{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:58:11Z","timestamp":1772557091067,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:00:00Z","timestamp":1658188800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:00:00Z","timestamp":1658188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005416","name":"Norges Forskningsr\u00e5d","doi-asserted-by":"publisher","award":["294330"],"award-info":[{"award-number":["294330"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005416","name":"Norges Forskningsr\u00e5d","doi-asserted-by":"publisher","award":["237718"],"award-info":[{"award-number":["237718"]}],"id":[{"id":"10.13039\/501100005416","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>We consider the problem of recommending relevant content to users of an internet platform in the form of lists of items, called slates. We introduce a variational Bayesian Recurrent Neural Net recommender system that acts on time series of interactions between the internet platform and the user, and which scales to real world industrial situations. The recommender system is tested both online on real users, and on an offline dataset collected from a Norwegian web-based marketplace, FINN.no, that is made public for research. This is one of the first publicly available datasets which includes all the slates that are presented to users as well as which items (if any) in the slates were clicked on. Such a data set allows us to move beyond the common assumption that implicitly assumes that users are considering all possible items at each interaction. Instead we build our likelihood using the items that are actually in the slate, and evaluate the strengths and weaknesses of both approaches theoretically and in experiments. We also introduce a hierarchical prior for the item parameters based on group memberships. Both item parameters and user preferences are learned probabilistically. Furthermore, we combine our model with bandit strategies to ensure learning, and introduce \u2018in-slate Thompson sampling\u2019 which makes use of the slates to maximise explorative opportunities. We show experimentally that explorative recommender strategies perform on par or above their greedy counterparts. Even without making use of exploration to learn more effectively, click rates increase simply because of improved diversity in the recommended slates.<\/jats:p>","DOI":"10.1007\/s10618-022-00849-w","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T18:02:45Z","timestamp":1658253765000},"page":"1756-1786","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Dynamic slate recommendation with gated recurrent units and Thompson sampling"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4290-4684","authenticated-orcid":false,"given":"Simen","family":"Eide","sequence":"first","affiliation":[]},{"given":"David S.","family":"Leslie","sequence":"additional","affiliation":[]},{"given":"Arnoldo","family":"Frigessi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"key":"849_CR1","doi-asserted-by":"publisher","unstructured":"Abdollahpouri H, Burke R, Mobasher B (2017) Controlling popularity bias in learning-to-rank recommendation. In: RecSys 2017 - Proceedings of the 11th ACM Conference on Recommender Systems pp 42\u201346. https:\/\/doi.org\/10.1145\/3109859.3109912","DOI":"10.1145\/3109859.3109912"},{"key":"849_CR2","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv: 1603.04467"},{"key":"849_CR3","unstructured":"Balandat M, Karrer B, Jiang DR, Daulton S, Letham B, Wilson AG, Bakshy E (2020) BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. Advances in Neural Information Processing Systems 33:21524\u201321538. