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Adaptive spectral graph wavelets for collaborative filtering

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Abstract

Collaborative filtering is a popular approach in recommender systems, whose objective is to provide personalized item suggestions to potential users based on their purchase or browsing history. However, personalized recommendations require considerable amount of behavioral data on users, which is usually unavailable for new users, giving rise to the cold-start problem. To help alleviate this challenging problem, we introduce a spectral graph wavelet collaborative filtering framework for implicit feedback data, where users, items and their interactions are represented as a bipartite graph. Specifically, we first propose an adaptive transfer function by leveraging a power transform with the goal of stabilizing the variance of graph frequencies in the spectral domain. Then, we design a deep recommendation model for efficient learning of low-dimensional embeddings of users and items using spectral graph wavelets in an end-to-end fashion. In addition to capturing the graph’s local and global structures, our approach yields localization of graph signals in both spatial and spectral domains and hence not only learns discriminative representations of users and items, but also promotes the recommendation quality. The effectiveness of our proposed model is demonstrated through extensive experiments on real-world benchmark datasets, achieving better recommendation performance compared with strong baseline methods.

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Notes

  1. https://github.com/wubinzzu/NeuRec/tree/master/dataset.

  2. https://github.com/wubinzzu/NeuRec.

References

  1. Boutemedjet S, Ziou D (2008) A graphical model for context-aware visual content recommendation. IEEE Trans Multimed 10(1):52–62

    Article  Google Scholar 

  2. Konstan J, Miller B, Maltz D, Herlocker J, Gordon L, Riedl J (1997) Grouplens: applying collaborative filtering to Usenet news. Commun ACM 40(3):77–87

    Article  Google Scholar 

  3. Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets. In: Proceedings of IEEE international conference on data mining, pp 263–272

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

    Article  Google Scholar 

  5. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme S (2009) BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of conference on uncertainty in artificial intelligence, pp 452–461

  6. Koren Y (2020) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of ACM SIGKDD international conference on knowledge discovery & data mining, pp 426–434

  7. Koren Y (2010) Factor in the neighbors: scalable and accurate collaborative filtering. ACM Trans Knowl Discov Data 4(1):1–24

    Article  MathSciNet  Google Scholar 

  8. He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of international conference on world wide web, pp 173–182

  9. Zheng L, Lu C-T, Jiang F, Zhang J, Yu PS (2018) Spectral collaborative filtering. In: Proceedings of ACM conference on recommender systems

  10. Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering. In: Proceedings of international ACM SIGIR conference on research and development in information retrieval, pp 165–174

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

  12. Wang X, Jin H, Zhang A, He X, Xu T, Chua T-S (2020) Disentangled graph collaborative filtering. In: Proceedings of international ACM SIGIR conference on research and development in information retrieval, pp 1001–1010

  13. Chang J, Gao C, He X, Li Y, Jin D (2020) Bundle recommendation with graph convolutional networks. In: Proceedings of international ACM SIGIR conference on research and development in information retrieval, pp 1673–1676

  14. Li Z, Xu Q, Jiang Y, Ma K, Cao X, Huang Q (2020) Neural collaborative preference learning with pairwise comparisons. IEEE Trans Multimed 23:1977–1989

    Article  Google Scholar 

  15. Jin J, Qin J, Fang Y, Du K, Zhang W, Yu Y, Zhang Z, Smola AJ (2020) An efficient neighborhood-based interaction model for recommendation on heterogeneous graph. In: Proceedings of ACM SIGKDD international conference on knowledge discovery & data mining, pp 75–84

  16. Chen L, Wu L, Hong R, Zhang K, Wang M (2020) Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: AAAI conference on artificial intelligence, pp 27–34

  17. Li Z, Xu Q, Jiang Y, Ma K, Cao X, Huang Q (2020) Neural collaborative preference learning with pairwise comparisons. IEEE Trans Multimed

  18. Sedhain S, Menon AK, Sanner S, Xie L (2015) AutoRec: autoencoders meet collaborative filtering. In: Proceedings of international conference on world wide web, pp 111–112

  19. Wang H, Wang N, Yeung D-Y (2015) Collaborative deep learning for recommender systems. In: Proceedings of ACM SIGKDD international conference on knowledge discovery & data mining, pp 1235–1244

  20. Hui B, Zhang L, Zhou X, Wen X, Nian Y (2022) Personalized recommendation system based on knowledge embedding and historical behavior. Appl Intell 52:954–966

    Article  Google Scholar 

  21. Kipf T, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations, pp 1–14

  22. Zhang H, McAuley J (2020) Stacked mixed-order graph convolutional networks for collaborative filtering. In: Proceedings of SIAM international conference on data mining, pp 73–81

  23. He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) LightGCN: simplifying and powering graph convolution network for recommendationg. In: Proceedings of international ACM SIGIR conference on research and development in information retrieval, pp 639–648

