Exploiting Positional Information for Session-based Recommendation

TL;DR

This paper introduces a dual positional encoding scheme and a position-aware neural network model to better utilize positional information in session-based recommendation, improving accuracy over existing methods.

Contribution

It develops a theoretical framework for analyzing positional encoding schemes and proposes a novel PosRec model with dual positional encoding for enhanced session-based recommendations.

Findings

PosRec outperforms state-of-the-art models on Yoochoose and Diginetica datasets.

Existing positional encoding schemes are mainly forward-aware, limiting their ability to capture session dynamics.

The dual positional encoding effectively captures both initial and latest user intentions.

Abstract

For present e-commerce platforms, session-based recommender systems are developed to predict users' preference for next-item recommendation. Although a session can usually reflect a user's current preference, a local shift of the user's intention within the session may still exist. Specifically, the interactions that take place in the early positions within a session generally indicate the user's initial intention, while later interactions are more likely to represent the latest intention. Such positional information has been rarely considered in existing methods, which restricts their ability to capture the significance of interactions at different positions. To thoroughly exploit the positional information within a session, a theoretical framework is developed in this paper to provide an in-depth analysis of the positional information. We formally define the properties of forward-awareness and backward-awareness to evaluate the ability of positional encoding schemes in capturing the initial and the latest intention. According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session. To enhance the positional encoding scheme for the session-based recommendation, a dual positional encoding (DPE) is proposed to account for both forward-awareness and backward-awareness. Based on DPE, we propose a novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module to fully exploit the positional information for session-based recommendation tasks. Extensive experiments are conducted on two e-commerce benchmark datasets, Yoochoose and Diginetica and the experimental results show the superiority of the PosRec by comparing it with the state-of-the-art session-based recommender models.