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KPG4Rec: Knowledge Property-Aware Graph for Recommender Systems

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Cloud Computing (CloudComp 2021)

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Abstract

The collaborative filtering (CF) based models have the powerful ability to use the interaction of users and items for recommendation. However, many existing CF-based approaches can only grasp the single relationship between users or items, such as item-based CF, which utilizes the single relationship of similarity identified from user-item matrix to compute recommendations. To overcome these shortcomings, we propose a novel approach named KPG4Rec which integrates multiple property relationships of items for personalized recommendation. In the initial step, we extract properties and corresponding triples of items from an existing knowledge graph, and utilize them to construct property-aware graphs based on user-item interaction graphs. Then, continuous low-dimensional vectors are learned through node2vec technology in these graphs. In the prediction phase, the recommendation score of one candidate item is computed by comparing it with each item in the user history preference sequence, where the pretrained embedding vectors of items are used to take all the properties into consideration. On the other hand, Locality Sensitive Hashing (LSH) mechanism is adopted to generate brand new preference sequences of users to improve the efficiency of KPG4Rec. Through extensive experiments on two real-world datasets, our approach is proved to outperform several widely adopted methods in the Top-N recommendation scenario.

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References

  1. Bell, R.M., Koren, Y.: Improved neighborhood-based collaborative filtering. In: KDD Cup Workshop 13th ACM SIGKDD International Conference on Knowledge Discovery, pp. 7–14. ACM, San Jose (2007)

    Google Scholar 

  2. Wang, F., Zhu, H., Srivastava, G., Li, S., Khosravi, M.R., Qi, L.: Robust collaborative filtering recommendation with user-item-trust records. IEEE Trans. Comput. Soc. Syst. 1–11 (2021)

    Google Scholar 

  3. Li, B., Zhu, X., Li, R., Zhang, C.: Rating knowledge sharing in cross-domain collaborative filtering. IEEE Trans. Cybern. 45(5), 1054–1068 (2015)

    Article  Google Scholar 

  4. Linden, G., Smith, B., York, J.: Amazon. com recommendations: item-to-item collaborative filtering. IEEE. Internet. Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  5. Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: ACM 15th International Conference Intelligence User Interfaces, pp. 31–40. ACM, Hong Kong (2010)

    Google Scholar 

  6. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation. In: 10th International Conference World Wide Web, pp. 285–295. ACM, Hong Kong (2001)

    Google Scholar 

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

    Article  Google Scholar 

  8. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: 25th Conference Uncertainity Artificial Intelligence, pp. 452–461. UAI 2009 (2009)

    Google Scholar 

  9. Luo, X., Zhou, M., Xia, Y., Zhu, Q.: An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans. Ind. Inform. 10, 1273–1284 (2014)

    Article  Google Scholar 

  10. Yu, X., et al.: Personalized entity recommendation: A heterogeneous information network approach. In: WSDM 2014 - Proceedings of 7th ACM International Conference Web Search Data Mining, pp. 283–292. ACM, New York (2014)

    Google Scholar 

  11. Zhao, H., Yao, Q., Li, J., Song, Y., Lee, D.L.: Meta-graph based recommendation fusion over heterogeneous information networks. In: ACM SIGKDD International Conference on Knowledge Discovery Data Mining, pp. 635–644. ACM, Halifax (2017)

    Google Scholar 

  12. Xu, X., Huang, Q., Zhang, Y., Li, S., Qi, L., Dou, W.: An LSH-based offloading method for IoMT services in integrated cloud-edge environment. ACM Trans. Multimed. Comput. Commun. 16(3), 1–19 (2021)

    Google Scholar 

  13. Grover, A., Leskovec, J.: Node2Vec. In: KDD 2016: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM, San Francisco (2016)

    Google Scholar 

  14. Datar, M., Indyk, P., Immorlica, N., Mirrokni, V.S.: Locality-sensitive hashing scheme based on p-stable distributions. In: Annual Symposium on Computational Geometry, pp. 253–262. ACM, Brooklyn (2004)

    Google Scholar 

  15. Xu, X., et al.: Secure service offloading for internet of vehicles in SDN-enabled mobile edge computing. IEEE Trans. Intell. Transp. Syst. 22(6), 3720–3729 (2021)

    Article  Google Scholar 

  16. Qi, L., Wang, X., Xu, X., Dou, W., Li, S.: Privacy-aware cross-platform service recommendation based on enhanced locality-sensitive hashing. IEEE Trans. Netw. Sci. Eng. 8, 1145–1153 (2020)

    Article  Google Scholar 

  17. Wang, H., et al.: RippleNet: propagating user preferences on the knowledge graph for recommender systems. In: International Conference on Information and Knowledge Management Proceedings, pp. 417–426. ACM, Torino (2018)