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/f5b1b89d98b7286673128a5fb112cb9a-Abstract.html"},{"key":"849_CR4","unstructured":"Bello I, Kulkarni S, Jain S, Boutilier C, Chi E, Eban E, Luo X, Mackey A (2018) Seq2Slate: Re-ranking and slate optimization with RNNs. arXiv: 1810.02019"},{"key":"849_CR5","unstructured":"Bingham E, Chen JP, Jankowiak M, Obermeyer F, Pradhan N, Karaletsos T, Singh R, Horsfall P, Goodman ND (2018) Pyro: Deep universal probabilistic programming. J of Machine Learning Res 20(Xxxx):0\u20135"},{"key":"849_CR6","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2017.1285773","author":"DM Blei","year":"2017","unstructured":"Blei DM, Kucukelbir A, McAuliffe JD (2017). Variational Inference: A Review for Statisticians. https:\/\/doi.org\/10.1080\/01621459.2017.1285773","journal-title":"Variational Inference: A Review for Statisticians."},{"key":"849_CR7","unstructured":"Blundell C, Cornebise J, Kavukcuoglu K, Wierstra D (2015) Weight uncertainty in neural networks. 32nd International Conference on Machine Learning. ICML 2:1613\u20131622"},{"key":"849_CR8","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240370","author":"AJ Chaney","year":"2017","unstructured":"Chaney AJ, Stewart BM, Engelhardt BE (2017). How algorithmic confounding in recommendation systems increases homogeneity and decreases utility. https:\/\/doi.org\/10.1145\/3240323.3240370","journal-title":"How algorithmic confounding in recommendation systems increases homogeneity and decreases utility."},{"key":"849_CR9","doi-asserted-by":"publisher","unstructured":"Cheng HT, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anderson G, Corrado G, Chai W, Ispir M, Anil R, Haque Z, Hong L, Jain V, Liu X, Shah H (2016) Wide & Deep Learning for Recommender Systems pp 1\u20134. https:\/\/doi.org\/10.1145\/2988450.2988454","DOI":"10.1145\/2988450.2988454"},{"key":"849_CR10","doi-asserted-by":"publisher","unstructured":"Cho K, van Merrienboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Stroudsburg, PA, USA, pp 1724\u20131734. https:\/\/doi.org\/10.3115\/v1\/D14-1179","DOI":"10.3115\/v1\/D14-1179"},{"key":"849_CR11","doi-asserted-by":"publisher","unstructured":"Covington P, Adams J, Sargin E (2016) Deep Neural Networks for YouTube Recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems - RecSys \u201916 pp 191\u2013198. https:\/\/doi.org\/10.1145\/2959100.2959190","DOI":"10.1145\/2959100.2959190"},{"key":"849_CR12","unstructured":"Criteo (2020) Criteo 1TB Click Logs dataset. https:\/\/ailab.criteo.com\/download-criteo-1tb-click-logs-dataset\/"},{"key":"849_CR13","doi-asserted-by":"publisher","unstructured":"Edwards JA, Leslie, DS (2018) Diversity as a Response to User Preference Uncertainty. In: Statistical Data Science, WORLD SCIENTIFIC (EUROPE), pp 55\u201368. https:\/\/doi.org\/10.1142\/9781786345400_0004","DOI":"10.1142\/9781786345400_0004"},{"key":"849_CR14","doi-asserted-by":"publisher","unstructured":"Edwards JA, Leslie DS (2019) Selecting multiple web adverts: A contextual multi-armed bandit with state uncertainty. Journal of the Operational Research Society pp 1\u201317. https:\/\/doi.org\/10.1080\/01605682.2018.1546650","DOI":"10.1080\/01605682.2018.1546650"},{"key":"849_CR15","doi-asserted-by":"publisher","unstructured":"Eide S, Zhou N (2018) Deep neural network marketplace recommenders in online experiments. In: Proceedings of the 12th ACM Conference on Recommender Systems, ACM, New York, NY, USA, pp 387\u2013391. https:\/\/doi.org\/10.1145\/3240323.3240387","DOI":"10.1145\/3240323.3240387"},{"key":"849_CR16","unstructured":"Gal Y, Mcallister RT, Rasmussen CE (2016) Improving PILCO with Bayesian Neural Network Dynamics Models. Data-Efficient Machine Learning Workshop, ICML pp 1\u20137"},{"key":"849_CR17","doi-asserted-by":"publisher","unstructured":"Gopalan P, Hofman JM, Blei DM (2013) Scalable Recommendation with Poisson Factorization pp 1\u201310. https:\/\/doi.org\/10.1002\/jae. arXiv: 1311.1704","DOI":"10.1002\/jae"},{"key":"849_CR18","doi-asserted-by":"publisher","unstructured":"Guo D, Ktena SI, Myana PK, Huszar F, Shi W, Tejani A, Kneier M, Das S (2020) Deep Bayesian Bandits: Exploring in Online Personalized Recommendations. In: Fourteenth ACM Conference on Recommender Systems, ACM, New York, NY, USA, pp 456\u2013461. https:\/\/doi.org\/10.1145\/3383313.3412214","DOI":"10.1145\/3383313.3412214"},{"key":"849_CR19","unstructured":"Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016) Session-based recommendations with recurrent neural networks. In: 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings"},{"key":"849_CR20","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1145\/3038912.3052639","volume":"2017","author":"CK Hsieh","year":"2017","unstructured":"Hsieh CK, Yang L, Cui Y, Lin TY, Belongie S, Estrin D (2017) Collaborative metric learning. 