nach oben

The Journal of Supercomputing

Erschienen in:

10.07.2018

Efficient processing of recommendation algorithms on a single-machine-based graph engine

verfasst von: Yong-Yeon Jo, Myung-Hwan Jang, Sang-Wook Kim, Kyungsik Han

Erschienen in: The Journal of Supercomputing | Ausgabe 10/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The wide use of recommendation systems includes more users and items in system operations, leading to a significant increase in the size of related datasets. However, recommendation algorithms on existing single-machine-based graph engines have been developed without considering the important characteristics of recommendation datasets, i.e., huge size and power-law degree distribution. In this paper, we address how to realize efficient graph- and matrix-factorization-based recommendation algorithms, handling recommendation datasets on RealGraph, a state-of-the-art single-machine-based graph engine. Through extensive experiments, we demonstrate that our recommendation algorithms on RealGraph universally and consistently outperform the algorithms on other graph engines over all datasets up to 34 times.

Vorheriger Artikel Hardware-aided update acceleration in a hybrid Semantic Web database system

Nächster Artikel Transition activity recognition using fuzzy logic and overlapped sliding window-based convolutional neural networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

http://grouplens.org/datasets/movielens/1m/.

https://archive.org/download/nf_prize_dataset.tar.

Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749CrossRef

Carey MJ, DeWitt DJ, Franklin MJ, Hall NE, McAuliffe ML, Naughton JF, Schuh DT, Solomon MH, Tan C, Tsatalos OG, White SJ, Zwilling, MJ (1994) Shoring up persistent applications. In: Proceedings of the ACM International Conference on Management of Data, ACM, pp 383–394

Carey MJ, DeWitt DJ, Richardson JE, Shekita EJ (1986) Object and file management in the EXODUS extensible database system. University of Wisconsin-Madison, Madison

Chen H, Li Z, Hu W (2016) An improved collaborative recommendation algorithm based on optimized user similarity. J Supercomput 72(7):2565–2578CrossRef

Chen, HC., Chen, AL (2001) A music recommendation system based on music data grouping and user interests. In: Proceedings of the International Conference on Information and Knowledge Management, ACM, pp 231–238

Chi Y, Dai G, Wang Y, Sun G, Li G, Yang H (2016) Nxgraph: an efficient graph processing system on a single machine. In: Proceedings of the IEEE International Conference on Data Engineering, pp 409–420

Chou HT, Dewitt DJ, Katz RH, Klug AC (1985) Design and implementation of the wisconsin storage system. Softw Pract Exp 15(10):943–962CrossRef

Da Zheng DM, Burns R, Vogelstein J, Priebe CE, Szalay AS (2015) Flashgraph: processing billion-node graphs on an array of commodity ssds. In: Proceedings of the USENIX Conference on File and Storage Technologies, pp 45–58

Fousek J (2017) Efficient sparse matrix-delayed vector multiplication for discretized neural field model. J Supercomput 74(5):1–22

10.

Gomez-Uribe CA, Hunt N (2016) The netflix recommender system: algorithms, business value, and innovation. ACM Trans Manag Inf Syst (TMIS) 6(4):13:1–13:19

11.

Gonzalez JE, Low Y, Gu H, Bickson D, Guestrin C (2012) Powergraph: distributed graph-parallel computation on natural graphs. In: Proceedings of the USENIX Symposium on Operating Systems Design and Implementation, vol 12, p 2

12.

Ha J, Kwon SH, Kim SW, Faloutsos C, Park S (2012) Top-n recommendation through belief propagation. In: Proceedings of the ACM International Conference on Information and Knowledge Management, ACM, pp 2343–2346

13.

Han WS, Lee S, Park K, Lee JH, Kim MS, Kim J, Yu H (2013) Turbograph: a fast parallel graph engine handling billion-scale graphs in a single pc. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp 77–85

14.

Hung L (2005) A personalized recommendation system based on product taxonomy for one-to-one marketing online. Expert Syst Appl 29(2):383–392CrossRef

15.

Hwang WS, Li S, Kim SW, Lee K (2014) Data imputation using a trust network for recommendation. In: Proceedings of the International Conference on World Wide Web, pp 299–300

16.

Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the ACM Conference on Recommender Systems, ACM, pp 135–142

17.

Jang MH, Faloutsos C, Kim SW, Kang U, Ha J (2016) Pin-trust: fast trust propagation exploiting positive, implicit, and negative information. In: Proceedings of the ACM International on Conference on Information and Knowledge Management, ACM, pp 629–638

18.

