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PAENL: personalized attraction enhanced network learning for recommendation

  • S.I. : Deep Geospatial Data Understanding
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Abstract

Reviews and user–item interactions have been widely used to predict the behaviors of users. However, the sparsity of user–item interactions on datasets remains a major challenge in predicting user behavior. Most of the fusion user–item information and review information is predicted for user behavior prediction in a linear sense. However, this coarse-grained data fusion encounters difficulty in finding the complex relationship between different modal features. In this study, we propose a personalized attraction enhanced network learning for recommendation PAENL. The model consists of two modules: a user–item feature learning module and a review feature interaction module. In addition to the capability of modeling heterogeneity information by convolutional neural networks, PAENL can capture the essence of different users’ emotional reviews by the attention neural model in a nonlinear sense. Experiments are conducted on three real datasets and compared with a variety of mainstream advanced algorithms. The results demonstrate that the proposed algorithm PAENL significantly outperforms all state-of-the-art methods, and the attention mechanism can increase the interpretability of the user behavior prediction.

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References

  1. Shi HJM, Mudigere D, Naumov M, Yang J (2020) Compositional embeddings using complementary partitions for memory-efficient recommendation systems. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining, pp 165–175

  2. Tanjim MM, Su C, Benjamin E, Hu D, Hong L, McAuley J (2020) Attentive sequential models of latent intent for next item recommendation. In: Proceedings of the web conference, pp 2528–2534

  3. Zhang S, Wang W, Ford J, Makedon F (2006) Learning from incomplete ratings using non-negative matrix factorization. In: Proceedings of the 2006 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, pp 549–553

  4. Mnih A, Salakhutdinov RR (2007) Probabilistic matrix factorization. Adv Neural Inf Process Syst 20:1257–1264

    Google Scholar 

  5. Shang S, Chen L, Wei Z, Jensen C, Zheng K, Kalnis P (2018) Parallel trajectory similarity joins in spatial networks. VLDB J 27(3):395–420

    Article  Google Scholar 

  6. Sedhain S, Menon AK, Sanner S, Xie L, Braziunas D (2017) Low-rank linear cold-start recommendation from social data. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, pp 1502–1508

  7. Sun P, Wu L, Zhang K, Fu, Yan jie, Hong, Richang, Wang Meng (2020) Dual learning for explainable recommendation: towards unifying user preference prediction and review generation. In: Proceedings of the web conference, pp 837–847

  8. Liu J, Shang S, Zheng K, Wen J (2016) Multi-view ensemble learning for dementia diagnosis from neuroimaging: an artificial neural network approach. Neurocomputing 195:112–116

    Article  Google Scholar 

  9. Zheng L, Noroozi V, Yu PS (2017) Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the tenth ACM international conference on web search and data mining, pp 425–434

  10. Ma C, Kang P, Liu X (2019) Hierarchical gating networks for sequential recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 825–833

  11. Chen L, Shang S, Jensen C, Xu J, Kalnis P, Yao B, Shao L (2020) Top-k term publish/subscribe for geo-textual data streams. VLDB J 29(5):1101–1128

    Article  Google Scholar 

  12. Chen L, Liu Y, He X, He X, Gao L, Zheng Z (2019) Matching user with item set: collaborative bundle recommendation with deep attention network. In: Proceedings of the Twenty-Eighth International Joint Conference on artificial intelligence, pp 2095–2101

  13. Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on Machine learning, pp 791–798

  14. Catherine R, Cohen W (2017) Transnets: learning to transform for recommendation. In: Proceedings of the eleventh ACM conference on recommender systems, pp 288–296

  15. Shang S, Ding R, Zheng K, Jensen S, Kalnis P, Zhou X (2014) Personalized trajectory matching in spatial networks. VLDB J 23(3):449–468

    Article  Google Scholar 

  16. Chen C, Zhang M, Liu Y, Ma S (2018) Neural attentional rating regression with review-level explanations. In: Proceedings of the 2018 world wide web conference, pp 1583–1592

  17. Chen J, Zhuang F, Hong X, Ao X, Xie X, He Q (2018) Attention-driven factor model for explainable personalized recommendation. In: The 41st international ACM SIGIR conference on research and development in information retrieval, pp 909–912

  18. Sun P, Wu L, Wang M (2018) Attentive recurrent social recommendation. In: The 41st international ACM SIGIR conference on research and development in information retrieval, pp 185–194

  19. Shi C, Li Y, Zhang J, Sun Y, Yu P (2016) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17–37

    Article  Google Scholar 

  20. Zhao H, Yao Q, Li J, Song Y, Lee D (2017) Meta-graph based recommendation fusion over heterogeneous information networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 635–644

  21. Guo Q, Sun Z, Zhang J, Theng Y (2020) An attentional recurrent neural network for personalized next location recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.  34 no. 01, pp 83–90

  22. Guo G, Chen B, Zhang X, Liu Z, Dong Z, He X (2020) Leveraging title-abstract attentive semantics for paper recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 01, pp 67–74

  23. Tang J, Wang K (2018) Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the eleventh ACM international conference on web search and data mining, pp 565–573

  24. Covington P, Adams J, Sargin E (2016) Deep neural networks for youtube recommendations. In: Proceedings of the 10th ACM conference on recommender systems, pp 191–198

