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Description
Title: | Tf-iduf: A novel term-weighting scheme for user modeling based on users’ personal document collections |
Author(s): | Beel, Joeran; Langer, Stefan; Gipp, Bela |
Subject(s): | Term weighting
user modeling Tf-iduf Recommender systems |
Abstract: | TF-IDF is one of the most popular term-weighting schemes, and is applied by search engines, recommender systems, and user modeling engines. With regard to user modeling and recommender systems, we see two shortcomings of TF-IDF. First, calculating IDF requires access to the document corpus from which recommendations are made. Such access is not always given in a user-modeling or recommender system. Second, TF-IDF ignores information from a user’s personal document collection, which could – so we hypothesize – enhance the user modeling process. In this paper, we introduce TF-IDuF as a term-weighting scheme that does not require access to the general document corpus and that considers information from the users’ personal document collections. We evaluated the effectiveness of TF-IDuF compared to TF-IDF and TF-Only and found that TF-IDF and TF-IDuF perform similarly (click-through rates (CTR) of 5.09% vs. 5.14%), and both are around 25% more effective than TF-Only (CTR of 4.06%) for recommending research papers. Consequently, we conclude that TF-IDuF could be a promising term-weighting scheme, especially when access to the document corpus for recommendations is not possible, and thus classic IDF cannot be computed. It is also notable that TF-IDuF and TF-IDF are not exclusive, so that both metrics may be combined to a more effective term-weighting scheme. |
Issue Date: | 2017 |
Publisher: | iSchools |
Citation Info: | Beel, J., Langer, S., & Gipp, B. (2017). TF-IDuF: A Novel Term-Weighting Scheme for User Modeling based on Users’ Personal Document Collections. In iConference 2017 Proceedings (pp. 452-459). https://doi.org/10.9776/17217 |
Series/Report: | iConference 2017 Proceedings |
Genre: | Conference Paper / Presentation |
Type: | Text |
Language: | English |
URI: | http://hdl.handle.net/2142/96756 |
DOI: | https://doi.org/10.9776/17217 |
Rights Information: | Copyright 2017 Joeran Beel, Stefan Langer, and Bela Gipp |
Date Available in IDEALS: | 2017-07-27 |