  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 the
prevailing understanding that LMs and traditional recommender models learn two
distinct representation spaces due to a huge gap in language and behavior
modeling objectives, this work rethinks 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 homomorphism between the language
representation space and an effective recommendation space, implying that
collaborative signals may indeed be encoded within advanced LMs. Motivated by
these findings, we propose a simple yet effective collaborative filtering (CF)
model named AlphaRec, which utilizes language representations of item textual
metadata (e.g., titles) instead of traditional ID-based embeddings.
Specifically, AlphaRec is comprised of three main components: a multilayer
perceptron (MLP), graph convolution, and contrastive learning (CL) loss
function, making it extremely easy to implement and train. Our empirical
results show that AlphaRec outperforms leading ID-based CF models on multiple
datasets, marking the first instance of such a recommender with text embeddings
achieving this level of performance. Moreover, AlphaRec introduces a new
language-representation-based CF paradigm with several desirable advantages:
being easy to implement, lightweight, rapid convergence, superior zero-shot
recommendation abilities in new domains, and being aware of user intention.
