Abstract

Over 80 approaches for academic literature recommendation exist today. The approaches were introduced and evaluated in more than 170 research articles, as well as patents, presentations and blogs. We reviewed these approaches and found most evaluations to contain major shortcomings. Of the approaches proposed, 21% were not evaluated. Among the evaluated approaches, 19% were not evaluated against a baseline. Of the user studies performed, 60% had 15 or fewer participants or did not report on the number of participants. Information on runtime and coverage was rarely provided. Due to these and several other shortcomings described in this paper, we conclude that it is currently not possible to determine which recommendation approaches for academic literature are the most promising. However, there is little value in the existence of more than 80 approaches if the best performing approaches are unknown.

Keywords:
Recommender system Computer science Baseline (sea) Information retrieval Value (mathematics) Data science Machine learning

Metrics

158
Cited By
25.33
FWCI (Field Weighted Citation Impact)
115
Refs
0.99
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Recommender Systems and Techniques
Physical Sciences →  Computer Science →  Information Systems
Expert finding and Q&A systems
Physical Sciences →  Computer Science →  Information Systems
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence

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