Computational creativity based video recommendation

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Computational creativity, as an emerging domain of application, emphasizes the use of big data to automatically design new knowledge. Based on the availability of complex multi-relational data, one aspect of computational creativity is to infer unexplored regions of feature space and novel learning paradigm, which is particularly useful for online recommendation. Tensor models offer effective approaches for complex multi-relational data learning and missing element completion. Targeting at constructing a recommender system that can compromise between accuracy and creativity for users, a deep Bayesian probabilistic tensor framework for tag and item recommending is adopted. Empirical results demonstrate the superiority of the proposed method and indicate that it can better capture latent patterns of interaction relationships and generate interesting recommendations based on creative tag combinations.
Original languageEnglish
Title of host publicationSIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages793-796
Number of pages4
ISBN (Electronic)9781450342902
DOIs
Publication statusPublished - 7 Jul 2016
Event39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016 - Pisa, Italy
Duration: 17 Jul 201621 Jul 2016

Conference

Conference39th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2016
CountryItaly
CityPisa
Period17/07/1621/07/16

Keywords

  • Bayesian tensor factorization
  • Recommendation
  • Serendipity

ASJC Scopus subject areas

  • Information Systems
  • Software

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