P
Pinar Karagoz
Researcher at Middle East Technical University
Publications - 108
Citations - 1232
Pinar Karagoz is an academic researcher from Middle East Technical University. The author has contributed to research in topics: Turkish & Event (computing). The author has an hindex of 16, co-authored 108 publications receiving 966 citations.
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Journal ArticleDOI
Neural information retrieval: at the end of the early years
Kezban Dilek Onal,Kezban Dilek Onal,Ye Zhang,Ismail Sengor Altingovde,Md. Mustafizur Rahman,Pinar Karagoz,Alexander Braylan,Brandon Dang,Heng-Lu Chang,Henna Kim,Quinten McNamara,Aaron Angert,Edward Banner,Vivek Khetan,Tyler McDonnell,An Thanh Nguyen,Dan Xu,Byron C. Wallace,Maarten de Rijke,Matthew Lease +19 more
TL;DR: The successes of neural IR thus far are highlighted, obstacles to its wider adoption are cataloged, and potentially promising directions for future research are suggested.
Journal ArticleDOI
A Novel Wind Power Forecast Model: Statistical Hybrid Wind Power Forecast Technique (SHWIP)
Mehmet Baris Ozkan,Pinar Karagoz +1 more
TL;DR: In this paper, a new statistical short-term (up to 48h) wind power forecast model, namely statistical hybrid wind power forecasting technique (SHWIP), is presented, where weather events are clustered with respect to the most important weather forecast parameters.
Journal ArticleDOI
CRoM and HuspExt: Improving Efficiency of High Utility Sequential Pattern Extraction
Oznur Alkan,Pinar Karagoz +1 more
TL;DR: An efficient data structures and pruning technique which is based on Cumulated Rest of Match (CRoM) based upper bound, which allows more conservative pruning before candidate pattern generation in comparison to the existing techniques is proposed.
Proceedings ArticleDOI
Context-Aware Friend Recommendation for Location Based Social Networks using Random Walk
Hakan Bagci,Pinar Karagoz +1 more
TL;DR: This paper proposes a random walk based context-aware friend recommendation algorithm (RWCFR), which considers the current context of the user to provide personalized recommendations and compares RWCFR with popularity-based, friend-based and expert-based baseline approaches.
Journal ArticleDOI
Context-aware location recommendation by using a random walk-based approach
Hakan Bagci,Pinar Karagoz +1 more
TL;DR: This paper proposes a context-aware location recommendation system for LBSNs using a random walk approach and compares the algorithm, CLoRW, with popularity-based, friend-based and expert-based baselines, user-based collaborative filtering approach and a similar work in the literature.