Recommender systems are becoming more and more important in our daily lives. However, traditional recommendation methods are challenged by data sparsity and efficiency, as the numbers of users, items, and interactions between the two in many real-world applications increase fast. In this work, we propose a novel clustering recommender system based on node2vec technology and rich information network, namely N2VSCDNNR, to solve these challenges. In particular, we use a bipartite network to construct the user-item network, and represent the interactions among users (or items) by the corresponding one-mode projection network. In order to alleviate the data sparsity problem, we enrich the network structure according to user and item categories, and construct the one-mode projection category network. Then, considering the data sparsity problem in the network, we employ node2vec to capture the complex latent relationships among users (or items) from the corresponding one-mode projection category network. Moreover, considering the dependency on parameter settings and information loss problem in clustering methods, we use a novel spectral clustering method, which is based on dynamic nearest-neighbors (DNN) and a novel automatically determining cluster number (ADCN) method that determines the cluster centers based on the normal distribution method, to cluster the users and items separately. After clustering, we propose the two-phase personalized recommendation to realize the personalized recommendation of items for each user. A series of experiments validate the outstanding performance of our N2VSCDNNR over several advanced embedding and side information based recommendation algorithms. Meanwhile, N2VSCDNNR seems to have lower time complexity than the baseline methods in online recommendations, indicating its potential to be widely applied in large-scale systems.
Then, with the development of network embedding, a number of embedding-based recommender systems were proposed in recently years. For instance, @cite_18 proposed an entity2rec algorithm to learning user-item relatedness from knowledge graphs, so as to realize item recommendation. Kiss and Filzmoser @cite_23 proposed a method to map the users and items to the same two-dimensional embedding space to make the recommendations. Swami @cite_33 introduced a heterogeneous representation learning model, called Metapath2vec++, which uses meta-path-based random walks to construct the heterogeneous neighbors of a node and then leverages a heterogeneous skip-gram model to perform node embeddings, and further make recommendations based on the network representation. @cite_1 proposed a network embedding method for bipartite networks, namely BiNE. It generates node sequences that can well preserve the long-tail distribution of nodes in the bipartite networks by performing biased random walks purposefully. The authors make recommendations with the generated network representation. @cite_22 proposed an embedding based recommendation method. In this model, they use a network embedding method to map each user into a low dimensional space at first, and then incorporate user vectors into a matrix factorization model for recommendation.
@cite_18 @cite_33 @cite_22 @cite_1 @cite_23
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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. We study the problem of representation learning in heterogeneous networks. Its unique challenges come from the existence of multiple types of nodes and links, which limit the feasibility of the conventional network embedding techniques. We develop two scalable representation learning models, namely metapath2vec and metapath2vec++. The metapath2vec model formalizes meta-path-based random walks to construct the heterogeneous neighborhood of a node and then leverages a heterogeneous skip-gram model to perform node embeddings. The metapath2vec++ model further enables the simultaneous modeling of structural and semantic correlations in heterogeneous networks. Extensive experiments show that metapath2vec and metapath2vec++ are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, such as node classification, clustering, and similarity search, but also discern the structural and semantic correlations between diverse network objects. With the advent of online social networks, the use of information hidden in social networks for recommendation has been extensively studied. Unlike previous work regarded social influence as regularization terms, we take advantage of network embedding techniques and propose an embedding based recommendation method. Specifically, we first pre-train a network embedding model on the users' social network to map each user into a low dimensional space, and then incorporate them into a matrix factorization model, which combines both latent and pre-learned features for recommendation. The experimental results on two real-world datasets indicate that our proposed model is more effective and can reach better performance than other related methods. This work develops a representation learning method for bipartite networks. While existing works have developed various embedding methods for network data, they have primarily focused on homogeneous networks in general and overlooked the special properties of bipartite networks. As such, these methods can be suboptimal for embedding bipartite networks. In this paper, we propose a new method named BiNE, short for Bipartite Network Embedding, to learn the vertex representations for bipartite networks. By performing biased random walks purposefully, we generate vertex sequences that can well preserve the long-tail distribution of vertices in the original bipartite network. We then propose a novel optimization framework by accounting for both the explicit relations (i.e., observed links) and implicit relations (i.e., unobserved but transitive links) in learning the vertex representations. We conduct extensive experiments on several real datasets covering the tasks of link prediction (classification), recommendation (personalized ranking), and visualization. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our BiNE method. This paper presents a novel, graph embedding based recommendation technique. The method operates on the knowledge graph, an information representation technique alloying content-based and collaborative information. To generate recommendations, a two dimensional embedding is developed for the knowledge graph. As the embedding maps the users and the items to the same vector space, the recommendations are then calculated on a spatial basis. Regarding to the number of cold start cases, precision, recall, normalized Cumulative Discounted Gain and computational resource need, the evaluation shows that the introduced technique delivers a higher performance compared to collaborative filtering on top-n recommendation lists. Our further finding is that graph embedding based methods show a more stable performance in the case of an increasing amount of user preference information compared to the benchmark method.