Authors: Xubin Ren, Chao Huang
Abstract: Deep neural networks have become a powerful technique for learning
representations from user-item interaction data in collaborative filtering (CF)
for recommender systems. However, many existing methods heavily rely on unique
user and item IDs, which limits their ability to perform well in practical
zero-shot learning scenarios where sufficient training data may be unavailable.
Inspired by the success of language models (LMs) and their strong
generalization capabilities, a crucial question arises: How can we harness the
potential of language models to empower recommender systems and elevate its
generalization capabilities to new heights? In this study, we propose EasyRec –
an effective and easy-to-use approach that seamlessly integrates text-based
semantic understanding with collaborative signals. EasyRec employs a
text-behavior alignment framework, which combines contrastive learning with
collaborative language model tuning, to ensure a strong alignment between the
text-enhanced semantic space and the collaborative behavior information.
Extensive empirical evaluations across diverse real-world datasets demonstrate
the superior performance of EasyRec compared to state-of-the-art alternative
models, particularly in the challenging text-based zero-shot recommendation
scenarios. Furthermore, the study highlights the potential of seamlessly
integrating EasyRec as a plug-and-play component into text-enhanced
collaborative filtering frameworks, thereby empowering existing recommender
systems to elevate their recommendation performance and adapt to the evolving
user preferences in dynamic environments. For better result reproducibility of
our EasyRec framework, the model implementation details, source code, and
datasets are available at the link: https://github.com/HKUDS/EasyRec.
Source: http://arxiv.org/abs/2408.08821v1
