text: Recently, word embedding algorithms have been applied to map the entities of recommender systems, such as users and items, to new feature spaces using textual element-context relations among them. Unlike many other domains, this approach has not achieved a desired performance in collaborative filtering problems, probably due to unavailability of appropriate textual data. In this paper we propose a new recommendation framework, called GEMRank that can be applied when the user-item matrix is the sole available souce of information. It uses the concept of profile co-occurrence for defining relations among entities and applies a factorization method for embedding the users and items. GEMRank then feeds the extracted representations to a neural network model to predict user-item like dislike relations which the final recommendations are made based on. We evaluated GEMRank in an extensive set of experiments against state of the art recommendation methods. The results show that GEMRank significantly outperforms the baseline algorithms in a variety of data sets with different degrees of density.
  @cite_0: Abstract GRank is a recent graph-based recommendation approach the uses a novel heterogeneous information network to model users’ priorities and analyze it to directly infer a recommendation list. Unfortunately, GRank neglects the semantics behind different types of paths in the network and during the process, it may use unreliable paths that are inconsistent with the general idea of similarity in neighborhood collaborative ranking. That negligence undermines the reliability of the recommendation list generated by GRank. This paper seeks to present a novel framework for reliable graph-based collaborative ranking, called ReGRank, that ranks items based on reliable recommendation paths that are in harmony with the semantics behind different approaches in neighborhood collaborative ranking. To our knowledge, ReGRank is the first unified framework for neighborhood collaborative ranking that in addition to traditional user-based collaborative ranking, can also be adapted for preference-based and representative-based collaborative ranking as well. Experimental results show that ReGRank significantly improves the state-of-the art neighborhood and graph-based collaborative ranking algorithms.
  @cite_14: Recommender systems have been successfully dealing with the problem of information overload. A considerable amount of research has been conducted on recommender systems, but most existing approaches only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a Multi-Layer Context Graph (MLCG) model which incorporates a variety of contextual information into a recommendation process and models the interactions between users and items for better recommendation. Moreover, we provide a new ranking algorithm based on Personalized PageRank for recommendation in MLCG, which captures users’ preferences and current situations. The experiments on two real-world datasets demonstrate the effectiveness of our approach.
  @cite_3: 
  @cite_16: Collaborative ranking is an emerging field of recommender systems that utilizes users’ preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users’ preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.
summary: Traditional NCR algorithms usually suffer from sparsity of the dataset as the similarity calculation can be hard when there is not enough information about users available. Graph based methods try to solve this problem by modeling the data as a graph in order to estimate the distances more accurately when the data is sparse. These methods first construct a graph to represent data and then make recommendations by analyzing the graph. In @cite_14 different types of nodes and a multi-layer structure have been used to make context-aware recommendation through a random walk in the graph. SibRank @cite_16 uses a signed bipartite preference network for representing the data and analyzes it using a signed version of Personalized PageRank to capture users' similarities. Among more recent approaches, GRank @cite_3 is an state of the art method which uses personalized PageRank over a tripartite preference network to directly infer the total ranking of items. GRank may use unreliable paths that are inconsistent with the general idea of similarity in neighborhood collaborative ranking. ReGRank @cite_0 ranks items based on reliable recommendation paths that are in harmony with the semantics behind different approaches in neighborhood collaborative ranking.