RDR ec: Rationale Distillation for LLM -based Recommendation (2024)

Abstract: Large language model (LLM)-based recommender models that bridge users and items through textual prompts for effective semantic reasoning have gained considerable attention. However, few methods consider the underlying rationales behind interactions, such as user preferences and item attributes, limiting the reasoning ability of LLMs for recommendations. This paper proposes a rationale distillation recommender (RDRec), a compact model designed to learn rationales generated by a larger language model (LM). By leveraging rationales from reviews related to users and items, RDRec remarkably specifies their profiles for recommendations. Experiments show that RDRec achieves state-of-the-art (SOTA) performance in both top-N and sequential recommendations. Our code is available online.

## introduction large language models (llms) with powerful reasoning capabilities have been extensively studied for recommendations, including news and item recommendations @xcite @xcite , explainable recommendations @xcite , and zero-/few-shot and cold-start recommendations @xcite . several attempts have leveraged knowledge of llms to improve recommendation performance, such as enhancing embedding initialization @xcite , reranking candidates @xcite , and learning representation @xcite @xcite . a straightforward approach is to integrate user and item ids into lms through prompt learning @xcite , including discrete prompts to find alternative words to represent ids, continuous prompts to directly feed id vectors into a pre-trained model @xcite , and hybrid prompts @xcite . @xcite present a p5 paradigm to transform user-item interactions, user sequential behaviors, and reviews into text-to-text prompts for llms. this enables p5 to capture deeper semantics for llm-based recommendations. @xcite enhance p5 by a prompt distillation, resulting in significant improvement and reductions in inference time. however, they pay no attention to mining the rationale behind each interaction, such as user preferences and item attributes, which hampers the reasoning capabilities of llms. as an example, in fig. 1 (a), a user review for an item says: "it was pretty fun and cool since we had to change our strategy (user preference) to try to prevent her from playing intrigue cards (item attributes)." the user prefers strategic thinking in the game, and intrigue cards symbolize item characteristics. this introduces noise into the user's profile, as the user leans towards a strategic game rather than merely cards. this suggests that the original review without intermediate prompts prevents the model from learning to understand the rationale behind the interaction. the chain-of-thought (cot) prompting @xcite ) that promises llms to decompose intermediate rationales, has been widely applied for rationale extraction @xcite @xcite . more recently, @xcite utilize the cot prompting to distill rationales via llms to train smaller models. inspired by this, we propose a compact recommender model to learn the interaction rationales, i.e., user preferences and item attributes, distilled from reviews using a larger lm. in this way, the model acquires clear textual knowledge with less noise (e.g., "intrigue cards" that may hinder understanding the user's preference for "strategic games" in fig. 1 (b) ). this enables the model to derive more specified user and item profiles from all reviews given by the user or regarding the item for recommendations, as illustrated in fig. 1 (cd) . the main contributions of this paper can be summarized as follows. (1) we propose a compact rdrec model that effectively specifies user and item profiles by distilling interaction rationales from relevant reviews using a larger lm, and (2) rdrec consistently outperforms sota baselines on three real-world datasets in both sequential and top-n recommendations. ## rdrec framework we present an rdrec model consisting of two stages, an interaction rationale distillation and a rationale-aware recommendation. ## conclusion we proposed a compact rdrec model to learn the underlying rationales for interactions generated by a larger lm. by learning rationales from all related reviews, rdrec effectively specifies user and item profiles for recommendations. experimental results showed the effectiveness of our rdrec. future work involves (i) exploring better prompts for sequential recommendations, and (ii) enhancing explanation generation in rdrec.