You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification 
Chapter Title:  Modeling User-Item-Rating Interactions Using Heterogeneous Graphs 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  Heterogeneous networks refer to the networks comprising multiple types of entities as well as their interaction relationships. They arise in a great variety of domains, for example, event-based social networks Meetup and Plancast, and DBLP. Recommendation is a useful task in these heterogeneous network systems. Although many recommendation algorithms are proposed for heterogeneous data, none of them is able to explicitly model the influence strength between different types of entities, which is useful not only for achieving higher recommendation accuracy but also better understanding the role of each entity type in recommendation problems. Moreover, many of those algorithms are designed for a particular task, and hence it is challenging to apply them in other problems. In this paper, we propose a graph-based model, called HeteRS, which can solve general recommendation problems on heterogeneous networks. Our method models the rich information with a heterogeneous graph and considers the recommendation problem as a query-dependent node proximity problem. To address the challenging issue of weighting the influences between different types of entities, we propose a learning scheme to set the influence weights between different types of entities in recommendation. Experimental results on real-world datasets demonstrate that our proposed method significantly outperforms the baseline methods in our experiments for all the recommendation tasks, and the learned influence weights help understanding user behaviors. 
3. Abstract of Cited Paper (BIB003):  Recently, social recommendation becomes a hot research direction, which leverages social relations among users to alleviate data sparsity and cold-start problems in recommender systems. The social recommendation methods usually employ simple similarity information of users as social regularization on users. Unfortunately, the widely used social regularization may suffer from several aspects: 1 the similarity information of users only stems from users' social relations; 2 it only has constraint on users; 3 it may not work well for users with low similarity. In order to overcome the shortcomings of social regularization, we propose a new dual similarity regularization to impose the constraint on users and items with high and low similarities simultaneously. With the dual similarity regularization, we design an optimization function to integrate the similarity information of users and items, and a gradient descend solution is derived to optimize the objective function. Experiments on two real datasets validate the effectiveness of the proposed solution. 
4. Abstract of Cited Paper (BIB004):  Knowledge Graphs have proven to be extremely valuable to recommender systems, as they enable hybrid graph-based recommendation models encompassing both collaborative and content information. Leveraging this wealth of heterogeneous information for top-N item recommendation is a challenging task, as it requires the ability of effectively encoding a diversity of semantic relations and connectivity patterns. In this work, we propose entity2rec, a novel approach to learning user-item relatedness from knowledge graphs for top-N item recommendation. We start from a knowledge graph modeling user-item and item-item relations and we learn property-specific vector representations of users and items applying neural language models on the network. These representations are used to create property-specific user-item relatedness features, which are in turn fed into learning to rank algorithms to learn a global relatedness model that optimizes top-N item recommendations. We evaluate the proposed approach in terms of ranking quality on the MovieLens 1M dataset, outperforming a number of state-of-the-art recommender systems, and we assess the importance of property-specific relatedness scores on the overall ranking quality. 
5. Abstract of Cited Paper (BIB005):  Recommendation based on heterogeneous information network(HIN) is attracting more and more attention due to its ability to emulate collaborative filtering, content-based filtering, context-aware recommendation and combinations of any of these recommendation semantics. Random walk based methods are usually used to mine the paths, weigh the paths, and compute the closeness or relevance between two nodes in a HIN. A key for the success of these methods is how to properly set the weights of links in a HIN. In existing methods, the weights of links are mostly set heuristically. In this paper, we propose a Bayesian Personalized Ranking(BPR) based machine learning method, called HeteLearn, to learn the weights of links in a HIN. In order to model user preferences for personalized recommendation, we also propose a generalized random walk with restart model on HINs. We evaluate the proposed method in a personalized recommendation task and a tag recommendation task. Experimental results show that our method performs significantly better than both the traditional collaborative filtering and the state-of-the-art HIN-based recommendation methods. 
6. Abstract of Cited Paper (BIB006):  In the past, hybrid recommender systems have shown the power of exploiting relationships amongst objects which directly or indirectly effect the recommendation task. However, the effect of all relations is not equal, and choosing their right balance for a recommendation problem at hand is non-trivial. We model these interactions using a Heterogeneous Information Network, and propose a systematic framework for learning their influence weights for a given recommendation task. Further, we address the issue of redundant results, which is very much prevalent in recommender systems. To alleviate redundancy in recommendations we use Vertex Reinforced Random Walk (a non-Markovian random walk) over a heterogeneous graph. It works by boosting the transitions to the influential nodes, while simultaneously shrinking the weights of others. This helps in discouraging recommendation of multiple influential nodes which lie in close proximity of each other, thus ensuring diversity. Finally, we demonstrate the effectiveness of our approach by experimenting on real world datasets. We find that, with the weights of relations learned using the proposed non-Markovian random walk based framework, the results consistently improve over the baselines.

