Evaluation of context-aware recommendation systems for information re-finding

Autor:	Wessel Kraaij, Suzan Verberne, Maya Sappelli
Rok vydání:	2017
Předmět:	Information Systems and Management Computer Networks and Communications Computer science media_common.quotation_subject Context (language use) 02 engineering and technology Library and Information Sciences Recommender system Task (project management) Knowledge worker DSC - Data Science 020204 information systems 0202 electrical engineering electronic engineering information engineering Relevance (information retrieval) Quality (business) media_common Flexibility (engineering) TS - Technical Sciences Information retrieval Data Science Language in Society Action (philosophy) 020201 artificial intelligence & image processing 2016 ICT Language & Speech Technology Information Systems
Zdroj:	Journal of the Association for Information Science and Technology, 68, 4, pp. 895-910 Journal of the Association for Information Science and Technology Journal of the Association for Information Science and Technology, 68, 895-910
ISSN:	2330-1643
Popis:	In this article we evaluate context-aware recommendation systems for information re-finding by knowledge workers. We identify 4 criteria that are relevant for evaluating the quality of knowledge worker support: context relevance, document relevance, prediction of user action, and diversity of the suggestions. We compare 3 different context-aware recommendation methods for information re-finding in a writing support task. The first method uses contextual prefiltering and content-based recommendation (CBR), the second uses the just-in-time information retrieval paradigm (JITIR), and the third is a novel network-based recommendation system where context is part of the recommendation model (CIA). We found that each method has its own strengths: CBR is strong at context relevance, JITIR captures document relevance well, and CIA achieves the best result at predicting user action. Weaknesses include that CBR depends on a manual source to determine the context and in JITIR the context query can fail when the textual content is not sufficient. We conclude that to truly support a knowledge worker, all 4 evaluation criteria are important. In light of that conclusion, we argue that the network-based approach the CIA offers has the highest robustness and flexibility for context-aware information recommendation.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e154905c49c355349e1a1aaeb180348 https://hdl.handle.net/2066/162330 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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