Preference Discerning with LLM-Enhanced Generative Retrieval

Fabian Paischer, Liu Yang, Linfeng Liu, Shuai Shao, Kaveh Hassani, Jiacheng Li, Ricky T. Q. Chen, Zhang Gabriel Li, Xiaoli Gao, Wei Shao, Xue Feng, Nima Noorshams, Sem Park, Bo Long, Hamid Eghbalzadeh

Abstract: In sequential recommendation, models recommend items based on user's interaction history. To this end, current models usually incorporate information such as item descriptions and user intent or preferences. User preferences are usually not explicitly given in open-source datasets, and thus need to be approximated, for example via large language models (LLMs). Current approaches leverage approximated user preferences only during training and rely solely on the past interaction history for recommendations, limiting their ability to dynamically adapt to changing preferences, potentially reinforcing echo chambers. To address this issue, we propose a new paradigm, namely *preference discerning*, which explicitly conditions a generative recommendation model on user preferences in natural language within its context. To evaluate *preference discerning*, we introduce a novel benchmark that provides a holistic evaluation across various scenarios, including preference steering and sentiment following. Upon evaluating current state-of-the-art methods on our benchmark, we discover that their ability to dynamically adapt to evolving user preferences is limited. To address this, we propose a new method named Mender (**M**ultimodal Prefer**en**ce **D**iscern**er**), which achieves state-of-the-art performance in our benchmark. Our results show that Mender effectively adapts its recommendation guided by human preferences, even if not observed during training, paving the way toward more flexible recommendation models.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission:

- Textual changes in Abstract and Introduction to improve clarity on Motivation and Relevance-
- More in-depth interpretation of results in Section 4.2, subcaptions added in Figures 4 and 5, defined missing symbols in Algorithm 1.
- Corrected indexing in Eq. (2), specifically changing $\arg \max_{p \in P_u^{(t)}}$ to $\arg \max_{p \in P_u^{(t-1)}}$ to clarify that there is no information leak for the recommendation task.
- Added missing references to Table 2 in the Appendix.
- Added a more elaborate section on current limitations of our work (Section 5).

Code: https://github.com/facebookresearch/preference_discerning

Assigned Action Editor: ~Jundong_Li2

Submission Number: 4336