    Google Scholar 

  18. Wang, H., Zhang, F., Zhao, M., Li, W., Xie, X., Guo, M.: Multi-task feature learning for knowledge graph enhanced recommendation. In: Web Conference on 2019 - Proceedings of World Wide Web Conference, pp. 2000–2010. ACM, San Francisco (2019)

    Google Scholar 

  19. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26, pp. 3111–3119. NIPS (2013)

    Google Scholar 

  20. Ostuni, V.C., Di Noia, T., Mirizzi, R., Di Sciascio, E.: Top-N recommendations from implicit feedback leveraging linked open data. In: CEUR Workshop Proceedings, pp. 20–27. ACM, Hong Kong (2014)

    Google Scholar 

  21. Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-N recommendation tasks. In: RecSys 2010 – Proceedings of 4th ACM Conference on Recommendation Systems, pp. 39–46. ACM, Barcelona (2010)

    Google Scholar 

  22. Ning, X., Karypis, G.: SLIM: sparse linear methods for top-N recommender systems. In: IEEE International Conference Data Mining, ICDM, pp. 497–506. IEEE, Vancouver (2011)

    Google Scholar 

  23. Zheng, L., Lu, C.T., Jiang, F., Zhang, J., Yu, P.S.: Spectral collaborative filtering. In: RecSys 2018 - 12th ACM Conference on Recommendation Systems, pp. 311–319. ACM. Vancouver (2018)

    Google Scholar 

  24. Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: Online learning of social representations. In: ACM SIGKDD International Conference Knowledge Discovery and Data Mining, pp. 701–710. ACM, New York (2014)

    Google Scholar 

  25. Wang, H., Zhang, F., Xie, X., Guo, M.: DKN: Deep knowledge-aware network for news recommendation. In: Web Conference on 2018 – Proceedings of World Wide Web Conference WWW 2018, pp. 1835–1844. ACM, Lyon (2018)

    Google Scholar 

  26. Wang, H., Zhao, M., Xie, X., Li, W., Guo, M.: Knowledge graph convolutional networks for recommender systems. In: Web Conference on 2019 – Proceedings of World Wide Web Conference WWW 2019, pp. 3307–3313. ACM, San Francisco (2019)

    Google Scholar 

  27. Wang, Z., Lin, G., Tan, H., Chen, Q., Liu, X.: CKAN: collaborative knowledge-aware attentive network for recommender systems. In: SIGIR 2020 – Proceedings of 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 219–228. ACM, Virtual Event (2020)

    Google Scholar 

  28. Xu, X., et al.: Edge content caching with deep spatiotemporal residual network for IoV in smart city. ACM Trans. Sens. Netw. 17(3), 1–33 (2021)

    Article  Google Scholar 

  29. Liu, Y., et al.: An attention-based category-aware GRU model for the next POI recommendation. Int. J. Intell. Syst. 36, 3174–3189 (2021)

    Article  Google Scholar 

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Acknowledgement

This work is supported in part by the Fundamental Research Fund for the Central Universities (30920041112, 30919011282), the National Key R&D Program of China (Funding No. 2020YFB1805503), Jiangsu Province Modern Education Technology Research Project (84365); National Vocational Education Teacher Enterprise Practice Base “Integration of Industry and Education” Special Project (Study on Evaluation Standard of Artificial Intelligence Vocational Skilled Level). the Postdoctoral Science Foundation of China (2019M651835), National Natural Science Foundation of China (61702264).

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Authors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, China

    Hao Ge, Qianmu Li & Shunmei Meng

  2. Intelligent Manufacturing Department, Wuyi University, Nanping, China

    Qianmu Li

  3. School of Social Science, Nanjing Vocational University of Industry Technology, Nanjing, China

    Jun Hou

Authors
  1. Hao Ge
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  2. Qianmu Li
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  3. Shunmei Meng
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  4. Jun Hou
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Corresponding author

Correspondence to Qianmu Li .

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Editors and Affiliations

  1. Persian Gulf University, Bushehr, Iran

    Mohammad R. Khosravi

  2. Swinburne University of Technology, Hawthorn, VIC, Australia

    Qiang He

  3. Nanjing University, Nanjing, China

    Haipeng Dai

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Ge, H., Li, Q., Meng, S., Hou, J. (2022). KPG4Rec: Knowledge Property-Aware Graph for Recommender Systems. In: Khosravi, M.R., He, Q., Dai, H. (eds) Cloud Computing. CloudComp 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 430. Springer, Cham. https://doi.org/10.1007/978-3-030-99191-3_9

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  • DOI: https://doi.org/10.1007/978-3-030-99191-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-99190-6

  • Online ISBN: 978-3-030-99191-3

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