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Register a new userMulti-Graph Convolution Collaborative Filtering
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Added by
Jianing Sun
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Personalized recommendation is ubiquitous, playing an important role in many online services. Substantial research has been dedicated to learning vector representations of users and items with the goal of predicting a users preference for an item based on the similarity of the representations. Techniques range from classic matrix factorization to more recent deep learning based methods. However, we argue that existing methods do not make full use of the information that is available from user-item interaction data and the similarities between user pairs and item pairs. In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding learning process. Multi-GCCF not only expressively models the high-order information via a partite user-item interaction graph, but also integrates the proximal information by building and processing user-user and item-item graphs. Furthermore, we consider the intrinsic difference between user nodes and item nodes when performing graph convolution on the bipartite graph. We conduct extensive experiments on four publicly accessible benchmarks, showing significant improvements relative to several state-of-the-art collaborative filtering and graph neural network-based recommendation models. Further experiments quantitatively verify the effectiveness of each component of our proposed model and demonstrate that the learned embeddings capture the important relationship structure.
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In recent years, graph neural networks (GNNs) have shown powerful ability in collaborative filtering, which is a widely adopted recommendation scenario. While without any side information, existing graph neural network based methods generally learn a one-hot embedding for each user or item as the initial input representation of GNNs. However, such one-hot embedding is intrinsically transductive, making these methods with no inductive ability, i.e., failing to deal with new users or new items that are unseen during training. Besides, the number of model parameters depends on the number of users and items, which is expensive and not scalable. In this paper, we give a formal definition of inductive recommendation and solve the above problems by proposing Inductive representation based Graph Convolutional Network (IGCN) for collaborative filtering. Specifically, we design an inductive representation layer, which utilizes the interaction behavior with core users or items as the initial representation, improving the general recommendation performance while bringing inductive ability. Note that, the number of parameters of IGCN only depends on the number of core users or items, which is adjustable and scalable. Extensive experiments on three public benchmarks demonstrate the state-of-the-art performance of IGCN in both transductive and inductive recommendation scenarios, while with remarkably fewer model parameters. Our implementations are available here in PyTorch.
User-item interactions in recommendations can be naturally de-noted as a user-item bipartite graph. Given the success of graph neural networks (GNNs) in graph representation learning, GNN-based C methods have been proposed to advance recommender systems. These methods often make recommendations based on the learned user and item embeddings. However, we found that they do not perform well wit sparse user-item graphs which are quite common in real-world recommendations. Therefore, in this work, we introduce a novel perspective to build GNN-based CF methods for recommendations which leads to the proposed framework Localized Graph Collaborative Filtering (LGCF). One key advantage of LGCF is that it does not need to learn embeddings for each user and item, which is challenging in sparse scenarios. Alternatively, LGCF aims at encoding useful CF information into a localized graph and making recommendations based on such graph. Extensive experiments on various datasets validate the effectiveness of LGCF especially in sparse scenarios. Furthermore, empirical results demonstrate that LGCF provides complementary information to the embedding-based CF model which can be utilized to boost recommendation performance.
Due to the development of graph neural network models, like graph convolutional network (GCN), graph-based representation learning methods have made great progress in recommender systems. However, the data sparsity is still a challenging problem that graph-based methods are confronted with. Recent works try to solve this problem by utilizing the side information. In this paper, we introduce easily accessible textual information to alleviate the negative effects of data sparsity. Specifically, to incorporate with rich textual knowledge, we utilize a pre-trained context-awareness natural language processing model to initialize the embeddings of text nodes. By a GCN-based node information propagation on the constructed heterogeneous graph, the embeddings of users and items can finally be enriched by the textual knowledge. The matching function used by most graph-based representation learning methods is the inner product, this linear operation can not fit complex semantics well. We design a predictive network, which can combine the graph-based representation learning with the matching function learning, and demonstrate that this predictive architecture can gain significant improvements. Extensive experiments are conducted on three public datasets and the results verify the superior performance of our method over several baselines.
The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph. In recent years, we have witnessed an emerging research effort in exploring user-item graph for collaborative filtering methods. Nevertheless, the formation of user-item interactions typically arises from highly complex latent purchasing motivations, such as high cost performance or eye-catching appearance, which are indistinguishably represented by the edges. The existing approaches still remain the differences between various purchasing motivations unexplored, rendering the inability to capture fine-grained user preference. Therefore, in this paper we propose a novel Multi-Component graph convolutional Collaborative Filtering (MCCF) approach to distinguish the latent purchasing motivations underneath the observed explicit user-item interactions. Specifically, there are two elaborately designed modules, decomposer and combiner, inside MCCF. The former first decomposes the edges in user-item graph to identify the latent components that may cause the purchasing relationship; the latter then recombines these latent components automatically to obtain unified embeddings for prediction. Furthermore, the sparse regularizer and weighted random sample strategy are utilized to alleviate the overfitting problem and accelerate the optimization. Empirical results on three real datasets and a synthetic dataset not only show the significant performance gains of MCCF, but also well demonstrate the necessity of considering multiple components.
Two main challenges in recommender systems are modeling users with heterogeneous taste, and providing explainable recommendations. In this paper, we propose the neural Attentive Multi-Persona Collaborative Filtering (AMP-CF) model as a unified solution for both problems. AMP-CF breaks down the user to several latent personas (profiles) that identify and discern the different tastes and inclinations of the user. Then, the revealed personas are used to generate and explain the final recommendation list for the user. AMP-CF models users as an attentive mixture of personas, enabling a dynamic user representation that changes based on the item under consideration. We demonstrate AMP-CF on five collaborative filtering datasets from the domains of movies, music, video games and social networks. As an additional contribution, we propose a novel evaluation scheme for comparing the different items in a recommendation list based on the distance from the underlying distribution of tastes in the users historical items. Experimental results show that AMP-CF is competitive with other state-of-the-art models. Finally, we provide qualitative results to showcase the ability of AMP-CF to explain its recommendations.
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