USER: A Unified Information Search and Recommendation Model based on Integrated Behavior Sequence

TL;DR

This paper introduces USER, a unified model that jointly handles search and recommendation tasks by leveraging integrated user behavior sequences to improve overall user satisfaction.

Contribution

It proposes a novel joint modeling approach that combines search and recommendation tasks using a unified behavior sequence, enhancing their interrelated performance.

Findings

Improved user satisfaction through joint modeling.

Effective mining of user interests from integrated sequences.

Unified approach outperforms separate models.

Abstract

Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal -- satisfying the user's information need at the right time. There are already a lot of Internet platforms and Apps providing both search and recommendation services, showing us the demand and opportunity to simultaneously handle both tasks. However, most platforms consider these two tasks independently -- they tend to train separate search model and recommendation model, without exploiting the relatedness and dependency between them. In this paper, we argue that jointly modeling these two tasks will benefit both of them and finally improve overall user satisfaction. We investigate the interactions between these two tasks in the specific information content service domain. We propose first integrating the user's behaviors in search and recommendation into a heterogeneous behavior sequence, then utilizing a joint model for handling both tasks based on the unified sequence. More specifically, we design the Unified Information Search and Recommendation model (USER), which mines user interests from the integrated sequence and accomplish the two tasks in a unified way.