IntentRec: Predicting User Session Intent with Hierarchical Multi-Task Learning
This work addresses the challenge of providing accurate personalized recommendations in digital services like e-commerce and streaming, but it appears incremental as it builds on existing multi-task learning approaches.
The paper tackles the problem of predicting user intent in sessions to improve recommendation quality, introducing IntentRec, a hierarchical multi-task neural network framework that uses implicit signals for intent estimation and next-item prediction, and it outperforms state-of-the-art methods on Netflix data.
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.