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The Journal of Supercomputing

Erschienen in:

09.11.2020

Attribute-aware deep attentive recommendation

verfasst von: Xiaoxin Sun, Lisa Zhang, Yuling Wang, Mengying Yu, Minghao Yin, Bangzuo Zhang

Erschienen in: The Journal of Supercomputing | Ausgabe 6/2021

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Abstract

Since the rich semantics of attribute information has become a great supplement to the ratings data in designing recommender systems, fusing attributes information into ratings has shown promising performance for many recommendation tasks. However, the use of attribute information is not easy, because different attributes are often: (1) multi-source, that is, attributes may come from many different fields, (2) unstructured, (3) unbalanced, (4) heterogeneous. In this paper, we explore effective fusion of user-item ratings and item attributes to improve recommendations, we propose an attribute-aware deep attentive recommendation model, which embeds attribute information into the latent semantic space of items through the attention mechanism, forming more accurate item representations. Extensive experiments show that our method is superior to the existing methods on both rating prediction and Top-N Recommendation tasks.

Vorheriger Artikel Error detection of arithmetic expressions

Nächster Artikel Classification of hyperspectral remote sensing image via rotation-invariant local binary pattern-based weighted generalized closest neighbor

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Liu K, Shi X, Natarajan P (2018). “A Sequential Embedding Approach for Item Recommendation with Heterogeneous Attributes.” arXiv preprint arXiv:1805.11008 .2018

Ying R, He R, Chen K, et al. (2018) “Graph convolutional neural networks for web-scale recommender systems.” In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2018: 974–983

Bouadjenek M R, Pacitti E, Servajean M, et al. (2018) “A Distributed Collaborative Filtering Algorithm Using Multiple Data Sources.” arXiv preprint arXiv: 1807.05853, 2018

Gao C, He X, Gan D, et al. (2018) “Learning recommender systems from multi-behavior data.” arXiv preprint arXiv:1809.08161, 2018

Vahidi Ferdousi Z, Colazzo D, Negre E (2018) “CBPF: leveraging context and content information for better recommendations. “arXiv preprint arXiv:1810.00751

Kim D, Park C, Oh J, et al. (2016) “Convolutional matrix factorization for document context-aware recommendation.” In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 2016: 233–240

Xue F, He X, Wang X et al (2019) Deep item-based collaborative filtering for top-n recommendation. ACM transactions on Information Syst. (TOIS) 37(3):1–25CrossRef

Guo H, Tang R, Ye Y, et al. (2018) “DeepFM: An end-to-end wide and deep learning framework for CTR prediction.” arXiv preprint arXiv:1804.04950

Kang W C, Wan M, McAuley J (2018) “Recommendation through mixtures of heterogeneous item relationships.” In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018: 1143–1152

10.

Li T, Ma Y, Xu J, et al. (2018) “Deep heterogeneous autoencoders for collaborative filtering.” In 2018 IEEE International Conference on Data Mining (ICDM). IEEE: 1164–1169

11.

Lv J, Song B, Guo J et al (2019) Interest-related item similarity model based on multimodal data for top-N recommendation. IEEE Access 7:12809–12821CrossRef

12.

Su Y, Erfani S M, Zhang R. “MMF: Attribute Interpretable Collaborative Filtering.” In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019: 1–8

13.

Song W, Xiao Z, Wang Y, et al. “Session-based social recommendation via dynamic graph attention networks.” In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. pp. 555–563

14.

Xin X, He X, Zhang Y, et al. (2019) “Relational collaborative filtering: Modeling multiple item relations for recommendation.” In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 125–134

15.

Liu T, Wang Z, Tang J, et al. (2019) “Recommender Systems with Heterogeneous Side Information.” The World Wide Web Conference. pp. 3027–3033.

16.

Wang R, Fu B, Fu G, et al. (2017) “Deep & cross network for ad click predictions.” In Proceedings of the ADKDD'17. ACM 12

17.

Lin G, Liu Y, Zhu W (2017) Speeding up a memetic algorithm for the max-bisection problem [J]. Numer Algebra Control Optimization 5(2):151–168MathSciNetMATH

18.

Guo W, Li J, Chen G et al (2015) A PSO-optimized real-time fault-tolerant task allocation algorithm in wireless sensor networks [J]. IEEE Transactions Parallel Distrib Syst 26(12):3236–3249CrossRef

19.

Li X, Zhu Z, Zhu W (2017) Discrete relaxation method for triple patterning lithography layout decomposition [J]. IEEE Transactions Comput 66(2):285–298MathSciNetMATHCrossRef

20.

Guo L, Shen H (2017) Efficient Approximation Algorithms for the Bounded Flexible Scheduling Problem in Clouds [J]. IEEE Transactions Parallel Distrib Syst 28(12):3511–3520CrossRef

21.

