Semantic sensitive tensor factorization
AuthorNakatsuji, Makoto; Toda, Hiroyuki; Sawada, Hiroshi; Zheng, Jin Guang; Hendler, James A.
Full CitationNakatsuji, Makoto, Hiroyuki Toda, Hiroshi Sawada, Jin Guang Zheng, and James A. Hendler. "Semantic sensitive tensor factorization." Artificial Intelligence 230 (2016): 224-245.
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URIhttps://doi.org/10.1016/j.artint.2015.09.001; https://www.sciencedirect.com/science/article/pii/S000437021500137X; https://hdl.handle.net/20.500.13015/6418
AbstractThe ability to predict the activities of users is an important one for recommender systems and analyses of social media. User activities can be represented in terms of relationships involving three or more things (e.g. when a user tags items on a webpage or tweets about a location he or she visited). Such relationships can be represented as a tensor, and tensor factorization is becoming an increasingly important means for predicting users' possible activities. However, the prediction accuracy of factorization is poor for ambiguous and/or sparsely observed objects. Our solution, Semantic Sensitive Tensor Factorization (SSTF), incorporates the semantics expressed by an object vocabulary or taxonomy into the tensor factorization. SSTF first links objects to classes in the vocabulary (taxonomy) and resolves the ambiguities of objects that may have several meanings. Next, it lifts sparsely observed objects to their classes to create augmented tensors. Then, it factorizes the original tensor and augmented tensors simultaneously. Since it shares semantic knowledge during the factorization, it can resolve the sparsity problem. Furthermore, as a result of the natural use of semantic information in tensor factorization, SSTF can combine heterogeneous and unbalanced datasets from different Linked Open Data sources. We implemented SSTF in the Bayesian probabilistic tensor factorization framework. Experiments on publicly available large-scale datasets using vocabularies from linked open data and a taxonomy from WordNet show that SSTF has up to 12% higher accuracy in comparison with state-of-the-art tensor factorization methods.;
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