A Theoretical Analysis of Two-Stage Recommendation for Cold-Start Collaborative Filtering

TL;DR

This paper develops a theoretical framework for two-stage cold-start recommendation in collaborative filtering, balancing exploration and exploitation, and proposes an approximate solution validated on synthetic and real datasets.

Contribution

It introduces a POMDP-based model for cold-start recommendation and derives an approximate solution based on resource relevance and correlation analysis.

Findings

Exact solution modeled as selecting highly relevant and correlated resources.

Approximate solution reduces computational complexity.

Initial experiments show performance improvements on synthetic and real data.

Abstract

In this paper, we present a theoretical framework for tackling the cold-start collaborative filtering problem, where unknown targets (items or users) keep coming to the system, and there is a limited number of resources (users or items) that can be allocated and related to them. The solution requires a trade-off between exploitation and exploration as with the limited recommendation opportunities, we need to, on one hand, allocate the most relevant resources right away, but, on the other hand, it is also necessary to allocate resources that are useful for learning the target's properties in order to recommend more relevant ones in the future. In this paper, we study a simple two-stage recommendation combining a sequential and a batch solution together. We first model the problem with the partially observable Markov decision process (POMDP) and provide an exact solution. Then, through an in-depth analysis over the POMDP value iteration solution, we identify that an exact solution can be abstracted as selecting resources that are not only highly relevant to the target according to the initial-stage information, but also highly correlated, either positively or negatively, with other potential resources for the next stage. With this finding, we propose an approximate solution to ease the intractability of the exact solution. Our initial results on synthetic data and the Movie Lens 100K dataset confirm the performance gains of our theoretical development and analysis.