26th International World Wide Web Conference. WWW 2017:193\u2013201. https:\/\/doi.org\/10.1145\/3038912.3052639","journal-title":"WWW"},{"key":"849_CR21","doi-asserted-by":"publisher","unstructured":"Hu Y, Koren Y, Volinsky C (2008) Collaborative Filtering for Implicit Feedback Datasets. In: 2008 Eighth IEEE International Conference on Data Mining, IEEE pp 263\u2013272. https:\/\/doi.org\/10.1109\/ICDM.2008.22","DOI":"10.1109\/ICDM.2008.22"},{"key":"849_CR22","doi-asserted-by":"publisher","unstructured":"Ie E, Jain V, Wang J, Narvekar S, Agarwal R, Wu R, Cheng HT, Chandra T, Boutilier C (2019) SLateq: A tractable decomposition for reinforcement learning with recommendation sets. IJCAI International Joint Conference on Artificial Intelligence 2019-Augus:2592\u20132599. https:\/\/doi.org\/10.24963\/ijcai.2019\/360","DOI":"10.24963\/ijcai.2019\/360"},{"key":"849_CR23","unstructured":"Kula M (2015) Metadata Embeddings for User and Item Cold-start Recommendations. CEUR Workshop Proc 1448:14\u201321 arXiv: 1507.08439"},{"key":"849_CR24","doi-asserted-by":"crossref","unstructured":"Lattimore T, Szepesv\u00e1ri C (2020) Bandit algorithms. Cambridge University Press","DOI":"10.1017\/9781108571401"},{"issue":"6","key":"849_CR25","doi-asserted-by":"publisher","first-page":"1548","DOI":"10.1007\/s10618-019-00632-4","volume":"33","author":"H Li","year":"2019","unstructured":"Li H, Liu Y, Qian Y, Mamoulis N, Tu W, Cheung DW (2019) HHMF: Hidden hierarchical matrix factorization for recommender systems. Data Mining and Knowledge Discovery 33(6):1548\u20131582. https:\/\/doi.org\/10.1007\/s10618-019-00632-4","journal-title":"Data Mining and Knowledge Discovery"},{"key":"849_CR26","doi-asserted-by":"publisher","unstructured":"Li S, Karatzoglou A, Gentile C (2016) Collaborative Filtering Bandits. Sigir pp 539\u2013548. https:\/\/doi.org\/10.1145\/2911451.2911548","DOI":"10.1145\/2911451.2911548"},{"key":"849_CR27","unstructured":"Liang D, Charlin L, Blei DM (2016) Causal Inference for Recommendation. Conference on Uncertainty in Artificial Intelligence"},{"key":"849_CR28","doi-asserted-by":"publisher","unstructured":"Linden G, Smith B, York J (2003) Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput 7(1):76\u201380. https:\/\/doi.org\/10.1109\/MIC.2003.1167344","DOI":"10.1109\/MIC.2003.1167344"},{"key":"849_CR29","doi-asserted-by":"publisher","unstructured":"Ludewig M, Jannach D (2018) Evaluation of session-based recommendation algorithms. User Modeling and User-Adapted Interaction 28(4\u20135):331\u2013390. https:\/\/doi.org\/10.1007\/s11257-018-9209-6","DOI":"10.1007\/s11257-018-9209-6"},{"key":"849_CR30","unstructured":"Mandt S, McInerney J, Abrol F, Ranganath R, Blei D (2016) Variational tempering. Proc of the 19th Int Conf on Artificial Intell and Statistics, AISTATS 2016 41:704\u2013712"},{"key":"849_CR31","doi-asserted-by":"publisher","first-page":"101682","DOI":"10.1109\/ACCESS.2020.2998824","volume":"8","author":"AN Ngaffo","year":"2020","unstructured":"Ngaffo AN, Ayeb WE, Choukair Z (2020) A Bayesian Inference Based Hybrid Recommender System. IEEE Access 8:101682\u2013101701. https:\/\/doi.org\/10.1109\/ACCESS.2020.2998824","journal-title":"IEEE Access"},{"key":"849_CR32","unstructured":"Park S, Kim YD, Choi S (2013) Hierarchical Bayesian matrix factorization with side information. IJCAI International Joint Conference on Artificial Intelligence pp 1593\u20131599"},{"key":"849_CR33","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, K\u00f6pf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 