Neural Attentive Session-based Recommendation

TL;DR

This paper introduces NARM, a neural network model with attention mechanisms for session-based recommendation, effectively capturing user intent and behavior to improve recommendations, especially in long sessions.

Contribution

The paper proposes a novel neural network framework, NARM, that models user behavior and main purpose using attention, outperforming existing methods on benchmark datasets.

Findings

NARM outperforms state-of-the-art baselines.

Significant improvement on long sessions.

Effective modeling of user intent and behavior.

Abstract

Given e-commerce scenarios that user profiles are invisible, session-based recommendation is proposed to generate recommendation results from short sessions. Previous work only considers the user's sequential behavior in the current session, whereas the user's main purpose in the current session is not emphasized. In this paper, we propose a novel neural networks framework, i.e., Neural Attentive Recommendation Machine (NARM), to tackle this problem. Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later. We then compute the recommendation scores for each candidate item with a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental results show that NARM outperforms state-of-the-art baselines on both datasets. Furthermore, we also find that NARM achieves a significant improvement on long sessions, which demonstrates its advantages in modeling the user's sequential behavior and main purpose simultaneously.