Learning from History and Present: Next-item Recommendation via Discriminatively Exploiting User Behaviors

TL;DR

This paper introduces BINN, a neural network model that leverages both long-term user preferences and short-term behaviors to improve next-item recommendations in e-commerce, demonstrating superior performance over existing methods.

Contribution

The paper proposes a novel Behavior-Intensive Neural Network (BINN) that integrates historical preferences and current motivations for enhanced recommendation accuracy.

Findings

BINN outperforms state-of-the-art methods on real-world datasets.

The new item embedding method effectively captures user interactions.

Discriminative learning of behaviors improves recommendation relevance.

Abstract

In the modern e-commerce, the behaviors of customers contain rich information, e.g., consumption habits, the dynamics of preferences. Recently, session-based recommendations are becoming popular to explore the temporal characteristics of customers' interactive behaviors. However, existing works mainly exploit the short-term behaviors without fully taking the customers' long-term stable preferences and evolutions into account. In this paper, we propose a novel Behavior-Intensive Neural Network (BINN) for next-item recommendation by incorporating both users' historical stable preferences and present consumption motivations. Specifically, BINN contains two main components, i.e., Neural Item Embedding, and Discriminative Behaviors Learning. Firstly, a novel item embedding method based on user interactions is developed for obtaining an unified representation for each item. Then, with the embedded items and the interactive behaviors over item sequences, BINN discriminatively learns the historical preferences and present motivations of the target users. Thus, BINN could better perform recommendations of the next items for the target users. Finally, for evaluating the performances of BINN, we conduct extensive experiments on two real-world datasets, i.e., Tianchi and JD. The experimental results clearly demonstrate the effectiveness of BINN compared with several state-of-the-art methods.