Abstract
Most current neural recommender systems for session-based data cast recommendations as a sequential or graph traversal problem, applying recurrent networks (LSTM/GRU) or graph neural networks (GNN). This makes the systems increasingly elaborate in order to model complex user/item connection networks and results in poor scalability to large item spaces and long item view/click sequences. Instead on focusing on the sequential nature of session-based recommendation, we propose to cast it as a density estimation problem on item sets. We introduce EMDE (Efficient Manifold Density Estimator) - a method utilizing arbitrary vector representations with the property of local similarity to succinctly represent smooth probability densities on Riemannian manifolds using compressed representations we call sketches. Within EMDE, session behaviors are represented with weighted item sets, largely simplifying the sequential aspect of the problem. Applying EMDE to both top-k and session-based recommendation settings, we establish new state-of-the-art results on multiple open datasets in both uni-modal and multi-modal settings. EMDE has also been applied to many other tasks and areas in top machine learning competitions involving recommendations and graph processing, taking the podium in KDD Cup 2021, WSDM Challenge 2021, and SIGIR eCom Challenge 2020. We release the code at https://github.com/emde-conf/emde-session-rec.
J. Dąbrowski and B. Rychalska—Equal contribution.
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Notes
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Standard configuration recommended by [16].
References
Backurs, A., Indyk, P., Wagner, T.: Space and time efficient kernel density estimation in high dimensions. In: NeurIPS (2019)
Ben-Shimon, D., Tsikinovsky, A., Friedmann, M., Shapira, B., Rokach, L., Hoerle, J.: Recsys challenge 2015 and the yoochoose dataset. In: RecSys (2015)
Bennett, J., Lanning, S., Netflix, N.: The netflix prize. In: In KDD Cup and Workshop in Conjunction with KDD (2007)
Charikar, M., Siminelakis, P.: Hashing-based-estimators for kernel density in high dimensions. In: 2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS) (2017)
Chen, T., Wong, R.C.W.: Handling information loss of graph neural networks for session-based recommendation (2020)
Coleman, B., Shrivastava, A.: Sub-linear race sketches for approximate kernel density estimation on streaming data, pp. 1739–1749, April 2020
Coleman, B., Shrivastava, A., Baraniuk, R.G.: Race: sub-linear memory sketches for approximate near-neighbor search on streaming data (2019)
Cormode, G., Muthukrishnan, S.: An improved data stream summary: the count-min sketch and its applications. In: Farach-Colton, M. (ed.) LATIN 2004: Theoretical Informatics (2004)
Dacrema, M.F., Cremonesi, P., Jannach, D.: Are we really making much progress? a worrying analysis of recent neural recommendation approaches. In: RecSys (2019)
Dognin, P., Melnyk, I., Mroueh, Y., Ross, J., Santos, C.D., Sercu, T.: Wasserstein barycenter model ensembling (2019)
Greengard, L., Strain, J.: The fast gauss transform (1991)
Hidasi, B., Karatzoglou, A.: Recurrent neural networks with top-k gains for session-based recommendations. In: CIKM (2018)
Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Conference Proceedings of the Annual ACM Symposium on Theory of Computing (2000)
Itoh, M., Satoh, H.: Geometric mean of probability measures and geodesics of fisher information metric (2017)
Kamehkhosh, I., Jannach, D., Ludewig, M.: A comparison of frequent pattern techniques and a deep learning method for session-based recommendation. In: RecTemp@RecSys (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) ICLR (20150
LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010). http://yann.lecun.com/exdb/mnist/
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, CIKM 2017. Association for Computing Machinery, New York (2017)
Liang, D., Krishnan, R.G., Hoffman, M.D., Jebara, T.: Variational autoencoders for collaborative filtering. In: WWW (2018)
Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: Stamp: short-term attention/memory priority model for session-based recommendation. In: KDD (2018)
Ludewig, M., Jannach, D.: Evaluation of session-based recommendation algorithms. User Model. User-Adap. Inter. 28(4–5), 331–390 (2018)
Ludewig, M., Mauro, N., Latifi, S., Jannach, D.: Performance comparison of neural and non-neural approaches to session-based recommendation. In: RecSys (2019)
Mi, F., Faltings, B.: Context tree for adaptive session-based recommendation. http://arxiv.org/abs/1806.03733
Ning, X., Karypis, G.: Slim: sparse linear methods for top-n recommender systems. In: ICDM (2011)
Paudel, B., Christoffel, F., Newell, C., Bernstein, A.: Updatable, accurate, diverse, and scalable recommendations for interactive applications. ACM Trans. Interact. Intell, Syst 7, 1–34 (2016)
Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP) (2014)
Ruocco, M., Skrede, O.S.L., Langseth, H.: Inter-session modeling for session-based recommendation. In: DLRS (2017)
Rychalska, B., Bąbel, P., Gołuchowski, K., Michałowski, A., Dabrowski, J.: Cleora: a simple, strong and scalable graph embedding scheme. arXiv https://arxiv.org/abs/2102.02302 (2020)
Siminelakis, P., Rong, K., Bailis, P., Charikar, M., Levis, P.: Rehashing kernel evaluation in high dimensions. In: International Conference on Machine Learning (2019)
Steck, H.: Embarrassingly shallow autoencoders for sparse data. In: WWW (2019)
Tallec, C., Ollivier, Y.: Unbiasing truncated backpropagation through time. arXiv preprint arXiv:1705.08209 (2017)
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: AAAI (2019)
Yu, F., Zhu, Y., Liu, Q., Wu, S., Wang, L., Tan, T.: TAGNN: target attentive graph neural networks for session-based recommendation. association for computing machinery (2020)
Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J.M., He, X.: A simple but hard-to-beat baseline for session-based recommendations (2018), http://arxiv.org/abs/1808.05163
Acknowledgements
Barbara Rychalska was supported by grant no 2018/31/N/ST6/02273 funded by National Science Centre, Poland.
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Dąbrowski, J. et al. (2021). An Efficient Manifold Density Estimator for All Recommendation Systems. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_27
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