  24. Yu J, Yin, H, Xia X, Chen T, Cui L, Nguyen QVH (2022) Are graph augmentations necessary? Simple graph contrastive learning for recommendation. In: Proceedings of international ACM SIGIR conference on research and development in information retrieval, pp 1294–1303

  25. Xia L, Huang C, Xu Y, Zhao J, Yin D, Huang J (2022) Hypergraph contrastive collaborative filtering. In: Proceedings of international ACM SIGIR conference on research and development in information retrieval, pp 70–79

  26. Liu Z, Meng L, Jiang F, Zhang J, Yu PS (2022) Deoscillated adaptive graph collaborative filtering. In: Proceedings of machine learning research, pp 248–257

  27. Song Y, Ye H, Li M, Cao F (2022) Deep multi-graph neural networks with attention fusion for recommendation. Expert Syst Appl 191:954–966

    Article  Google Scholar 

  28. Qian S, Xue D, Zhang H, Fang Q, Xu C (2021) Dual adversarial graph neural networks for multi-label cross-modal retrieval. In: Proceedings of AAAI conference on artificial intelligence, pp 2440–2448

  29. Qian S, Xue D, Fang Q, Xu C (2023) Deep multi-graph neural networks with attention fusion for recommendation. IEEE Trans Pattern Anal Mach Intell 45:4794–4811

    PubMed  Google Scholar 

  30. Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation. In: Proceedings of international conference on World Wde Web, pp 417–426

  31. Wu J, Wang X, Feng F, He X, Chen L, Lian J, Xie X (2021) Hypergraph contrastive collaborative filtering. In: Proceedings of international ACM SIGIR conference on research and development in information Retrieval, pp 726–735

  32. Defferrard M, Bresson X, Vandergheynst P (2016) Convolutional neural networks on graphs with fast localized spectral filtering. Adv Neural Inf Process pp 3844–3852

  33. Wu F, Souza AH, Zhang T, Fifty C, Yu T, Weinberger KQ (2019) Simplifying graph convolutional networks. In: Proceedings of international conference on machine learning, pp 6861–6871

  34. Godsil C, Royle G (2001) Algebraic graph theory. Springer-Verlag, New York

    Book  Google Scholar 

  35. Krim H, Ben Hamza A (2015) Geometric methods in signal and image analysis. Cambridge University Press

    Book  Google Scholar 

  36. Hammond D, Vandergheynst P, Gribonval R (2011) Wavelets on graphs via spectral graph theory. Appl Comput Harmonic Anal 30(2):129–150

    Article  MathSciNet  Google Scholar 

  37. Li C, BenHamza A (2013) A multiresolution descriptor for deformable 3D shape retrieval. Vis Comput 29:513–524

    Article  Google Scholar 

  38. Donnat C, Zitnik M, Hallac D, Leskovec J (2018) Learning structural node embeddings via diffusion wavelets. In: Proceedings of ACM SIGKDD international conference on knowledge discovery & data mining, pp 1320–1329

  39. Xu B, Shen H, Cao Q, Qiu Y, Cheng X (2019) Graph wavelet neural network. In: International conference on learning representations

  40. Yeo I, Johnson R (2000) A new family of power transformations to improve normality or symmetry. Biometrika 87:954–959

    Article  MathSciNet  Google Scholar 

  41. Harper F, Konstan J (2015) The MovieLens datasets: history and context. ACM Trans Interact Intell Syst 5(4):1–19

    Article  Google Scholar 

  42. Ni J, Li J, McAuley J (2019) Justifying recommendations using distantly-labeled reviews and fined-grained aspects. In: Proceedings of EMNLP-IJCNLP, pp 188–197

  43. Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst 20(4):422–446

    Article  Google Scholar 

  44. Xue F, He X, Wang X, Xu J, Liu K, Hong R (2019) Deep item-based collaborative filtering for top-n recommendation. ACM Trans Inf Syst 37(3):1–25

    Article  Google Scholar 

  45. He X, Du X, Wang X, Tian F, Tang J, Chua T-S (2018) Outer product-based neural collaborative filtering. In: Proceedings of international joint conference on artificial intelligence, pp 2227–2233

  46. Chae D-K, Kang J-S, Kim S-W, Lee J-T (2018) CFGAN: a generic collaborative filtering framework based on generative adversarial networks. In: Proceedings of ACM international conference on information and knowledge management, pp 137–146

  47. Berg R, Kipf TN, Welling M (2018) Graph convolutional matrix completion. In: Proceedings of ACM SIGKDD international conference on knowledge discovery & data mining

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  1. Adaptive Spectral GraphWavelets for Collaborative Filtering, Concordia Institute for Information Systems Engineering Concordia University, Montreal, QC, Canada

    Osama Alshareet & A. Ben Hamza

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  1. Osama Alshareet
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  2. A. Ben Hamza
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Correspondence to A. Ben Hamza.

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Alshareet, O., Ben Hamza, A. Adaptive spectral graph wavelets for collaborative filtering. Pattern Anal Applic 27, 10 (2024). https://doi.org/10.1007/s10044-024-01214-x

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