Agentic Recommender Systems: A Systematic Literature Review

Recommender systems (RSs) are software systems that use machine learning techniques to suggest items, such as movies, products or routes to users based on input provided over time. These systems have been widely used by companies, such as Amazon, Google, and Netflix, who rely heavily on recommendations as a core element of their interaction with users. In the case of software engineering, RSs are used to recommend, for example, software tasks, developers, APIs, libraries, and bug fixes. More recently, advancements in natural language processing and large language models (LLMs) have led to the creation of LLM agents capable of generating plans, interacting with users and the environment, receiving feedback, and using tools. These developments have resulted in the use of LLM agents in RSs, giving rise to multi-agent RSs referred to as agentic recommender systems (ARSs). However, agentic RSs are not yet well characterized in terms of the basic elements of system modeling and design. This systematic literature review characterizes agentic recommender systems with respect to agents, roles, relationships, prompts, integration, use cases, strategies and evaluation methods. An analysis of published studies yields insights, such as (i) the identification of 13 distinct types of agents, (ii) the popularity of GPT models, (iii) a framework for ARSs, (iv) a generalized prompt, (v) the use of persona, cues, output format, and zero-shot prompting, (vi) the frequent use of Amazon, Yelp, and MovieLens datasets, (vii) the use of nDCG@k and Recall@k as common evaluation metrics, and (viii) the identification of many research gaps. This systematic literature review aims to support the continued research and development of agentic recommender systems.