×

Optimized item selection to boost exploration for recommender systems. (English) Zbl 07493644

Stuckey, Peter J. (ed.), Integration of constraint programming, artificial intelligence, and operations research. 18th international conference, CPAIOR 2021, Vienna, Austria, July 5–8, 2021. Proceedings. Cham: Springer. Lect. Notes Comput. Sci. 12735, 427-445 (2021).
Summary: Recommender Systems have become the backbone of personalized services that provide tailored experiences to individual users. Still, data sparsity remains a common challenging problem, especially for new applications where training data is limited or not available. In this paper, we formalize a combinatorial problem that is concerned with selecting the universe of items for experimentation with recommender systems. On one hand, a large set of items is desirable to increase the diversity of items. On the other hand, a smaller set of items enable rapid experimentation and minimize the time and the amount of data required to train machine learning models. We show how to optimize for such conflicting criteria using a multi-level optimization framework. Our approach integrates techniques from discrete optimization, unsupervised clustering, and latent text embeddings. Experimental results on well-known movie and book recommendation benchmarks demonstrate the benefits of optimized item selection.
For the entire collection see [Zbl 1482.68041].

MSC:

68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
90C27 Combinatorial optimization
PDF BibTeX XML Cite
Full Text: DOI

References:

[1] Beasley, JE, An algorithm for set covering problem, Eur. J. Oper. Res., 31, 1, 85-93 (1987) · Zbl 0679.90039
[2] Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108-122 (2013)
[3] Caruana, R., Munson, A., Niculescu-Mizil, A.: Getting the most out of ensemble selection. In: Proceedings of the 6th IEEE International Conference on Data Mining (ICDM 2006), Hong Kong, China, 18-22 December 2006, pp. 828-833. IEEE Computer Society (2006)
[4] Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A.: Ensemble selection from libraries of models. In: Brodley, C.E. (ed.) Machine Learning, Proceedings of the Twenty-first International Conference (ICML 2004), Banff, Alberta, Canada, 4-8 July 2004. ACM International Conference Proceeding Series, vol. 69. ACM (2004)
[5] Cheng, H., et al.: Wide & deep learning for recommender systems. In: Karatzoglou, A., et al. (eds.) Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, DLRS@RecSys 2016, Boston, MA, USA, 15 September 2016, pp. 7-10. ACM (2016)
[6] Cohen, J., Statistical Power Analysis for the Behavioral Sciences (2013), Cambridge: Academic Press, Cambridge
[7] Covington, P., Adams, J., Sargin, E.: Deep neural networks for Youtube recommendations. In: Sen, S., Geyer, W., Freyne, J., Castells, P. (eds.) Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, 15-19 September 2016, pp. 191-198. ACM (2016)
[8] Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
[9] Goldberg, D.; Nichols, DA; Oki, BM; Terry, DB, Using collaborative filtering to weave an information tapestry, Commun. ACM, 35, 12, 61-70 (1992)
[10] Golub, GH; Reinsch, C.; Bauer, FL, Singular value decomposition and least squares solutions, Linear Algebra, Handbook for Automatic Computation, 134-151 (1971), Heidelberg: Springer, Heidelberg
[11] Grave, E., Bojanowski, P., Gupta, P., Joulin, A., Mikolov, T.: Learning word vectors for 157 languages. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018) (2018)
[12] Guo, H., Tang, R., Ye, Y., Li, Z., He, X.: DeepFM: a factorization-machine based neural network for CTR prediction. In: Sierra, C. (ed.) Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, Melbourne, Australia, 19-25 August 2017, pp. 1725-1731. ijcai.org (2017)
[13] Hansen, C., et al.: Contextual and sequential user embeddings for large-scale music recommendation. In: Santos, R.L.T., et al. (eds.) RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, 22-26 September 2020, pp. 53-62. ACM (2020)
[14] Harper, F.; Konstan, J., The MovieLens datasets: history and context, ACM Trans. Interact. Intell. Syst., 5, 4, 1-19 (2015)
[15] Hooker, JN, Integrated Methods for Optimization (2007), Boston: Springer, Boston · Zbl 1122.90002
[16] Forrester, J., et al.: coin-or/Cbc: Version 2.10.5, March 2020
[17] Jones, KS, A statistical interpretation of term specificity and its application in retrieval, J. Document., 28, 11-21 (1972)
[18] Kilitcioglu, D., Kadioglu, S.: Representing the unification of text featurization using a context-free grammar. In: Proceedings of the AAAI Conference on Artificial Intelligence (2021)
[19] Koren, Y.; Bell, RM; Volinsky, C., Matrix factorization techniques for recommender systems, Computer, 42, 8, 30-37 (2009)
