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Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder

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Abstract

Context-aware recommender systems are intended primarily to consider the circumstances under which a user encounters an item to provide better-personalized recommendations. Users acquire point-of-interest, movies, products, and various online resources as suggestions. Classical collaborative filtering algorithms are shown to be satisfactory in a variety of recommendation activities processes, but cannot often capture complicated interactions between item and user, along with sparsity and cold start constraints. Hence it becomes a surge to apply a deep learning-based recommender model owing to its dynamic modeling potential and sustained success in other fields of application. In this work, a trust-based attentive contextual denoising autoencoder (TACDA) for enhanced Top-N context-aware recommendation is proposed. Specifically, the TCADA model takes the sparse preference of the user that is integrated with trust data as input into the autoencoder to prevail over the cold start and sparsity obstacle and efficiently accumulates the context condition into the model via attention framework. Thereby, the attention technique is used to encode context features into a latent space of the user's trust data that is integrated with their preferences, which interconnects personalized context circumstances with the active user's choice to deliver recommendations suited to that active user. Experiments conducted on Epinions, Caio, and LibraryThing datasets make it obvious the efficiency of the TACDA model persistently outperforms the state-of-the-art methods.

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Data Availability Statement

The datasets analyzed during the current study are available in the [Product Review Datasets: Epinions and Ciao] repository, [https://www.cse.msu.edu/~tangjili/datasetcode/truststudy.htm] and LibraryThing (a book review website) Dataset is available in [https://cseweb.ucsd.edu/~jmcauley/datasets.html#social_data].

References

  1. 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):734–749

    Article  Google Scholar 

  2. Huang R, Wang N, Han C, Yu F, Cui L (2020) TNAM: A tag-aware neural attention model for Top-N recommendation. Neurocomputing 385:1–12

    Article  Google Scholar 

  3. Unger M, Tuzhilin A, Livne A (2020) Context-aware recommendations based on deep learning frameworks. ACM Trans Manag Inf Syst (TMIS) 11(2):1–15

    Article  Google Scholar 

  4. Adomavicius G, Tuzhilin A (2011) Context-aware recommender systems. In: Ricchi F et al (eds) Recommender systems-handbook. Springer, Boston, MA, pp 217–253

    Chapter  Google Scholar 

  5. Bahdanau, D Cho K, and Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: ICLR

  6. Abinaya S, Devi MK (2021) Enhancing top-N recommendation using stacked autoencoder in context-aware recommender system. Neural Process Lett 53(3):1865–1888

    Article  Google Scholar 

  7. Sedhain S, Menon AK, Sanner S, & Xie L (2015) Autorec: autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on World Wide Web, pp. 111–112

  8. 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

  9. Jhamb Y, Ebesu T and Fang Y, (2018) Attentive contextual denoising autoencoder for recommendation. In: Proceedings of the 2018 ACM SIGIR international conference on theory of information retrieval, pp. 27–34

  10. Abinaya S, Kavitha Devi MK (2022) Trust-based context-aware collaborative filtering using denoising autoencoder. In: Ranganathan G et al (eds) Pervasive computing and social networking. Springer, Singapore, pp 35–49

    Chapter  Google Scholar 

  11. Guo G, Zhang J and Yorke-Smith N, (2015) Trustsvd: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the AAAI conference on artificial intelligence, Vol. 29

  12. Liu BYYLD and Liu J, (2013) Social collaborative filtering by trust. In: Proceedings of the twenty-third international joint conference on artificial intelligence, IEEE

  13. Ma H, (2014) On measuring social friend interest similarities in recommender systems. In: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pp. 465–474

  14. Kumar P, Thakur RS (2018) Recommendation system techniques and related issues: a survey. Int J Inf Technol 10(4):495–501

    Google Scholar 

  15. Deldjoo Y, Elahi M, Cremonesi P, Garzotto F, Piazzolla P, Quadrana M (2016) Content-based video recommendation system based on stylistic visual features. J Data Sem 5(2):99–113

    Article  Google Scholar 

  16. Anwar T, Uma V (2019b) Mrec-crm: movie recommendation based on collaborative filtering and rule mining approach. In: 2019 international conference

  17. Chen MH, Teng CH, Chang PC (2015) Applying artificial immune systems to collaborative filtering for movie recommendation. Adv Eng Inform 29(4):830–839

