Everyone's Preference Changes Differently: Weighted Multi-Interest Retrieval Model

TL;DR

This paper introduces the Multi-Interest Preference (MIP) model that effectively captures user interests with multiple embeddings and dynamically learns their importance over time, improving recommendation retrieval.

Contribution

The MIP model advances user representation by modeling multiple interests and automatically learning their weights, addressing limitations of previous methods.

Findings

MIP improves recall in candidate retrieval tasks.

The model outperforms existing approaches on industrial-scale datasets.

Dynamic interest weighting enhances recommendation accuracy.

Abstract

User embeddings (vectorized representations of a user) are essential in recommendation systems. Numerous approaches have been proposed to construct a representation for the user in order to find similar items for retrieval tasks, and they have been proven effective in industrial recommendation systems as well. Recently people have discovered the power of using multiple embeddings to represent a user, with the hope that each embedding represents the user's interest in a certain topic. With multi-interest representation, it's important to model the user's preference over the different topics and how the preference change with time. However, existing approaches either fail to estimate the user's affinity to each interest or unreasonably assume every interest of every user fades with an equal rate with time, thus hurting the recall of candidate retrieval. In this paper, we propose the Multi-Interest Preference (MIP) model, an approach that not only produces multi-interest for users by using the user's sequential engagement more effectively but also automatically learns a set of weights to represent the preference over each embedding so that the candidates can be retrieved from each interest proportionally. Extensive experiments have been done on various industrial-scale datasets to demonstrate the effectiveness of our approach.