[20] Lake, T., Williamson, S.A., Hawk, A.T., Johnson, C.C., Wing, B.P.: Large-scale collaborative filtering with product embeddings. CoRR abs/1901.04321 (2019)
[21] Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: International Conference on Machine Learning, pp. 1188-1196 (2014)
[22] Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, pp. 556-562 (2001)
[23] Lops, P.; de Gemmis, M.; Semeraro, G.; Ricci, F.; Rokach, L.; Shapira, B.; Kantor, PB, Content-based recommender systems: state of the art and trends, Recommender Systems Handbook, 73-105 (2011), Boston: Springer, Boston
[24] van der Maaten, L.; Hinton, G., Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 2579-2605 (2008) · Zbl 1225.68219
[25] McInnes, L., Healy, J., Melville, J.: UMAP: uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)
[26] Mehrotra, R., Shah, C., Carterette, B.A.: Investigating listeners’ responses to divergent recommendations. In: Santos, R.L.T., et al. (eds.) RecSys 2020: Fourteenth ACM Conference on Recommender Systems, Virtual Event, Brazil, 22-26 September 2020, pp. 692-696. ACM (2020)
[27] Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111-3119 (2013)
[28] Pan, SJ; Yang, Q., A survey on transfer learning, IEEE Trans. Knowl. Data Eng., 22, 10, 1345-1359 (2010)
[29] Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532-1543 (2014)
[30] Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Cremonesi, P., Ricci, F., Berkovsky, S., Tuzhilin, A. (eds.) Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, 27-31 August 2017, pp. 130-137. ACM (2017)
[31] Rendle, S.: Factorization machines. In: Webb, G.I., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds.) ICDM 2010, The 10th IEEE International Conference on Data Mining, Sydney, Australia, 14-17 December 2010, pp. 995-1000. IEEE Computer Society (2010)
[32] Ricci, F.; Rokach, L.; Shapira, B., Recommender Systems Handbook (2015), Boston: Springer, Boston · Zbl 1214.68392
[33] Ryan, TP; Morgan, J., Modern experimental design, J. Stat. Theory Pract., 1, 3-4, 501-506 (2007) · Zbl 1425.00047
[34] Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.) Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 3-6 December 2007, pp. 1257-1264. Curran Associates, Inc. (2007)
[35] Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1715-1725. Association for Computational Linguistics, Berlin, Germany, August 2016
[36] Strong, E., Kleynhans, B., Kadioglu, S.: MABWiser: a parallelizable contextual multi-armed bandit library for Python. In: 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI 2019), pp. 885-890. IEEE (2019). https://github.com/fidelity/mabwiser
[37] Thompson, WR, On the likelihood that one unknown probability exceeds another in view of the evidence of two samples, Biometrika, 25, 285-294 (1933) · JFM 59.1159.03
[38] Toffolo, T.A.M., Santos, H.G.: Python-MIP: Version 1.9.1. https://www.python-mip.com/
[39] Valko, M., Korda, N., Munos, R., Flaounas, I., Cristianini, N.: Finite-time analysis of kernelised contextual bandits. In: Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, pp. 654-663 (2013)
[40] Vazirani, VV, Approximation Algorithms (2001), Heidelberg: Springer, Heidelberg · Zbl 0999.68546
[41] Wan, M., McAuley, J.J.: Item recommendation on monotonic behavior chains. In: Pera, S., Ekstrand, M.D., Amatriain, X., O’Donovan, J. (eds.) Proceedings of the 12th ACM Conference on Recommender Systems, RecSys 2018, Vancouver, BC, Canada, 2-7 October 2018, pp. 86-94. ACM (2018)
[42] Wan, M., Misra, R., Nakashole, N., McAuley, J.J.: Fine-grained spoiler detection from large-scale review corpora. In: Korhonen, A., Traum, D.R., Màrquez, L. (eds.) Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Florence, Italy, 28 July-2 August 2019, Volume 1: Long Papers, pp. 2605-2610. Association for Computational Linguistics (2019)
[43] Wang, R., Fu, B., Fu, G., Wang, M.: Deep & cross network for ad click predictions. CoRR abs/1708.05123 (2017)
[44] Wu, C., Alvino, C.V., Smola, A.J., Basilico, J.: Using navigation to improve recommendations in real-time. In: Sen, S., Geyer, W., Freyne, J., Castells, P. (eds.) Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, 15-19 September 2016, pp. 341-348. ACM (2016)
[45] Zhang, S., Yao, L., Sun, A.: Deep learning based recommender system: a survey and new perspectives. CoRR abs/1707.07435 (2017)
[46] Zhao, L., Pan, S.J., Xiang, E.W., Zhong, E., Lu, Z., Yang, Q.: Active transfer learning for cross-system recommendation. In: DesJardins, M., Littman, M.L. (eds.) Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, Bellevue, Washington, USA, 14-18 July 2013. AAAI Press (2013)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.