  25. Liu J, Zhao P, Zhuang F, Liu Y, Sheng V, Xu J, Zhou X, Xiong H (2020) Exploiting aesthetic preference in deep cross networks for cross-domain recommendation. In: Proceedings of the web conference 2020, pp 2768–2774

  26. Loni B, Shi Y, Larson M, Hanjalic A (2014) Cross-domain collaborative filtering with factorization machines. In: European conference on information retrieval, pp 656–661

  27. Man T, Shen H, Jin X, Jin X, Cheng X (2017) Cross-domain recommendation: an embedding and mapping approach. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, pp 2464–2470

  28. Hu G, Zhang Y, Yang Q (2018) CoNet: collaborative cross networks for cross-domain recommendation. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 667–676

  29. Yang D, He J, Qin H, Xiao Y, Wang W (2015) A graph-based recommendation across heterogeneous domains. In: Proceedings of the 24th ACM international on conference on information and knowledge management, pp 463–472

  30. Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the AAAI conference on artificial intelligence, vol. 30, no. 01, pp 194–200

  31. Gao R, Li J, Li X, Song C, Chang J, Liu D, Wang C (2018) STSCR: Exploring spatial-temporal sequential influence and social information for location recommendation. Neurocomputing 319:118–133

    Article  Google Scholar 

  32. Ji M, Joo W, Song K, Kim Y, Moon IC (2020) Sequential Recommendation with Relation-Aware Kernelized Self-Attention. In: Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 04, pp 4304–4311

  33. Seo S, Huang J, Yang H, Liu Y (2017) Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In: Proceedings of the eleventh ACM conference on recommender systems, pp 297–305

  34. Chen J, Zhang H, He X, Nie L, Liu W, Chua T (2017) Attentive collaborative filtering: multimedia recommendation with item-and component-level attention. In: Proceedings of the 40th international ACM SIGIR conference on Research and development in information retrieval, pp 335–344

  35. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Aidan N, Lukasz K, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Systs 30:5998–6008

    Google Scholar 

  36. Chen Z, Wang X, Xie X, Wu T, Bu G, Wang Y, Chen E (2019) Co-attentive multi-task learning for explainable recommendation. In: Proceedings of the international joint conference on artificial intelligence, pp 2137–2143

  37. Yang Z, Yang D, Dyer C, He X, Smola A, Hovy E (2016) Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 1480–1489

  38. Zhang T, Zheng W, Cui Z, Zong Y, Li Y (2018) Spatial-temporal recurrent neural network for emotion recognition. IEEE Transactions on cybernetics 49(3):839–847

    Article  Google Scholar 

  39. Kim D, Park C, Oh J, Lee S, YU H (2016) Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM conference on recommender systems, pp 233–240

  40. Wu Y, DuBois C, Zheng AX, Ester M (2016) Collaborative denoising auto-encoders for top-n recommender systems. In: Proceedings of the ninth ACM international conference on web search and data mining, pp 153–162

  41. Chen L, Shang S, Yang C, Li J (2020) Spatial keyword search: a survey. GeoInformatica 24(1):85–106

    Article  Google Scholar 

  42. Shang S, Chen L, Jensen CS, Wen JR, Kalnis P (2017) Searching trajectories by regions of interest. IEEE Transactions on knowledge and data engineering 29(7):1549–1562

    Article  Google Scholar 

  43. Shang S, Chen L, Wei Z, Jensen CS, Wen JR, Kalnis P (2015) Collective travel planning in spatial networks. IEEE Transactions on knowledge and data engineering 28(5):1132–1146

    Article  Google Scholar 

  44. Liu J, Zhao K, Sommer P, Shang S, Kusy B, Lee JG, Jurdak R (2016) A novel framework for online amnesic trajectory compression in resource-constrained environments. IEEE Transactions on knowledge and data engineering 28(11):2827–2841

    Article  Google Scholar 

  45. Shang S, Chen L, Zheng K, Jensen C, Wei Z, Kalnis P (2019) Parallel trajectory-to-location join. IEEE Transactions on knowledge and data engineering 31(6):1194–1207

    Article  Google Scholar 

  46. Chen L, Shang S, Zhang Z, Cao X, Jensen C, Kalnis P (2018) Location-aware top-k term publish/subscribe. In: 2018 IEEE 34th international conference on data engineering (ICDE), pp 749–760

  47. Liu A, Wang W, Shang S, Li Q, Zhang X (2018) Efficient task assignment in spatial crowdsourcing with worker and task privacy protection. GeoInformatica 22(2):335–362

    Article  Google Scholar 

  48. Shang S, Chen L, Wei Z, Jensen C, Zheng K, Kalnis P (2017) Trajectory similarity join in spatial networks. In: Proceedings of the VLDB Endowment 10(11):1178–1189

    Article  Google Scholar 

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Acknowledgements

This work was supported by the China Postdoctoral Science Foundation funded project (2020M672413).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhuan, 430072, China

    Yangyang Xu & Zengmao Wang

  2. Thinvent Technology Co. LTD, Nanchang City, 330008, Jiangxi, China

    Jedi S. Shang

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  1. Yangyang Xu
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  2. Zengmao Wang
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  3. Jedi S. Shang
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Correspondence to Zengmao Wang.

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Xu, Y., Wang, Z. & Shang, J.S. PAENL: personalized attraction enhanced network learning for recommendation. Neural Comput & Applic 35, 3725–3735 (2023). https://doi.org/10.1007/s00521-021-05812-2

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