Pre-trained Sequential Recommendation Framework for Zero-Shot Cross-Domain and Cross-Application Transfer

Core Concepts

This paper proposes a novel pre-trained sequential recommendation framework, PrepRec, that can achieve zero-shot cross-domain and cross-application transfer without any auxiliary information.

Abstract

The paper presents a novel pre-trained sequential recommendation framework called PrepRec that aims to address the challenge of zero-shot cross-domain and cross-application sequential recommendation without any auxiliary information.
Key highlights:

PrepRec learns universal item and sequence representations by modeling item popularity dynamics, instead of learning representations specific to each item ID or using application-dependent auxiliary information.
The universal item representations are learned based on the changes in item popularity over different time horizons (long-term and short-term).
A popularity dynamics-aware transformer architecture is proposed to learn universal sequence representations.
PrepRec can achieve zero-shot cross-domain and cross-application transfer without any auxiliary information, setting a baseline for pre-trained sequential recommenders.
Extensive experiments show that PrepRec can outperform state-of-the-art sequential recommender models, especially for long-tail items, through a simple post-hoc interpolation.

Stats

The marginal distribution of user and item activities is universal and heavy-tailed across datasets.
The popularity dynamics of items are crucial for predicting user behavior.

Quotes

None

Key Insights Distilled From

A Pre-trained Sequential Recommendation Framework

by Junting Wang... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2401.01497.pdf

Deeper Inquiries

How can the proposed popularity dynamics-aware transformer architecture be extended to capture more complex temporal patterns in user-item interactions

The popularity dynamics-aware transformer architecture can be extended to capture more complex temporal patterns in user-item interactions by incorporating additional layers or modules that focus on different aspects of temporal dynamics. One approach could be to introduce attention mechanisms that prioritize certain time intervals or trends in the popularity dynamics. By allowing the model to attend to specific patterns or changes in popularity over time, it can better capture the nuances and fluctuations in user-item interactions. Additionally, incorporating recurrent or convolutional layers can help capture sequential dependencies and local patterns in the popularity dynamics, enabling the model to learn more intricate temporal relationships.

What are the potential limitations of the zero-shot transfer approach, and how can they be addressed in future research

One potential limitation of the zero-shot transfer approach is the assumption of disjoint user and item sets between domains, which may not always hold in real-world scenarios. In practice, there may be some overlap or similarities between users or items in different domains, leading to challenges in zero-shot transfer. To address this limitation, future research could explore techniques for handling overlapping entities between domains, such as domain adaptation methods or transfer learning strategies that leverage shared information. Additionally, incorporating domain-specific regularization or fine-tuning mechanisms can help mitigate the effects of overlapping entities and improve the generalizability of the zero-shot transfer approach.

How can the insights from this work on learning universal representations be applied to other recommendation tasks beyond sequential recommendation

The insights from learning universal representations in sequential recommendation tasks can be applied to other recommendation tasks by leveraging the concept of domain-agnostic feature learning. By focusing on capturing intrinsic characteristics and patterns in user-item interactions that are independent of specific domains or applications, models can generalize better across different recommendation tasks. This approach can be extended to collaborative filtering, content-based recommendation, or hybrid recommendation systems by learning universal representations of users and items based on their inherent properties and interactions. By decoupling the representation learning process from domain-specific features, models can achieve better transferability and performance in diverse recommendation scenarios.