    Article  Google Scholar 

  18. Yan Y, Huang C, Wang Q, Hu B (2020) Data mining of customer choice behavior in internet of things within relationship network. Int J Inf Manag 50:566–574

    Article  Google Scholar 

  19. Ahmadian S, Afsharchi M, Meghdadi M (2019) A novel approach based on multi-view reliability measures to alleviate data sparsity in recommender systems. In: Multimedia tools and applications, pp 1–36

  20. Abinaya S, Kavitha Devi MK, Sherly Alphonse A (2022) Enhancing context-aware recommendation using hesitant fuzzy item clustering by stacked autoencoder based smoothing technique. Int J Uncertain Fuzz Knowl-Based Syst 30(04):595–624

    Article  Google Scholar 

  21. Hassan T (2019) Trust and trustworthiness in social recommender systems. In: Companion proceedings of the 2019 world wide web conference, pp. 529–532

  22. Wang M, Wu Z, Sun X, Feng G, Zhang B (2019) Trust-aware collaborative filtering with a denoising autoencoder. Neural Process Lett 49(2):835–849

    Article  Google Scholar 

  23. Parvin H, Moradi P, Esmaeili S (2019) TCFACO: trust-aware collaborative filtering method based on ant colony optimization. Expert Syst Appl 118:152–168

    Article  Google Scholar 

  24. Zhao Q, Zhang Y, Friedman D, & Tan F (2015) E-commerce recommendation with personalized promotion. In: Proceedings of the 9th ACM conference on recommender systems, pp. 219–226.

  25. Verma C, Hart M, Bhatkar S, Parker-Wood A, Dey S (2015) Improving scalability of personalized recommendation systems for enterprise knowledge workers. IEEE Access 4:204–215

    Article  Google Scholar 

  26. Alashkar T, Jiang S, Wang S, & Fu Y (2017). Examples-rules guided deep neural network for makeup recommendation. In: Proceedings of the AAAI conference on artificial intelligence, Vol. 31

  27. Chen C, Zhao P, Li L, Zhou J, Li X, & Qiu M (2017) Locally connected deep learning framework for industrial-scale recommender systems. In: Proceedings of the 26th international conference on World Wide Web companion (pp. 769–770)

  28. Elkahky AM, Song Y, & He X (2015). A multi-view deep learning approach for cross domain user modeling in recommendation systems. In: Proceedings of the 24th international conference on world wide web (pp. 278–288)

  29. He X, Du X, Wang X, Tian F, Tang J, & Chua TS (2018) Outer product-based neural collaborative filtering. arXiv preprint arXiv:1808.03912

  30. Christakopoulou K, Beutel A, Li R, Jain S, & Chi EH (2018) Q&R: a two-stage approach toward interactive recommendation. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 139–148

  31. Abdul-Rahman A, Hailes S (2000) Supporting trust in virtual communities. In: Proceedings of the 33rd Hawaii international conference on system sciences, Hawaii, USA

  32. Massa P, Avesani P (2004) Trust-aware collaborative filtering for recommender systems. In: Proceedings of the federated international conference on the move to meaningful internet

  33. Liang D, Krishnan RG, Hoffman MD, &Jebara T (2018) Variational autoencoders for collaborative filtering. In: Proceedings of the 2018 world wide web conference (pp. 689–698)

  34. Bathla G, Aggarwal H, Rani R (2020) AutoTrustRec: recommender system with social trust and deep learning using autoencoder. Multimed Tools Appl 79:845–860

    Article  Google Scholar 

  35. Pan Y, He F, & Yu H (2020) A correlative denoising autoencoder to model social influence for top-N recommender system. Front Comput Sci 14(3)

  36. Aghdam MH (2019) Context-aware recommender systems using hierarchical hidden markov model. Physica A 518:89–98n: Companion proceedings of the 2019 world wide web conference, ACM, pp 529–532

  37. Rezaeimehr F, Moradi P, Ahmadian S, Qader NN, Jalili M (2018) Tcars: time-and community-aware recommendation system. Future GenerComputSyst 78:419–429

    Google Scholar 

  38. Xia H, Luo Y, Liu Y (2021) Attention neural collaboration filtering based on GRU for recommender systems. Complex Intell Syst 7(3):1367–1379

