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2019 | OriginalPaper | Chapter

Unified and Scalable Incremental Recommenders with Consumed Item Packs

Authors : Rachid Guerraoui, Erwan Le Merrer, Rhicheek Patra, Jean-Ronan Vigouroux

Published in: Euro-Par 2019: Parallel Processing

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter Enhancing the Programmability and Performance Portability of GPU Tensor Operations

next chapter Declarative Big Data Analysis for High-Energy Physics: TOTEM Use Case

Please refer to our technical report [20] for a detailed study of the scalability of CIP based algorithms facing a varying number of partitions.

Christakopoulou, E., Karypis, G.: HOSLIM: higher-order sparse linear method for top-n recommender systems. In: PAKDD (2014)

Barkan, O., Koenigstein, N.: Item2vec: neural item embedding for collaborative filtering. CoRR abs/1603.04259 (2016)

Kabbur, S., Ning, X., Karypis, G.: FISM: factored item similarity models for top-n recommender systems. In: KDD (2013)

Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRef

Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR abs/1301.3781 (2013)

Lee, T.Q., Park, Y., Park, Y.-T.: An empirical study on effectiveness of temporal information as implicit ratings. Expert. Syst. Appl. 36(2), 1315–1321 (2009)CrossRef

McAuley, J., Ruining, H.: Fusing similarity models with Markov chains for sparse sequential recommendation. In: ICDM (2016)

Boutet, A., Frey, D., Guerraoui, R., Kermarrec, A.-M., Patra, R.: HyRec: leveraging browsers for scalable recommenders. In: Middleware (2014)

Bengio, Y., Ducharme, R., Vincent, P., Janvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 3, 1137–1155 (2003)MATH

10.

Dean, J., et al.: Large scale distributed deep networks. In: NIPS (2012)

11.

DeepDist: lightning-fast deep learning on spark. http://deepdist.com/

12.

Fontenla-Romero, Ó., Guijarro-Berdiñas, B., Martinez-Rego, D., Pérez-Sánchez, B., Peteiro-Barral, D.: Online machine learning. In: Efficiency and Scalability Methods for Computational Intellect, p. 27 (2013)

13.

Chen, C., Yin, H., Yao, J., Cui, B.: TeRec: a temporal recommender system over tweet stream. In: VLDB (2013)

14.

CIP-based implicit recommenders: GitHub code repo. https://github.com/rpatra/CIP

15.

Apache spark. https://spark.apache.org/

16.

Movielens. http://grouplens.org/datasets/movielens/

17.

Ciao. http://www.ciao.com/

18.

Python recsys. https://pypi.python.org/pypi/python-recsys/0.2

19.

Sequence-based recommendations: GitHub code repo. https://github.com/rdevooght/sequence-based-recommendations

20.

Guerraoui, R., Le Merrer, E., Patra, R., Vigouroux, J.: Sequences, items and latent links: recommendation with consumed item packs. CoRR abs/1711.06100 (2017)

21.

Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys (2010)

22.

Craswell, N., Szummer, M.: Random walks on the click graph. In: SIGIR (2007)

23.

Ostuni, V.C., Di Noia, T., Di Sciascio, E., Mirizzi, R.: Top-n recommendations from implicit feedback leveraging linked open data. In: RecSys (2013)

24.

Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: ICDM (2008)

25.

Baltrunas, L., Amatriain, X.: Towards time-dependant recommendation based on implicit feedback. In: CARS (2009)

26.

Gordea, S., Zanker, M.: Time filtering for better recommendations with small and sparse rating matrices. In: Benatallah, B., Casati, F., Georgakopoulos, D., Bartolini, C., Sadiq, W., Godart, C. (eds.) WISE 2007. LNCS, vol. 4831, pp. 171–183. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76993-4_15CrossRef

Title: Unified and Scalable Incremental Recommenders with Consumed Item Packs
Authors: Rachid Guerraoui
Erwan Le Merrer
Rhicheek Patra
Jean-Ronan Vigouroux
Publisher: Springer International Publishing
Book: Euro-Par 2019: Parallel Processing
Print ISBN: 978-3-030-29399-4

Electronic ISBN: 978-3-030-29400-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-29400-7_17

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