IntentRec: Predicting User Session Intent with Hierarchical Multi-Task Learning

TL;DR

IntentRec is a hierarchical multi-task neural network framework that predicts user session intent from implicit signals to enhance personalized recommendations, outperforming existing methods on Netflix data.

Contribution

Introduces IntentRec, a novel hierarchical multi-task neural network architecture for user intent prediction to improve recommendation accuracy.

Findings

Outperforms state-of-the-art next-item and next-intent predictors.

Effectively leverages implicit signals for intent estimation.

Enables downstream applications with improved recommendation quality.

Abstract

Recommender systems have played a critical role in diverse digital services such as e-commerce, streaming media, social networks, etc. If we know what a user's intent is in a given session (e.g. do they want to watch short videos or a movie or play games; are they shopping for a camping trip), it becomes easier to provide high-quality recommendations. In this paper, we introduce IntentRec, a novel recommendation framework based on hierarchical multi-task neural network architecture that tries to estimate a user's latent intent using their short- and long-term implicit signals as proxies and uses the intent prediction to predict the next item user is likely to engage with. By directly leveraging the intent prediction, we can offer accurate and personalized recommendations to users. Our comprehensive experiments on Netflix user engagement data show that IntentRec outperforms the state-of-the-art next-item and next-intent predictors. We also share several findings and downstream applications of IntentRec.