Learning to Recommend with Multiple Cascading Behaviors

Abstract

Most existing recommender systems leverage user behavior data of one type only, such as the purchase behavior in E-commerce that is directly related to the business KPI (Key Performance Indicator) of conversion rate. Besides the key behavioral data, we argue that other forms of user behaviors also provide valuable signal, such as views, clicks, adding a product to shop carts and so on. They should be taken into account properly to provide quality recommendation for users. In this work, we contribute a new solution named NMTR (short for Neural Multi-Task Recommendation) for learning recommender systems from user multi-behavior data. We develop a neural network model to capture the complicated and multi-type interactions between users and items. In particular, our model accounts for the cascading relationship among different types of behaviors (e.g., a user must click on a product before purchasing it). To fully exploit the signal in the data of multiple types of behaviors, we perform a joint optimization based on the multi-task learning framework, where the optimization on a behavior is treated as a task. Extensive experiments on two real-world datasets demonstrate that NMTR significantly outperforms state-of-the-art recommender systems that are designed to learn from both single-behavior data and multi-behavior data. Further analysis shows that modeling multiple behaviors is particularly useful for providing recommendation for sparse users that have very few interactions.

Published in IEEE Transactions on Knowledge and Data Engineering (TKDE)

References in corpus (7)

• Neural Graph Collaborative Filtering
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks
• Ask the GRU: Multi-Task Learning for Deep Text Recommendations
• Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews
• MMALFM: Explainable Recommendation by Leveraging Reviews and Images
• Item Silk Road: Recommending Items from Information Domains to Social Users

Cited by in corpus (5)

• Multi-Behavior Recommendation with Cascading Graph Convolution Networks
• Multi-Behavior Graph Neural Networks for Recommender System
• Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation
• Incorporating Heterogeneous User Behaviors and Social Influences for Predictive Analysis
• User Invariant Preference Learning for Multi-Behavior Recommendation