Cheng Y, Jiang H, Wang F et al (2018) Using high-bandwidth networks efficiently for fast graph computation[J]. IEEE Transactions Parallel Distrib Syst 30(5):1–1

22.

Huang X, Liu G, Guo W et al (2015) Obstacle-avoiding algorithm in X-architecture based on discrete particle swarm optimization for VLSI design [J]. ACM Transactions Des Automation Electron Syst (TODAES) 20(2):24

23.

Huang X, Guo W, Liu G et al (2016) FH-OAOS: a fast four-step heuristic for obstacle-avoiding octilinear steiner tree construction [J]. ACM Transactions on Des Automation Electron Syst (TODAES) 21(3):48

24.

Li X, Zhu W (2017) Two-stage layout decomposition for hybrid E-beam and triple patterning lithography [J]. ACM Transactions Des Automation Electron Syst (TODAES) 23(1):6MathSciNet

25.

Cheng H, Xiong N, Yang LT et al (2013) Distributed scheduling algorithms for channel access in TDMA wireless mesh networks[J]. J Supercomput 63(2):407–430CrossRef

26.

Lu Y, Dong R, Smyth B (2018). “Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews.” In Proceedings of the World Wide Web Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2018: 773–782

27.

Liu G, Zhang L (2019). “Do Co-purchases Reveal Preferences? Explainable Recommendation with Attribute Networks.” arXiv preprint arXiv:1908.05928, 2019

28.

Cheng H T, Koc L, Harmsen J, et al. (2016) “Wide & deep learning for recommender systems.”In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM: 7–10

29.

Shan Y, Hoens T R, Jiao J, et al. (2016) “Deep Crossing: Web-scale modeling without manually crafted combinatorial features.”In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016: 255–262

30.

He X, Liao L, Zhang H, et al. (2017) “Neural collaborative filtering.” Proceedings of the 26th international conference on World Wide Web.: 173–182

31.

Guo H, Tang R, Ye Y, et al. (2017) “DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.” arXiv preprint arXiv:1703.04247

32.

He X, Chua T S (2017). “Neural factorization machines for sparse predictive analytics.” Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 2017: 355–364

33.

Li S, Kawale J, Yun F (2015) Deep collaborative filtering via marginalized denoising auto-encoder. CIKM. ACM 15:811–820CrossRef

34.

Shi C, Zhang Z, Luo P, Yu PS, Yue Y, Wu B (2015) Semantic path based personalized recommendation on weighted heterogeneous information networks. CIKM. 15:453–462CrossRef

35.

Wang X, Wang Y (2014). “Improving content-based and hybrid music recommendation using deep learning.”In Proceedings of the 22nd ACM international conference on Multimedia. ACM, 627–636

36.

Okura S, Tagami Y, Ono S, et al. (2017) “Embedding-based News Recommendation for Millions of Users.”In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM 1933–1942

37.

Strub F, Mary J (2015). “Collaborative filtering with stacked denoisingautoencoders and sparse inputs.”In Proc. NIPSWorkshop on Machine Learning for eCommerce, December

38.

Wu Y, DuBois C, Zheng A X, Ester M (2016). “Collaborativedenoising auto-encoders for top-n recommender systems.”In Proc. the 9th ACM International Conference on WebSearch and Data Mining, February pp.153–162

39.

Yue L, Sun XX, Gao WZ et al (2018) Multiple auxiliary information based deep model for collaborative filtering. J Comput Sci Tech 33(4):668–681CrossRef

40.

Strub F, Gaudel R, Mary J (2016). “Hybrid recommender systembased on autoencoders.”In Proc. the 1st Workshop onDeep Learning for Recommender Systems, September pp.11–16

41.

Mnih A, Salakhutdinov R R (2008). “Probabilistic matrix factorization.”In Advances in neural information processing systems 1257–1264

42.

Wang C, Blei D M (2011). “Collaborative topic modeling for recommending scientific articles.”In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM 448–456

43.

Wang H, Wang N, Yeung D Y (2015). “Collaborative deep learningfor recommender systems.”In Proc. the 21th ACMSIGKDD International Conference on Knowledge Discoveryand Data Mining, August pp.1235–1244

44.

Huang X, Guo W, Liu G et al (2017) MLXR: multi-layer obstacle-avoiding X-architecture Steiner tree construction for VLSI routing [J]. Sci China Information Sci 60(1):19102CrossRef

45.

Liu G, Guo W, Li R et al (2015) XGRouter: high-quality global router in X-architecture with particle swarm optimization [J]. Frontiers Comput Sci 9(4):576–594CrossRef

46.

Guo W, Liu G, Chen G et al (2014) A hybrid multi-objective PSO algorithm with local search strategy for VLSI partitioning [J]. Frontiers Comput Sci 8(2):203–216MathSciNetCrossRef

47.

