The Journal of Machine Learning Research
Bilingual topic aspect classification with a few training examples
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
Personalised and dynamic trust in social networks
Proceedings of the third ACM conference on Recommender systems
Latent Topic Extraction from Relational Table for Record Matching
DS '09 Proceedings of the 12th International Conference on Discovery Science
Cross-language information retrieval with latent topic models trained on a comparable corpus
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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Multi-lingual text processing is important for content-based and hybrid recommender systems. It helps recommender systems extract content information from broader sources. It also enables systems to recommend items in a user's native language. We propose a cross-lingual keyword recommendation method, which is built on an extended latent Dirichlet allocation model, for extracting latent features from parallel corpora. With this model, the proposed method can recommend keywords from text written in different languages. We evaluate the proposed method using a cross-lingual bibliographic database that contains both English and Japanese abstracts and keywords and show that the proposed method can recommend keywords from abstracts in a cross-lingual environment with almost the same accuracy as in a monolingual environment.