Language Representations Can be What Recommenders Need: Findings and Potentials
Leheng Sheng An Zhang Yi Zhang Yuxin Chen Xiang Wang Tat-Seng Chua
Recent studies empirically indicate that language models (LMs) encode rich world knowledge beyond mere semantics, attracting significant attention across various fields.
However, in the recommendation domain, it remains uncertain whether LMs implicitly encode user preference information. Contrary to prevailing understanding that LMs and traditional recommenders learn two distinct representation spaces due to the huge gap in language and behavior modeling objectives, this work re-examines such understanding and explores extracting a recommendation space directly from the language representation space.
Surprisingly, our findings demonstrate that item representations, when linearly mapped from advanced LM representations, yield superior recommendation performance.
This outcome suggests the possible homomorphism between the advanced language representation space and an effective item representation space for recommendation, implying that collaborative signals may be implicitly encoded within LMs.
Motivated by the finding of homomorphism, we explore the possibility of designing advanced collaborative filtering (CF) models purely based on language representations without ID-based embeddings.
To be specific, we incorporate several crucial components (i.e., a multilayer perceptron (MLP), graph convolution, and contrastive learning (CL) loss function) to build a simple yet effective model, with the language representations of item textual metadata (i.e., title) as the input.
Empirical results show that such a simple model can outperform leading ID-based CF models on multiple datasets, which sheds light on using language representations for better recommendation.
Moreover, we systematically analyze this simple model and find several key features for using advanced language representations:
a good initialization for item representations, superior zero-shot recommendation abilities in new datasets, and being aware of user intention.
Our findings highlight the connection between language modeling and behavior modeling, which can inspire both natural language processing and recommender system communities.
Collaborative filtering, Language-representation-based recommendation, Language models, Language model representations