# Spars of Artificial General Recommender (AGR): Experiments with ChatGPT
## 1 Introduction  
Recommender systems have achieved pervasive prominence across diverse digital applications in the aim of optimizing user efficacy and experience, encompassing areas such as product endorsements, curated video selections, aggregated news content, tailored job recommendations, and more. A multitude of models have been developed on such areas. These encompass conventional approaches that capitalize on methodologies such as Collaborative Filtering and Matrix Factorization [1; 2], as well as more contemporary endeavors that formulate diverse neural network architectures [3; 4; 5].  
With the recent progress in Large Language Models (LLMs) [6; 7; 8; 9; 10], researchers have initiated investigations into the potential deployment of LLMs as recommender systems or to leverage their capabilities for enhancing existing ones. A number of pioneering works have been proposed. Ref. [11] presents a novel LLM-empowered recommender system by initially processing user inputs using a prompt constructor that combines user information and dialogue history, followed by feeding the processed input to ChatGPT in order to generate item recommendations from a predefined list of candidates. Ref. [12] presents a comparable framework that employs ChatGPT for item recommendations, utilizing meticulously crafted prompts tailored for both zero-shot and few-shot scenarios. In zero-shot scenarios, solely the user's purchase history or metadata is present in the prompt, while in few-shot instances, in-context learning is also applied to provide the system with examples of which item the user chooses next, given his/her current purchase history. Ref. [13] demonstrates the utilization of an LLM to enhance the data preparation process across various phases of recommendation modeling, such as the use of LLM to enhance supplementary textual features derived from the user's initial input. Refs. [14; 15] provide comprehensive insights into the methodologies for executing prompting, instruction-tuning, and prompt tuning specifically tailored for recommender systems based on LLMs, alongside models for each respective approach.  
As evidenced by these investigations, Large Language Models (LLMs) exhibit substantial promise within the domain of recommendations, showcasing the ability to achieve astate-of-the-art performance on a range of recommendation tasks, as measured by common metrics such as Hit Rate and NDCG. Building upon such encouraging outcomes, our perspective work aims to expand the research endeavor by exploring the potential for an LLM to function as an Artificial General Recommender (AGR) without downstream fine-tuning. We define an AGR to encompass two primary aspects: (1) Conversationality, implying the ability to engage in interactive recommendation-oriented dialogues with users, and (2) Universality, signifying the ability to execute a multitude of tasks and generate personalized recommendations for a variety of item domains. In this study, we aim to establish the fundamental principles to which an AGR should adhere to based on its definition. Subsequently, we assess the capacity of ChatGPT, a sophisticated LLM, to serve as an AGR by employing instance-based testing protocols designed for each principle, drawing from the insights of Microsoft's reflective paper on GPT testings [16].  
While prior works primarily concentrate on recommendation outputs with metric-based performance evaluations, we focus on evaluating the quality of the interactive dialogues generated for recommendations from various aspects. We hope the principles and dialogue instances discussed in our work can be viewed as a starting point for other researchers, guiding them to conduct user-centric studies or surveys that could further quantify an LLM's efficacy as an AGR.