Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights

Understand

Text-based collaborative filtering (TCF) has emerged as the prominent technique for text and news recommendation, employing language models (LMs) as text encoders to represent items.

• However, the current landscape of TCF models mainly relies on the utilization of relatively small or medium-sized LMs.
• The potential impact of using larger, more powerful language models (such as these with over 100 billion parameters) as item encoders on recommendation performance remains uncertain.
• Can we anticipate unprecedented results and discover new insights? To address this question, we undertake a comprehensive series of experiments aimed at exploring the performance limits of the TCF paradigm.