Literature Review:
We notice several relevant works that perform low-rank factorization or representation learning in heterogeneous graphs, such as BIB002 BIB003 BIB004 BIB005 BIB006 BIB007 . Note that these works do not require graphstructured data. Instead, they model the interactions between user, item, rating, and attribute as a heterogeneous graph. The interactions of users and items can be represented by a heterogeneous graph with two node types. An edge is unweighted for implicit feedback and weighted for explicit opinions. External attributes are typically leveraged by assigning them extra nodes in the heterogeneous graph. A heterogeneous graph structure is more suitable for categorical attributes, since each candidate value of attributes can be naturally assigned a node. In heterogeneous graphs, recommendation can be viewed as a link prediction problem. Predicting a future rating corresponds to forecasting whether an edge will be built between user and item nodes. The existing works commonly adopt a two-stage algorithm to learn the model. First, we apply a random-walk or a meta-path algorithm to gather the similarities between users and items from a heterogeneous graph. The similarity information can be kept as multiple similarity matrices or network embedding vectors. Then, a matrix factorization model or other supervised machine learning algorithms are applied to extract discriminative features from the gathered similarity information, which is used for future rating prediction. Another kind of method is to first define the environment where ranking or similarity algorithms are applied. Defining the environment refers to either determining the heterogeneous graph structures or learning the transition probabilities between nodes from observed heterogeneous graphs. Having the environment, we can apply an existing algorithm (Rooted PageRank, for example) or a proposed method to gain the relative ranking scores for each item. In other words, the main difference between the two kinds of methods is whether the similarity calculation is in the first stage or the second stage. Both kinds of methods as abovementioned can be unified as a constrained likelihood maximization: where a parameterized function f θ is specifically defined to estimate a similarity score s(u, i) of item i, given user u as a query. The calculation of a similarity score comes from the set P u,i of random walks or paths w from node u to i in the heterogeneous graph. The generation of P u,i considers the attribute node set X. Either or both of the likelihood and the constraint may involve the information of observed ratings δ(R) of rating matrix R for likelihood maximization or similarity calculation. In our estimation, the current heterogeneous-graphbased models do not directly solve the constrained optimization problem (Equation 43 ). Commonly, they first exploit a twostage solution that either solves the likelihood maximization or satisfies the similarity constraint. The output is then cast into the other part of (Equation 43). With different definitions of f θ and p, the two-stage process may run only once or iteratively until convergence. The definition of s(u, i) in the surveyed papers includes PageRank BIB006 , PathSim BIB002 , and so on. The likelihood function p guides the similarity-related parameters θ to fit the distribution objective of observed similarities s or ratings R. The objective may be given attributes X as auxiliary learning data. Minor works like do not optimize the likelihood; instead, they directly compute the similarity constraint with pre-defined θ from a specifically designed heterogeneous graph. We now explain why random walk or path-based algorithms in heterogeneous graphs are regarded as collaborative filtering methods. For ease of explanation, we first consider the case of no auxiliary attributes. We have users and items as nodes in a graph structure, where edge weights denote the ratings of users toward items. If both users u and v rate the same item i, then i becomes a shortcut from u to v. Therefore, starting from user node u, another user v at a low shortest path distance from u could have similar rating behaviors as u. We can then recommend items at short distances from u, based on the shortcut through v. This is just the spirit of collaborative filtering, which exploits the similar rating behaviors of other users for future recommendation to target users. If attribute nodes are taken into consideration in heterogeneous graphs, they also become the shortcuts for paths between users and items. • HeteRec BIB002 . This model first assumes an attribute-aware heterogeneous graph formed by attributes and ratings. We then obtain M non-negative PathSim BIB001 similarity matrices S (1) , S (2) , . . . , S (m) , . . . , S (M) . Given low-rank non-negative factorization of each S (m) = U (m)⊤ V (m) , a rating estimater is defined as follows: • Graph-Based Flexible Recommendation (GFREC) . This approach applies personalized PageRank, an unsupervised random walk-based algorithm, to perform random walks in a bipartite heterogeneous graph for recommendation. Instead of independently defining a single node for each categorical attribute value, GFREC makes a node imply both an attribute value and its associated user or item. For example, given a user u and its corresponding attribute value x, we can put a node named (u, x) in the heterogeneous network. In a GFREC bipartite heterogeneous graph, two disjoint sets respectively refer to users and items. GFREC shows that personalized PageRank can compute the visiting probabilities of each node in this bipartite heterogeneous graph. Finally, the probabilities are used to rank items to be recommended.