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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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