    Article  Google Scholar 

  39. Papagelis M, Plexousakis D, Kutsuras T (2005) Alleviating the sparsity problem of collaborative filtering using trust inferences. In: 3rd international conference, iTrust 2005, proceedings DBLP, pp 224–239

  40. Wang J, Hu J, Qiao S, Sun W, Zang X, Zhang B (2016) Recommendation with implicit trust relationship based on users similarity. In: International conference on manufacturing science and information engineering (ICMSIE), pp 373–378

  41. Bahdanau D, Cho K, & Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473

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

    Article  Google Scholar 

  43. Wu Y, DuBois C, Zheng A X, Ester M (2016) Collaborative denoising autoencoders for top-N recommender systems. In: Proceedings of the 9th ACM international conference on web search and data mining. pp. 153–162

  44. Pan Y, He F, Yu H (2020) A correlative denoising autoencoder to model social influence for top-N recommender system. Front Comp Sci 14(3):1–13

    Google Scholar 

  45. Bathla G, Aggarwal H, Rani R (2020) AutoTrustRec: recommender system with social trust and deep learning using autoEncoder. Multimed Tools Appl 79(29):20845–20860

    Article  Google Scholar 

  46. Yengikand AK, Meghdadi M, Ahmadian S, Jalali SMJ, Khosravi A, & Nahavandi S (2021) Deep representation learning using multilayer perceptron and stacked autoencoder for recommendation systems. In: 2021 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 2485–2491). IEEE

  47. Kashani SMZ, & Hamidzadeh J (2020) Improvement of non-negative matrix-factorization-based and Trust-based approach to collaborative filtering for recommender systems. In: 2020 6th Iranian conference on signal processing and intelligent systems (ICSPIS) (pp. 1–7). IEEE

  48. Deng S, Huang L, Xu G, Wu X, Wu Z (2016) On deep learning for trust-aware recommendations in social networks. IEEE Trans Neural Netw Learn Syst 28(5):1164–1177

    Article  Google Scholar 

  49. Zhang Z, Liu Y, Jin Z, & Zhang R (2017) Selecting influential and trustworthy neighbors for collaborative filtering recommender systems. In: 2017 IEEE 7th annual computing and communication workshop and conference (CCWC) (pp. 1–7). IEEE

  50. Wan L, Xia F, Kong X, Hsu CH, Huang R, Ma J (2020) Deep matrix factorization for trust-aware recommendation in social networks. IEEE Trans Netw Sci Eng 8(1):511–528

    Article  Google Scholar 

  51. Pan Y, He F, Yu H (2020) Learning social representations with deep autoencoder for recommender system. World Wide Web 23(4):2259–2279

    Article  Google Scholar 

  52. Wu B, Wang L, Wang S, Zeng YR (2021) Forecasting the US oil markets based on social media information during the COVID-19 pandemic. Energy 226:120403

    Article  Google Scholar 

  53. Wu B, Wang L, Zeng YR (2022) Interpretable wind speed prediction with multivariate time series and temporal fusion transformers. Energy 252:123990

    Article  Google Scholar 

  54. Su X, Taghi MK (2009) Survey of collaborative filtering techniques. Adv Artif Intell 1:1–19

    Article  Google Scholar 

  55. Liu W, Wang Z, Liu X, Zeng N, Liu Y, Alsaadi FE (2016) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26. https://doi.org/10.1016/j.neucom.2016.12.038

    Article  Google Scholar 

  56. Kunaver M, Požrl T (2017) Diversity in recommender systems–a survey. Knowl-Based Syst 123:154–162

    Article  Google Scholar 

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Funding

The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript. No funding was received for conducting this study. No funds, grants, or other support was received.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

    S. Abinaya, A. Sherly Alphonse & S. Abirami

  2. Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India

    M. K. Kavithadevi

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  1. S. Abinaya
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  3. S. Abirami
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Contributions

Abinaya S. & Abirami S. wrote the main manuscript text and Sherly Alphonse A. and KavithaDevi M.K. prepared all the figures. All authors reviewed the manuscript.

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Correspondence to S. Abinaya.

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Abinaya, S., Alphonse, A.S., Abirami, S. et al. Enhancing Context-Aware Recommendation Using Trust-Based Contextual Attentive Autoencoder. Neural Process Lett 55, 6843–6864 (2023). https://doi.org/10.1007/s11063-023-11163-x

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