Advances in Collaborative Filtering and Ranking

TL;DR

This dissertation presents recent advances in collaborative filtering and ranking, introducing new methods for graph-based filtering, efficient ranking algorithms, and a novel regularization technique to improve personalization and prevent overfitting.

Contribution

It introduces a new deep graph encoding method, a near-linear time pairwise ranking algorithm, a listwise loss approach, and the Stochastic Shared Embeddings regularization technique for improved recommendation systems.

Findings

Deep graph encoding improves existing algorithms

Near-linear time collaborative ranking achieved

SSE regularization enhances personalization and reduces overfitting

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.