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Syst."],"published-print":{"date-parts":[[2019,1,31]]},"abstract":"<jats:p>With the availability of abundant online multi-relational video information, recommender systems that can effectively exploit these sorts of data and suggest creatively interesting items will become increasingly important. Recent research illustrates that tensor models offer effective approaches for complex multi-relational data learning and missing element completion. So far, most tensor-based user clustering models have focused on the accuracy of recommendation. Given the dynamic nature of online media, recommendation in this setting is more challenging as it is difficult to capture the users\u2019 dynamic topic distributions in sparse data settings as well as to identify unseen items as candidates of recommendation. Targeting at constructing a recommender system that can encourage more creativity, a deep Bayesian probabilistic tensor framework for tag and item recommendation is proposed. During the score ranking processes, a metric called Bayesian surprise is incorporated to increase the creativity of the recommended candidates. The new algorithm, called Deep Canonical PARAFAC Factorization (DCPF), is evaluated on both synthetic and large-scale real-world problems. An empirical study for video recommendation demonstrates the superiority of the proposed model and indicates that it can better capture the latent patterns of interactions and generates interesting recommendations based on creative tag combinations.<\/jats:p>","DOI":"10.1145\/3233773","type":"journal-article","created":{"date-parts":[[2018,12,13]],"date-time":"2018-12-13T15:45:03Z","timestamp":1544715903000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["A Deep Bayesian Tensor-Based System for Video Recommendation"],"prefix":"10.1145","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2600-6905","authenticated-orcid":false,"given":"Wei","family":"Lu","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China, Chengdu, China"}]},{"given":"Fu-Lai","family":"Chung","sequence":"additional","affiliation":[{"name":"Hong Kong Polytechnic University, Kowloon, Hong Kong"}]},{"given":"Wenhao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Tencent AI Lab, Shenzhen, China"}]},{"given":"Martin","family":"Ester","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Canada"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"Tencent AI Lab, Shenzhen, China"}]}],"member":"320","published-online":{"date-parts":[[2018,12,13]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2559952"},{"key":"e_1_2_1_2_1","volume-title":"Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems","author":"Balan Anoop Korattikara","year":"2015"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2009.12.007"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","volume-title":"Learning Deep Architectures for AI","author":"Bengio Yoshua","DOI":"10.1561\/9781601982957"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asr013"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"key":"e_1_2_1_7_1","volume-title":"Proceedings of the 32nd International Conference on Machine Learning (ICML\u201915)","author":"Blundell Charles","year":"2015"},{"key":"e_1_2_1_8_1","volume-title":"Long Fail, and Long Play in the Digital Music Space","author":"Celma \u00d2scar"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.19"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1137\/110859063"},{"key":"e_1_2_1_11_1","volume-title":"Proceedings of the 12th International Conference on Artificial Intelligence and Statistics. 89\u201396","author":"Chu Wei","year":"2009"},{"key":"e_1_2_1_12_1","volume-title":"Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-Way Data Analysis and Blind Source Separation","author":"Cichocki Andrzej"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the 20th European Conference on Artificial Intelligence. 21\u201326","author":"Colton Simon","year":"2012"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2156801"},{"key":"e_1_2_1_15_1","volume-title":"4th International Conference on Learning Representations (ICLR'16)","author":"Gal Yarin","year":"2016"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959151"},{"key":"e_1_2_1_17_1","unstructured":"Thomas Griffiths and Zoubin Ghahramani. 2005. 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