Semantic Data Representation for Improving Tensor Factorization


  • Makoto Nakatsuji NTT Corporation
  • Yasuhiro Fujiwara NTT Corporation
  • Hiroyuki Toda NTT Corporation
  • Hiroshi Sawada NTT Corporation
  • Jin Zheng Rensselaer Polytechnic Institute
  • James Hendler Rensselaer Polytechnic Institute



Collaborative Filtering, Recommender Systems, Semantic Web, Machine Learning, Tensor Factorization, Linked Open Data, Link Prediction


Predicting human activities is important for improving recommender systems or analyzing social relationships among users. Those human activities are usually repre- sented as multi-object relationships (e.g. user’s tagging activities for items or user’s tweeting activities at some locations). Since multi-object relationships are naturally represented as a tensor, tensor factorization is becom- ing more important for predicting users’ possible ac- tivities. However, its prediction accuracy is weak for ambiguous and/or sparsely observed objects. Our so- lution, Semantic data Representation for Tensor Fac- torization (SRTF), tackles these problems by incorpo- rating semantics into tensor factorization based on the following ideas: (1) It first links objects to vocabu- laries/taxonomies and resolves the ambiguity caused by objects that can be used for multiple purposes. (2) It next links objects to composite classes that merge classes in different kinds of vocabularies/taxonomies (e.g. classes in vocabularies for movie genres and those for directors) to avoid low prediction accuracy caused by rough-grained semantics. (3) It then lifts sparsely observed objects into their classes to solve the sparsity problem for rarely observed objects. To the best of our knowledge, this is the first study that leverages seman- tics to inject expert knowledge into tensor factorization. Experiments show that SRTF achieves up to 10% higher accuracy than state-of-the-art methods.




How to Cite

Nakatsuji, M., Fujiwara, Y., Toda, H., Sawada, H., Zheng, J., & Hendler, J. (2014). Semantic Data Representation for Improving Tensor Factorization. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Main Track: Novel Machine Learning Algorithms