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Data Requirements for Evaluation of Personalization of Information Retrieval - A Position Paper
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Two key, but usually ignored, issues for the evaluation of methods of personalization for information retrieval are: that such evaluation must be of a search session as a whole; and, that people, during the course of an information search session, engage in a variety of activities, intended to accomplish differ- ent goals or intentions. Taking serious account of these factors has major impli- cations for not only evaluation methods and metrics, but also for the nature of the data that is necessary both for understanding and modeling information search, and for evaluation of personalized support for information retrieval (IR). In this position paper, we: present a model of IR demonstrating why these fac- tors are important; identify some implications of accepting their validity; and, on the basis of a series of studies in interactive IR, identify some types of data concerning searcher and system behavior that we claim are, at least, necessary, if not necessarily sufficient, for meaningful evaluation of personalization of IR.
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Cited by 1 Pith paper
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IntellectSeeker: A Personalized Literature Management System with the Probabilistic Model and Large Language Model
IntellectSeeker combines a fine-tuned GPT-3.5-turbo term translator and a probabilistic relevance filter for personalized academic search, reporting BLEU 0.93 and ROUGE-1 0.94 on a self-generated corpus.
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