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Title: Computational creativity based video recommendation
Authors: Lu, W 
Chung, FL 
Keywords: Bayesian tensor factorization
Issue Date: 2016
Publisher: Association for Computing Machinary
Source: SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2016, p. 793-796 How to cite?
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.
ISBN: 9781450342902
DOI: 10.1145/2911451.2914707
Appears in Collections:Conference Paper

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