Multi-Task Determinantal Point Processes for Recommendation

TL;DR

This paper introduces a multi-task DPP model tailored for basket completion in recommendation systems, leveraging tensor factorization to improve diversity and accuracy over existing models.

Contribution

The paper proposes a novel multi-task DPP model that enhances basket completion by integrating tensor factorization and multi-class classification techniques.

Findings

Multi-task DPP outperforms state-of-the-art models in predictive accuracy.

The model effectively captures set diversity and item quality.

Experimental results on real datasets demonstrate significant improvements.

Abstract

Determinantal point processes (DPPs) have received significant attention in the recent years as an elegant model for a variety of machine learning tasks, due to their ability to elegantly model set diversity and item quality or popularity. Recent work has shown that DPPs can be effective models for product recommendation and basket completion tasks. We present an enhanced DPP model that is specialized for the task of basket completion, the multi-task DPP. We view the basket completion problem as a multi-class classification problem, and leverage ideas from tensor factorization and multi-class classification to design the multi-task DPP model. We evaluate our model on several real-world datasets, and find that the multi-task DPP provides significantly better predictive quality than a number of state-of-the-art models.