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GESIS Search study finds semantic similarity best for social science recommendations

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv details the integration and evaluation of various recommendation systems within GESIS Search, a specialized search engine for social sciences. Researchers implemented and compared lexical similarity, transformer-based semantic similarity, and session-based recommendation algorithms. The findings indicate that users generally prefer recommendations based on semantic similarity over term-similarity and session-based methods, though performance varies across different categories within the search engine. AI

IMPACT Findings suggest semantic similarity is key for academic search recommendations, potentially guiding future development in scholarly information retrieval.

RANK_REASON Academic paper detailing research findings on recommendation systems. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GESIS Search study finds semantic similarity best for social science recommendations

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Academic paper detailing research findings on recommendation systems. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Daniel Hienert ·

    Continuous Online Evaluation of Recommendation Strategies in Social Science Academic Search

    Delivering relevant recommendations in academic search engines is a complex task due to the diversity of subject areas, information types, and user preferences. In this case study, we address these challenges by integrating and evaluating a range of recommendation systems within …

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