  Large language models have been flourishing in the natural language
processing (NLP) domain, and their potential for recommendation has been paid
much attention to. Despite the intelligence shown by the
recommendation-oriented finetuned models, LLMs struggle to fully understand the
user behavior patterns due to their innate weakness in interpreting numerical
features and the overhead for long context, where the temporal relations among
user behaviors, subtle quantitative signals among different ratings, and
various side features of items are not well explored. Existing works only
fine-tune a sole LLM on given text data without introducing that important
information to it, leaving these problems unsolved. In this paper, we propose
ELCoRec to Enhance Language understanding with CoPropagation of numerical and
categorical features for Recommendation. Concretely, we propose to inject the
preference understanding capability into LLM via a GAT expert model where the
user preference is better encoded by parallelly propagating the temporal
relations, and rating signals as well as various side information of historical
items. The parallel propagation mechanism could stabilize heterogeneous
features and offer an informative user preference encoding, which is then
injected into the language models via soft prompting at the cost of a single
token embedding. To further obtain the user's recent interests, we proposed a
novel Recent interaction Augmented Prompt (RAP) template. Experiment results
over three datasets against strong baselines validate the effectiveness of
ELCoRec. The code is available at
https://anonymous.4open.science/r/CIKM_Code_Repo-E6F5/README.md.
