Personal Assistant Systems
How Steven Spielberg Convinced the Cast of Disclosure Day That Aliens Are Real
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Can LLMs Outshine Conventional Recommenders? A Comparative Evaluation
Integrating large language models (LLMs) into recommender systems has created new opportunities for improving recommendation quality. However, a comprehensive benchmark is needed to thoroughly evaluate and compare the recommendation capabilities of LLMs with traditional recommender systems. In this paper, we introduce \recbench{}, which systematically investigates various item representation forms (including unique identifier, text, semantic embedding, and semantic identifier) and evaluates two primary recommendation tasks, i.e., click-through rate prediction (CTR) and sequential recommendation (SeqRec). Our extensive experiments cover up to 17 large models and are conducted across five diverse datasets from fashion, news, video, books, and music domains. Our findings indicate that LLM-based recommenders outperform conventional recommenders, achieving up to a 5% AUC improvement in CTR and up to a 170% NDCG@10 improvement in SeqRec. However, these substantial performance gains come at the expense of significantly reduced inference efficiency, rendering LLMs impractical as real-time recommenders. We have released our code and data to enable other researchers to reproduce and build upon our experimental results.
The Fight Over AI Is Really a Fight Over Who Governs
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What's In--and Not In--the Immigration Enforcement Funding Bill Congress Passed
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Bill Gates Is Set to Give an Interview in the House Epstein Probe. Here's What to Know About His Ties to Epstein
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Young People Don't Need Another Lecture About Phones
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Maine Democrats Pick Graham Platner, Scandals and All, to Take On Susan Collins
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Disclosure Day, One of Spielberg's Finest, Is a Plea to Preserve All that Makes Us Human
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Meet Nithya Raman, the Progressive Democrat Who Secured the Second Spot in LA Mayor Race
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Who NASA Chose For Its Artemis III Crew and What Their Mission Is
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