This paper provides a comprehensive survey on the emerging paradigm of generative search and recommendation. It first summarizes the previous machine learning-based and deep learning-based paradigms in search and recommendation, which approach the tasks as discriminative matching problems. In contrast, the generative paradigm formulates the tasks as generation problems, aiming to directly generate the target documents or items.
The survey then abstracts a unified framework for generative search and recommendation, consisting of four key steps: query/user formulation, document/item identifiers, training, and inference. Within this framework, the paper categorizes and analyzes the existing works on generative search and recommendation, highlighting their strengths, weaknesses, and unique challenges.
For generative search, the paper discusses various document identifiers, including numeric IDs, titles, n-grams, codebooks, and multiview identifiers. It also examines the training and inference processes, including generative and discriminative training, as well as free generation and constrained generation.
For generative recommendation, the paper focuses on the user formulation, which incorporates task descriptions, user's historical interactions, user profiles, context information, and external knowledge. It also reviews the different item identifiers, such as numeric IDs and textual metadata.
The survey further delves into the comparison between generative search and recommendation, identifies open problems in the generative paradigm, and envisions the next information-seeking paradigm that could emerge from the advancements in large language models.
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arxiv.org
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by Yongqi Li,Xi... : arxiv.org 04-29-2024
https://arxiv.org/pdf/2404.16924.pdfDaha Derin Sorular