Jo YY, Jang MH, Jung H, Kim, SW (2018) A high-performance graph engine for efficient social network analysis. In: Proceedings of the International Conference on World Wide Web, pp 61–62

19.

Kim J, Kang S, Lim Y, Kim HM (2013) Recommendation algorithm of the app store by using semantic relations between apps. J Supercomput 65(1):16–26CrossRef

20.

Kim MS, An K, Park H, Seo H, Kim J (2016) GTS: a fast and scalable graph processing method based on streaming topology to GPUS. In: Proceedings of the ACM International Conference on Management of Data, ACM, pp 447–461

21.

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

22.

Kyrola A, Blelloch GE, Guestrin C (2012) Graphchi: large-scale graph computation on just a pc. In: Proceedings of the USENIX Symposium on Operating Systems Design and Implementation, USENIX, pp 31–46

23.

Lee YC, Kim SW, Lee D (2018) GOCCF: graph-theoretic one-class collaborative filtering based on uninteresting items. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 3448–3456

24.

Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data 1(1):2CrossRef

25.

Liu NN, Yang Q (2008) Eigenrank: a ranking-oriented approach to collaborative filtering. In: Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp 83–90

26.

Ma L, Yang Z , Chen H , Xue J, Dai Y (2017) Garaph: efficient GPU-accelerated graph processing on a single machine with balanced replication. In: Proceedings of the USENIX Annual Technical Conference, pp 195–207

27.

Maass S, Min C, Kashyap S, Kang W, Kumar M, Kim T (2017) Mosaic: Processing a trillion-edge graph on a single machine. In: Proceedings of the European Conference on Computer Systems, pp 527–543

28.

Nelakurthi, AR, He, J (2017) Finding cut from the same cloth: cross network link recommendation via joint matrix factorization. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 1467–1473

29.

Paterek A (2007) Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of the KDD Cup and Workshop 2007, pp 5–8

30.

Roy A, Mihailovic I, Zwaenepoel W (2013) X-stream: edge-centric graph processing using streaming partitions. In: Proceedings of the ACM Symposium on Operating Systems Principles, ACM, pp 472–488

31.

Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: The Adaptive Web, vol 4321. Springer, Berlin, pp 291–324

32.

Shi L, Zhao WX, Shen YD (2017) Local representative-based matrix factorization for cold-start recommendation. ACM Trans Inf Syst 36(2):22CrossRef

33.

Takács G, Pilászy I, Németh B, Tikk D (2008) Matrix factorization and neighbor based algorithms for the netflix prize problem. In: Proceedings of the ACM Conference on Recommender Systems, ACM, pp 267–274

34.

Takács G, Tikk D (2012) Alternating least squares for personalized ranking. In: Proceedings of the ACM Conference on Recommender Systems, ACM, pp 83–90

35.

Walter FE, Battiston S, Schweitzer F (2008) A model of a trust-based recommendation system on a social network. Auton Agent Multi-Agent Syst 16(1):57–74CrossRef

36.

Yang Z, Chen W, Huang J (2017) Enhancing recommendation on extremely sparse data with blocks-coupled non-negative matrix factorization. Neurocomputing 278:126–133CrossRef

37.

Yildirim H, Krishnamoorthy MS (2008) A random walk method for alleviating the sparsity problem in collaborative filtering. In: Proceedings of the ACM Conference on Recommender Systems, ACM, pp 131–138

38.

Zhou Y, Wilkinson D, Schreiber R, Pan R (2008) Large-scale parallel collaborative filtering for the netflix prize. In: Proceedings of the International Conference on Algorithmic Applications in Management, Springer, Berlin, pp 337–348

39.

Zhu X, Han W, Chen W (2015) Gridgraph: large-scale graph processing on a single machine using 2-level hierarchical partitioning. In: Proceedings of the USENIX Annual Technical Conference, USENIX, pp 375–386

Titel: Efficient processing of recommendation algorithms on a single-machine-based graph engine
verfasst von: Yong-Yeon Jo
Myung-Hwan Jang
Sang-Wook Kim
Kyungsik Han
Publikationsdatum: 10.07.2018
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 10/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-018-2477-4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 10/2020

Techniques and guidelines for effective migration from RDBMS to NoSQL

KMLOD: linked open data service for Korean medical database

Hash-tree PCA: accelerating PCA with hash-based grouping

An attribute-based community search method with graph refining

Intelligent fault detection using raw vibration signals via dilated convolutional neural networks

Transition activity recognition using fuzzy logic and overlapped sliding window-based convolutional neural networks