Document (#40529)

Author

Sappelli, M.
Verberne, S.
Kraaij, W.

Title

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

Source

Journal of the Association for Information Science and Technology. 68(2017) no.4, S.895-910

Year

2017

Abstract

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.

Content

Vgl.: http://onlinelibrary.wiley.com/doi/10.1002/asi.23717/full.

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5. Sun, J.; Zhu, M.; Jiang, Y.; Liu, Y.; Wu, L.L.: Hierarchical attention model for personalized tag recommendation : peer effects on information value perception (2021) 0.26

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