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Register a new userInductive Representation Based Graph Convolution Network for Collaborative Filtering
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Added by
Yunfan Wu
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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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.
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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.
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.
Personalization lies at the core of boosting the product search system performance. Prior studies mainly resorted to the semantic matching between textual queries and user/product related documents, leaving the user collaborative behaviors untapped. In fact, the collaborative filtering signals between users intuitively offer a complementary information for the semantic matching. To close the gap between collaborative filtering and product search, we propose a Hierarchical Heterogeneous Graph Neural Network (HHGNN) approach in this paper. Specifically, we organize HHGNN with a hierarchical graph structure according to the three edge types. The sequence edge accounts for the syntax formulation from word nodes to sentence nodes; the composition edge aggregates the semantic features to the user and product nodes; and the interaction edge on the top performs graph convolutional operation between user and product nodes. At last, we integrate the higher-order neighboring collaborative features and the semantic features for better representation learning. We conduct extensive experiments on six Amazon review datasets. The results show that our proposed method can outperform the state-of-the-art baselines with a large margin. In addition, we empirically prove that collaborative filtering and semantic matching are complementary to each other in product search performance enhancement.
Recently, Graph Convolution Network (GCN) based methods have achieved outstanding performance for recommendation. These methods embed users and items in Euclidean space, and perform graph convolution on user-item interaction graphs. However, real-world datasets usually exhibit tree-like hierarchical structures, which make Euclidean space less effective in capturing user-item relationship. In contrast, hyperbolic space, as a continuous analogue of a tree-graph, provides a promising alternative. In this paper, we propose a fully hyperbolic GCN model for recommendation, where all operations are performed in hyperbolic space. Utilizing the advantage of hyperbolic space, our method is able to embed users/items with less distortion and capture user-item interaction relationship more accurately. Extensive experiments on public benchmark datasets show that our method outperforms both Euclidean and hyperbolic counterparts and requires far lower embedding dimensionality to achieve comparable performance.
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