Reservoir Sampling Based Streaming Method for Large Scale Collaborative Filtering

Authors

  • Tevfik Aytekin Bahçeşehir Unversity

DOI:

https://doi.org/10.18201/ijisae.2018644776

Keywords:

Collaborative filtering, reservoir sampling, large-scale recommender systems.

Abstract

Collaborative filtering algorithms work on user feedback data (such as purchases, clicks, or ratings.) in order to build models of users and items. User feedback data in real life e-commerce sites can be very large which incurs high costs on maintenance and model building. Parallelization of computation might help but it results in additional costs for extra computing power and maintenance problems of very large datasets still persist. Sampling at this point can be an effective approach for reducing the amount of data. In this work we propose a novel sampling technique for collaborative filtering which can be used to reduce the amount of data considerably. Experimental results on three real life datasets show that the proposed method leads to a significant reduction in the amount of data with little harm to the accuracy of the models. The method works in a streaming fashion which makes it suitable for being used in real time at large-scale e-commerce applications where there is a large flow of continuous user feedback.

Downloads

Download data is not yet available.

Downloads

Published

26.09.2018

How to Cite

Aytekin, T. (2018). Reservoir Sampling Based Streaming Method for Large Scale Collaborative Filtering. International Journal of Intelligent Systems and Applications in Engineering, 6(3), 191–196. https://doi.org/10.18201/ijisae.2018644776

Issue

Section

Research Article