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Evaluating the CC-IDF citation-weighting scheme: How effectively can ‘Inverse Document Frequency’ (IDF) be applied to references?
Beel, Joeran; Breitinger, Corinna; Langer, Stefan
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https://hdl.handle.net/2142/96778
Description
- Title
- Evaluating the CC-IDF citation-weighting scheme: How effectively can ‘Inverse Document Frequency’ (IDF) be applied to references?
- Author(s)
- Beel, Joeran
- Breitinger, Corinna
- Langer, Stefan
- Issue Date
- 2017
- Keyword(s)
- Recommender systems
- Common Citation Inverse Document Frequency (CC-IDF)
- Digital libraries
- Weighting schemes
- Inverse Document Frequency (IDF)
- Related document search
- Date of Ingest
- 2017-07-27T15:59:04Z
- Abstract
- In the domain of academic search engines and research-paper recommender systems, CC-IDF is a common citation-weighting scheme that is used to calculate semantic relatedness between documents. CC-IDF adopts the principles of the popular term-weighting scheme TF-IDF and assumes that if a rare academic citation is shared by two documents then this occurrence should receive a higher weight than if the citation is shared among a large number of documents. Although CC-IDF is in common use, we found no empirical evaluation and comparison of CC-IDF with plain citation weight (CC-Only). Therefore, we conducted such an evaluation and present the results in this paper. The evaluation was conducted with real users of the recommender system Docear. The effectiveness of CC-IDF and CC-Only was measured using click-through rate (CTR). For 238,681 delivered recommendations, CC-IDF had about the same effectiveness as CC-Only (CTR of 6.15% vs. 6.23%). In other words, CC-IDF was not more effective than CC-Only, which is a surprising result. We provide a number of potential reasons and suggest to conduct further research to understand the principles of CC-IDF in more detail.
- Publisher
- iSchools
- Series/Report Name or Number
- iConference 2017 Proceedings
- Type of Resource
- text
- Language
- en
- Permalink
- http://hdl.handle.net/2142/96778
- Copyright and License Information
- Copyright 2017 Joeran Beel, Corinna Breitinger, and Stefan Langer
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