Unified and Scalable Incremental Recommenders with Consumed Item Packs

Recommenders personalize the web content using collaborative filtering to relate users (or items). This work proposes to unify user-based, item-based and neural word embeddings types of recommenders under a single abstraction for their input, we name Consumed Item Packs (CIPs). In addition to genericity, we show this abstraction to be compatible with incremental processing, which is at the core of low latency recommendation to users. We propose three such algorithms using CIPs, analyze them, and describe their implementation and scalability for the Spark platform. We demonstrate that all three provide a recommendation quality that is competitive with three algorithms from the state-of-the-art.


Editor(s):
Guerraoui, Rachid
Published in:
Euro-Par 2019: Parallel Processing. 25th International Conference on Parallel and Distributed Computing, Göttingen, Germany, August 26–30, 2019, Proceedings, 11725, 227-240
Presented at:
Euro-Par 2019 : European Conference on Parallel Processing, Göttingen, Germany, August 26–30, 2019
Year:
2019
Publisher:
Springer
ISBN:
978-3-030-29399-4
978-3-030-29400-7
Keywords:
Other identifiers:
Laboratories:


Note: The status of this file is: Anyone


 Record created 2020-03-03, last modified 2020-04-20

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