Deep Learning for Recommender System: Toward Structures and Sequences

Recommender systems are nowadays everywhere to help users to explore online new resources, items, contents and etc. Along the booming of recommender systems, it is the urgent desire to develop applicable algorithms and frameworks to enhance the performance of recommendation results, where deep learning methods serve the best. In this thesis, I investigate two type of information that existing recommender systems are able to leverage, \textit{i.e.} the structural and sequential information. The former is focused in the interactions between users and items, where we construct graphs to characterize the underlying patterns. The latter incorporate interactions in chronological orders, which can thus reflect their sequential correlations. I present two papers associated with the structural information, including how to design spectral graph convolution for cross-domain recommendation and how to resolve basket recommendation with graph neural networks. Moreover, I introduce two papers associated with the sequential information, including augmenting sequential recommendation via pseudo-prior items and contrastive self-supervised learning for sequential recommendation. Additionally, I discuss how to simultaneously leverage the structure information and sequential information via the temporal collaborative transformer over a temporal graph. Finally, I conclude this thesis by summarizing deep learning techniques towards structures and sequences.