Advances in Collaborative Filtering and Ranking

Abstract

In this dissertation, we cover some recent advances in collaborative filtering and ranking. In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chapter 2 we first talk about pointwise collaborative filtering problem with graph information, and how our proposed new method can encode very deep graph information which helps four existing graph collaborative filtering algorithms; chapter 3 is on the pairwise approach for collaborative ranking and how we speed up the algorithm to near-linear time complexity; chapter 4 is on the new listwise approach for collaborative ranking and how the listwise approach is a better choice of loss for both explicit and implicit feedback over pointwise and pairwise loss; chapter 5 is about the new regularization technique Stochastic Shared Embeddings (SSE) we proposed for embedding layers and how it is both theoretically sound and empirically effectively for 6 different tasks across recommendation and natural language processing; chapter 6 is how we introduce personalization for the state-of-the-art sequential recommendation model with the help of SSE, which plays an important role in preventing our personalized model from overfitting to the training data; chapter 7, we summarize what we have achieved so far and predict what the future directions can be; chapter 8 is the appendix to all the chapters.

PhD Dissertation 2020

References in corpus (11)

• Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
• Semi-Supervised Classification with Graph Convolutional Networks
• Improving neural networks by preventing co-adaptation of feature detectors
• Empirical Analysis of Predictive Algorithms for Collaborative Filtering
• BPR: Bayesian Personalized Ranking from Implicit Feedback
• On the difficulty of training Recurrent Neural Networks
• Graph Convolutional Matrix Completion
• Translation-based Recommendation
• Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons
• Preference Completion from Partial Rankings
• Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering