Hui Li


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LRMM: Learning to Recommend with Missing Modalities
Cheng Wang | Mathias Niepert | Hui Li
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Multimodal learning has shown promising performance in content-based recommendation due to the auxiliary user and item information of multiple modalities such as text and images. However, the problem of incomplete and missing modality is rarely explored and most existing methods fail in learning a recommendation model with missing or corrupted modalities. In this paper, we propose LRMM, a novel framework that mitigates not only the problem of missing modalities but also more generally the cold-start problem of recommender systems. We propose modality dropout (m-drop) and a multimodal sequential autoencoder (m-auto) to learn multimodal representations for complementing and imputing missing modalities. Extensive experiments on real-world Amazon data show that LRMM achieves state-of-the-art performance on rating prediction tasks. More importantly, LRMM is more robust to previous methods in alleviating data-sparsity and the cold-start problem.


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Chinese Temporal Tagging with HeidelTime
Hui Li | Jannik Strötgen | Julian Zell | Michael Gertz
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers


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Incorporating Bigram Constraints into an LR Table
Hiroki Imai | Hui Li | Hozumi Tanaka
Proceedings of the 10th Research on Computational Linguistics International Conference