P ep R ec: Progressive Enhancement of Prompting for Recommendation (2024)

Abstract: With large language models (LLMs) achieving remarkable breakthroughs in natural language processing (NLP) domains, recent researchers have actively explored the potential of LLMs for recommendation systems by converting the input data into textual sentences through prompt templates. Although semantic knowledge from LLMs can help enrich the content information of items, to date it is still hard for them to achieve comparable performance to traditional deep learning recommendation models, partly due to a lack of ability to leverage collaborative filtering. In this paper, we propose a novel training-free prompting framework, PepRec, which aims to capture knowledge from both content-based filtering and collaborative filtering to boost recommendation performance with LLMs, while providing interpretation for the recommendation. Experiments based on two real-world datasets from different domains show that PepRec significantly outperforms various traditional deep learning recommendation models and prompt-based recommendation systems.

## introduction recommendation systems, which aim to estimate the likelihood of a user interacting with an item, have been widely deployed in the real world, including e-commerce @xcite , social media platforms @xcite , and news aggregators @xcite . deep learningbased recommendation models (dlrms) have been the cornerstone and achieved state-of-the-art performance in recommendation, leveraging a wide array of user and item attributes, including both categorical and numerical features @xcite @xcite . recently, large language models (llms) have been particularly transformative, achieving unparalleled performance across various nlp tasks @xcite @xcite @xcite . they are usually pretrained on a large corpus of text and thus can offer rich semantic knowledge to help improve recommendations via content-based filtering. for example, two movies "oppenheimer" and "the dark knight" from a user's item-interacted history, despite having completely different film names and genres -where the first is a biographical thriller film and the latter is a superhero film -can have some hidden commonality as they are both directed by christopher nolan. this commonality, among other hidden common features, can be obtained from llms and be used to help recommend if the user may watch another new movie sharing the same content characteristic. in addition, llms can provide explanations for recommendations to enhance transparency and even do not require model training compared to dlrms. recent research has explored the use of llms in recommendation systems @xcite @xcite , usually converting the categorical features into textual sentences through prompt templates and feeding these sentences into llms to generate a prediction. for example, llm4rs @xcite proposes three types of prompts for point-wise, pair-wise, and list-wise ranking, respectively, to explore llm capability in recommendation. however, prompts to llms are designed heuristically in existing works only based on a user's historical interactions with items, and are unable to achieve results comparable to traditional dlrms. part of the reason is that llms, while offering content analysis and filtering capability, do not perform any collaborative filtering between users (i.e., recommending a user the items on the basis of the preferences of other users with similar tastes) like conventional dlrms would do. though some works @xcite propose to train a dlrm and an llm concurrently in a unified framework for alignment to solve this issue, they usually come with pretrain-ing and fine-tuning, which are computational-costly and time-consuming. in contrast, we propose a training-free progressive enhancement of prompting (peprec) framework to achieve outstanding recommendation performance with llms, by introducing a novel method to progressively discover a nuanced understanding of a user's likes and dislikes. peprec distinguishes itself from prior prompt-based recommendations by enabling both fine-grained semantic knowledge mining from a user and collaborative filtering from other similar users to improve the performance of the recommendations. it iteratively recognizes and leverages the collective preferences of similar user cohorts, thereby enriching individual users' likes and dislikes with broader communal insights. on the other hand, compared to dlrm-based recommendation, peprec inherits the strengths of llms, particularly their ability to extract and summarize hidden content traits from items, which not only helps boost the prediction but also makes recommendation outcome humancomprehensible. our contributions through this work are threefold: our findings with peprec underscore the potential of llms to significantly boost recommendation performance over various state-of-the-art recommendation methods, which to the best of our knowledge, is not achieved by the prior literature. in the meantime, peprec provides intuitive and humanunderstood explanations for recommendation outcomes, thereby adding a layer of transparency to recommendation process, while maintaining operational efficiency (characterized by reduced training requirements and minimal training parameters). ## related work in this section, we review the existing related work on dlrms and prompt learning. ## method in this section, we introduce the details of our proposed peprec framework, including an in-depth overview, and its core components. ## experiment in this section, we present the experimental details, including datasets, evaluation metrics, baseline models, implementation details, and experimental results in order to answer the following research questions: ## conclusion this study proposes a progressive enhancementof-prompting framework for recommendation, peprec, as a pioneering approach for making accurate and explainable recommendation predictions. peprec can capture the user-item dynamics from both content-based filtering and collaborative filtering to emulate human-like decision-making in recommendations, which is achieved by progressively analyzing and synthesizing user preferences in conjunction with insights gathered from similar users' item-interaction histories. the extensive experimental results show that peprec significantly outperforms various traditional dlrms and promptbased models. the enhanced prompt also demonstrates its capability to deliver personalized and explainable recommendations. furthermore, our proposed peprec maintains operational efficiency (reduced training requirements and minimal training parameters) compared to existing baselines.