Graph Representation Learning-Based Recommender Systems

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Personalized recommendation has been applied to many online services such as E-commerce and adverting. It facilitates users to discover a small set of relevant items, which meet their personalized interests, from many choices. Nowadays, various auxiliary information on users and items become increasingly available in online platforms, such as user demographics, social relations, and item knowledge. More recent evidences suggests that incorporating such auxiliary data with collaborative filtering can better capture the underlying and complex user-item relationships, and further achieve higher recommendation quality. In this thesis, we focus on auxiliary data with graph structure, such as social networks and knowledge graphs (KG). For example, we can improve recommendation performance by mining social relationships between users, and also by using knowledge graphs to enhance the semantics of recommended items. Network representation learning aims to represent each vertex in a network (graph) as a low-dimensional vector while still preserving its structural information. Due to the availability of massive graph data in recommender systems, it is a promising approach to combine network representation learning with recommendation. Applying the learned graph features to recommender systems will effectively enhance the learning ability of the recommender systems and improve the accuracy and user satisfaction of the recommender systems. For network representation learning and its application in recommendation systems, the major contributions of this thesis are as follows: (1) Attention-based Adversarial Autoencoder for Multi-scale Network Embedding. Existing Network representation methods usually adopt a one-size-fits-all approach when concerning multi-scale structure information, such as first- and second-order proximity of nodes, ignoring the fact that different scales play different roles in embedding learning. We propose an Attention-based Adversarial Autoencoder Network Embedding (AAANE) framework, which promotes the collaboration of different scales and lets them vote for robust representations. (2) Multi-modal Multi-view Bayesian Semantic Embedding for Community Question Answering: Semantic embedding has demonstrated its value in latent representation learning of data, and can be effectively adopted for many applications. However, it is difficult to propose a joint learning framework for semantic embedding in Community Question Answer (CQA), because CQA data have multi-view and sparse properties. In this thesis, we propose a generic Multi-modal Multi-view Semantic Embedding (MMSE) framework via a Bayesian model for question answering. (3) Context-Dependent Propagating-based Video Recommendation in Multi-modal Heterogeneous Information Networks. Conventional approaches to video recommendation primarily focus on exploiting content features or simple user-video interactions to model the users’ preferences. However these methods fail to model the complex video context interdependency, which is obscure/hidden in heterogeneous auxiliary data. In this paper, we propose a Context-Dependent Propagating Recommendation network (CDPRec) to obtain accurate video embedding and capture global context cues among videos in HINs. The CDPRec can iteratively propagate the contexts of a video along links in a graph-structured HIN and explore multiple types of dependencies among the surrounding video nodes. (4) Knowledge Graph Enhanced Neural Collaborative Filtering. Existing neural collaborative filtering (NCF) recommendation methods suffer from severe sparsity problem. Knowledge Graph (KG), which commonly consists of fruitful connected facts about items, presents an unprecedented opportunity to alleviate the sparsity problem. However, NCF only methods can hardly model the high-order connectivity in KG, and ignores complex pairwise correlations between user/item embedding dimensions. To address these issues, we propose a novel Knowledge graph enhanced Neural Collaborative Recommendation (K-NCR) framework, which effectively combines user-item interaction information and auxiliary knowledge information for recommendation.
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