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User Intention Generation with Large Language Models Using Chain-of-Thought Prompting Title
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One of the techniques for personalized recommendation is to identify the intention. Traditional user intention identification uses the user鈥檚 selection when facing multiple items. This modeling relies primarily on historical behaviour data resulting in challenges such as the cold start, unintended choice, and failure to capture intention when items are new. Motivated by recent advancements in Large Language Models (LLMs) like ChatGPT, we present an approach for user intention identification by embracing LLMs with Chain-of-Thought (CoT) prompting. We use the initial user profile as input to LLMs and design a collection of prompts to align the LLM's response through various recommendation tasks encompassing rating prediction, search and browse history, user clarification, etc. Our tests on real-world datasets demonstrate the improvements in recommendation by explicit user intention identification and, with that intention, merged into a user model. <iframe src="https://publications.waset.org/abstracts/185916.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=personalized%20recommendation" title="personalized recommendation">personalized recommendation</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20user%20modelling" title=" generative user modelling"> generative user modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=user%20intention%20identification" title=" user intention identification"> user intention identification</a>, <a href="https://publications.waset.org/abstracts/search?q=large%20language%20models" title=" large language models"> large language models</a>, <a href="https://publications.waset.org/abstracts/search?q=chain-of-thought%20prompting" title=" chain-of-thought prompting"> chain-of-thought prompting</a> </p> <a href="https://publications.waset.org/abstracts/185916/user-intention-generation-with-large-language-models-using-chain-of-thought-prompting-title" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/185916.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">53</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div 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