctions of memory-based collaborative filtering and modelbased collaborative filtering. In contrast, we are the first that have aimed at classifying the existing works based on the methodology proposed instead of the type of data used. We further provide mathematical connections for different types of models so that the readers can better understand the spirit of the design of different models as well as their technical differences. Second, we are the first to provide thorough experiment results (seven different models on eight benchmark datasets) to compare different types of attribute-award recommendation systems. Note that Bobadilla et al. (2013) is the only previous review work with experimental results. However, for that study, experiments were performed to compare different similarity measures in collaborative filtering algorithms instead of directly verifying the effectiveness of different attribute-aware recommender systems. Finally, we cover the latest work on attribute-aware recommender systems. We note that the existing review papers do not include forty papers after 2015. In recent years, several deep neural network-based solutions have achieved state-of-the-art performance for this task. Table 1 shows comparisons between our work and previous reviews. We will introduce the basic concepts behind recommender systems in section 2, followed by formal analyses of attributeaware recommender systems in sections 3 and 4. A series of experiments detailed in section 5 were conducted to compare the accuracy and parameter sensitivity of six widely adopted models. 