32(NeurIPS)"},{"key":"849_CR34","first-page":"814","volume":"33","author":"R Ranganath","year":"2014","unstructured":"Ranganath R, Gerrish S, Blei DM (2014) Black box variational inference. J of Machine Learning Res 33:814\u2013822","journal-title":"J of Machine Learning Res"},{"key":"849_CR35","first-page":"07901","volume":"2103","author":"N Rekabsaz","year":"2021","unstructured":"Rekabsaz N, Lesota O, Schedl M, Brassey J, Eickhoff C (2021) TripClick: The Log Files of a Large Health Web Search Engine, vol 1. Association for Computing Machinery. arXiv 2103:07901","journal-title":"Association for Computing Machinery. arXiv"},{"key":"849_CR36","doi-asserted-by":"publisher","unstructured":"Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) BPR: Bayesian Personalized Ranking from Implicit Feedback. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence pp 452\u2013461. https:\/\/doi.org\/10.5555\/1795114.1795167","DOI":"10.5555\/1795114.1795167"},{"key":"849_CR37","doi-asserted-by":"publisher","unstructured":"Russo DJ, Van\u00a0Roy B, Kazerouni A, Osband I, Wen Z (2018) A Tutorial on Thompson Sampling. Foundations and Trends\u00ae in Machine Learning 11(1):1\u201396. https:\/\/doi.org\/10.1561\/2200000070","DOI":"10.1561\/2200000070"},{"key":"849_CR38","doi-asserted-by":"publisher","unstructured":"Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In: Proceedings of the 25th international conference on Machine learning - ICML \u201908, ACM Press, New York, New York, USA, pp 880\u2013887. https:\/\/doi.org\/10.1145\/1390156.1390267","DOI":"10.1145\/1390156.1390267"},{"key":"849_CR39","doi-asserted-by":"publisher","unstructured":"Tran VA, Hennequin R, Royo-Letelier J, Moussallam M (2019) Improving collaborative metric learning with efficient negative sampling. In: SIGIR 2019 - Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval pp 1201\u20131204. https:\/\/doi.org\/10.1145\/3331184.3331337","DOI":"10.1145\/3331184.3331337"},{"key":"849_CR40","unstructured":"Wenzel F, Roth K, Veeling BS, \u015awia\u0327tkowski J, Tran L, Mandt S, Snoek J, Salimans T, Jenatton R, Nowozin S (2020) How Good is the Bayes Posterior in Deep Neural Networks Really? (1), arXiv: 2002.02405"},{"key":"849_CR41","doi-asserted-by":"publisher","unstructured":"Ying H, Zhuang F, Zhang F, Liu Y, Xu G, Xie X, Xiong H, Wu J (2018) Sequential recommender system based on hierarchical attention network. IJCAI International Joint Conference on Artificial Intelligence 2018-July(July):3926\u20133932. https:\/\/doi.org\/10.24963\/ijcai.2018\/546","DOI":"10.24963\/ijcai.2018\/546"},{"key":"849_CR42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2889774","author":"C Zhang","year":"2018","unstructured":"Zhang C, Butepage J, Kjellstrom H, Mandt S (2018). Advances in Variational Inference. https:\/\/doi.org\/10.1109\/TPAMI.2018.2889774","journal-title":"Advances in Variational Inference."},{"key":"849_CR43","doi-asserted-by":"publisher","unstructured":"Zhang Y, Koren J (2007) Efficient bayesian hierarchical user modeling for recommendation system. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR\u201907 pp 47\u201354. https:\/\/doi.org\/10.1145\/1277741.1277752","DOI":"10.1145\/1277741.1277752"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00849-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-022-00849-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-022-00849-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,6]],"date-time":"2022-10-06T10:10:29Z","timestamp":1665051029000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-022-00849-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,19]]},"references-count":43,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["849"],"URL":"https:\/\/doi.org\/10.1007\/s10618-022-00849-w","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,19]]},"assertion":[{"value":"29 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The code and the data used in the article are available in the following repository: .","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}}]}}