Quaternion-Based Self-Attentive Long Short-Term User Preference Encoding for Recommendation
This work addresses the challenge of improving recommendation accuracy for users by introducing a novel quaternion-based method, though it appears incremental as it builds on existing self-attentive and adversarial learning techniques.
The paper tackled the problem of modeling user preferences in recommender systems by proposing a quaternion-based approach to encode both long-term and short-term interests, resulting in a fused model that outperformed 11 state-of-the-art baselines with average improvements of 8.43% at HIT@1 and 10.27% at NDCG@1.
Quaternion space has brought several benefits over the traditional Euclidean space: Quaternions (i) consist of a real and three imaginary components, encouraging richer representations; (ii) utilize Hamilton product which better encodes the inter-latent interactions across multiple Quaternion components; and (iii) result in a model with smaller degrees of freedom and less prone to overfitting. Unfortunately, most of the current recommender systems rely on real-valued representations in Euclidean space to model either user's long-term or short-term interests. In this paper, we fully utilize Quaternion space to model both user's long-term and short-term preferences. We first propose a QUaternion-based self-Attentive Long term user Encoding (QUALE) to study the user's long-term intents. Then, we propose a QUaternion-based self-Attentive Short term user Encoding (QUASE) to learn the user's short-term interests. To enhance our models' capability, we propose to fuse QUALE and QUASE into one model, namely QUALSE, by using a Quaternion-based gating mechanism. We further develop Quaternion-based Adversarial learning along with the Bayesian Personalized Ranking (QABPR) to improve our model's robustness. Extensive experiments on six real-world datasets show that our fused QUALSE model outperformed 11 state-of-the-art baselines, improving 8.43% at HIT@1 and 10.27% at NDCG@1 on average compared with the best baseline.