Cheng Y, Wang F, Jiang H et al (2018) A communication-reduced and computation-balanced framework for fast graph computation [J]. Frontiers ComputSci 12(5):887–907CrossRef

48.

Yang D, Liao X, Shen H et al (2017) Relative influence maximization in competitive social networks [J]. Sci China Information Sci 60(10):108101CrossRef

49.

Wei J, Liao X, Zheng H, et al. Learning from context: A mutual reinforcement model for Chinese microblog opinion retrieval [J]. Frontiers Comput Sci 2017.

50.

Chen X, Li A, Zeng X et al (2015) Runtime model based approach to IoT application development [J]. Frontiers Comput Sci 9(4):540–553CrossRef

51.

Guo L, Shen H, Zhu W (2017) Efficient approximation algorithms for multi-antennae largest weight data retrieval [J]. IEEE Trans Mob Comput 16(12):3320–3333CrossRef

52.

Zhang Q, Qiu Q, Guo W et al (2016) A social community detection algorithm based on parallel grey label propagation [J]. Comput Netw 107:133–143CrossRef

53.

Lin B, Guo W, Xiong N et al (2016) A pretreatment workflow scheduling approach for big data applications in multicloud environments [J]. IEEE Trans Netw Serv Manage 13(3):581–594CrossRef

54.

Yu Z, Yu Z, Chen Y (2016) Multi-hop mobility prediction [J]. Mobile Netw Appl 21(2):367–374CrossRef

55.

Yang Y, Zheng X, Tang C (2017) Lightweight distributed secure data management system for health internet of things [J]. J Netw Comput Appl 89:26–37CrossRef

56.

AreT. Bai, J.-Y. Nie, W. X. Zhao, Y. Zhu, P. Du, J.-R. Wen (2018), “Anattribute-aware neural attentive model for next basket recommendation,”in SIGIR pp. 1201–1204

57.

Can Wang, Chi-Hung Chi, Wei Zhou, Raymond K Wong. 2015. Coupled Interdependent Attribute Analysis on Mixed Data. In AAAI. 1861–1867

58.

P. Nguyen, J. Dines, J. Krasnodebski (2017). “A multi-objective learning to rerank approach to optimize online marketplaces for multiple stakeholders,” CoRR, vol. abs/1708.00651

59.

Qiu H, Liu Y, Guo G, Sun Z, Zhang J, Nguyen HT (2018) Bprh: bayesian personalized ranking for heterogeneous implicit feedback. Inf Sci 453:80–98CrossRef

60.

Guo G, Qiu H, Tan Z, Liu Y, Ma J, Wang X (2017) Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems. Knowl Based Syst 138:202–207CrossRef

61.

J. Ding, G. Yu, X. He, Y. Quan, Y. Li, T.-S. Chua, D. Jin, J. Yu (2018), “Improving implicit recommender systems with view data.” In IJCAI, pp. 3343–3349

62.

C. Gao, X. He, D. Gan, X. Chen, F. Feng, Y. Li, T.-S. Chua et al.( 2018), “Learning recommender systems from multi-behavior data,” arXiv preprint arXiv: 1809.08161

63.

Q. Xia, P. Jiang, F. Sun, Y. Zhang, X. Wang, Z. Sui (2018), “Modeling consumer buying decision for recommendation based on multi-task deep learning,” in CIKM, pp. 1703–1706

64.

X. Chen, H. Xu, Y. Zhang, J. Tang, Y. Cao, Z. Qin, H. Zha (2018), “Sequential recommendation with user memory networks,” in WSDM, pp. 108–116

65.

R. He, W.-C. Kang, J. McAuley (2017), “Translation-based recommendation,” in RecSys, pp. 161–169.

66.

R. He, C. Fang, Z.Wang, J. McAuley (2018), “Vista: A visually, socially, and temporally-aware model for artistic recommendation,” in RecSys, pp. 309–316.arXiv:1812.08434,

67.

Zhang S, Yao L, Sun A, et al. (2018) “Neurec: on nonlinear transformation for personalized ranking”. arXiv preprint arXiv:1805.03002

68.

X. Ning, G. Karypis 2011. Slim: Sparse linear methods for top-n recommender systems. In ICDM, pp 497–506

69.

S. Rendle, C. Freudenthaler, Z. Gantner, L. Schmidt-Thieme 2009. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, pages 452–461, Arlington, Virginia, United States

Titel: Attribute-aware deep attentive recommendation
verfasst von: Xiaoxin Sun
Lisa Zhang
Yuling Wang
Mengying Yu
Minghao Yin
Bangzuo Zhang
Publikationsdatum: 09.11.2020
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 6/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-020-03459-9

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