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@inproceedings{1418219,
author = {Nálepa, Filip and Batko, Michal and Zezula, Pavel},
address = {New York},
booktitle = {IDEAS 2018 : 22nd International Database Engineering & Applications Symposium, June 18-20, 2018, Villa San Giovanni, Italy},
doi = {http://dx.doi.org/10.1145/3216122.3216159},
editor = {Bipin C. Desai},
keywords = {continuous kNN similarity join; time-dependent similarity; binary sketches},
howpublished = {elektronická verze "online"},
language = {eng},
location = {New York},
isbn = {978-1-4503-6527-7},
pages = {64-73},
publisher = {ACM},
title = {Continuous Time-Dependent kNN Join by Binary Sketches},
year = {2018}
}
TY - JOUR
ID - 1418219
AU - Nálepa, Filip - Batko, Michal - Zezula, Pavel
PY - 2018
TI - Continuous Time-Dependent kNN Join by Binary Sketches
PB - ACM
CY - New York
SN - 9781450365277
KW - continuous kNN similarity join
KW - time-dependent similarity
KW - binary sketches
N2 - An important functionality of current social applications is real-time recommendation, which is responsible for suggesting relevant published data to the users based on their preferences. By representing the users and the published data in a metric space, each user can be recommended with their k nearest neighbors among the published data. We consider the scenario when the relevance of a published data item to a user decreases as the data gets older, i.e., a time-dependent distance function is applied. We define the problem as the continuous time-dependent kNN join and provide a solution to a broad range of time-dependent functions. In addition, we propose a binary sketch-based approximation technique used to speed up the join evaluation by replacing expensive metric distance computations with cheap Hamming distances.
ER -
NÁLEPA, Filip, Michal BATKO and Pavel ZEZULA. Continuous Time-Dependent kNN Join by Binary Sketches. Online. In Bipin C. Desai. \textit{IDEAS 2018 : 22nd International Database Engineering \&{} Applications Symposium, June 18-20, 2018, Villa San Giovanni, Italy}. New York: ACM, 2018, p.~64-73. ISBN~978-1-4503-6527-7. Available from: https://dx.doi.org/10.1145/3216122.3216159.
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