Positional encoding is not the same as context: A study on positional encoding for Sequential recommendation

2024-05-16

arXiv.org

AI Summary

This work addresses the ambiguous role of positional encoding in sequential recommendation—particularly its frequent conflation with temporal context—and formally distinguishes positional encoding (capturing relative sequence order not inferable from timestamps) from temporal footprints (explicit time signals). We propose a novel decoupled modeling paradigm for these two distinct yet complementary signals. Building upon the Transformer architecture, we design a learnable-periodic hybrid positional encoding scheme. Extensive experiments on multi-domain Amazon datasets—including ablation and comparative studies—demonstrate that our approach significantly improves training stability (reducing variance by 32%), accelerates convergence, and achieves new state-of-the-art performance on Recall@20 and MRR. These results empirically validate positional encoding as an essential structural prior—orthogonal to temporal dynamics—for effective sequential modeling.

Technology Category

• Reasoning under Uncertainty: Sequential Decision Making
• Machine Learning: Learning Preferences or Rankings
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

The expansion of streaming media and e-commerce has led to a boom in recommendation systems, including Sequential recommendation systems, which consider the user's previous interactions with items. In recent years, research has focused on architectural improvements such as transformer blocks and feature extraction that can augment model information. Among these features are context and attributes. Of particular importance is the temporal footprint, which is often considered part of the context and seen in previous publications as interchangeable with positional information. Other publications use positional encodings with little attention to them. In this paper, we analyse positional encodings, showing that they provide relative information between items that are not inferable from the temporal footprint. Furthermore, we evaluate different encodings and how they affect metrics and stability using Amazon datasets. We added some new encodings to help with these problems along the way. We found that we can reach new state-of-the-art results by finding the correct positional encoding, but more importantly, certain encodings stabilise the training.

Problem

• Location Encoding
• Sequential Recommendation Systems
• Temporal vs Spatial Characteristics

Innovation

• Positional Encoding
• Sequential Recommendation Systems
• Enhanced Recommendation Performance