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Recently, to alleviate the data sparsity and cold start problem, many research efforts have been devoted to the usage of knowledge graph (KG) in recommender systems. It is common for most existing KG based models to represent users and items using real-valued embeddings. However, compared with complex or hypercomplex numbers, these real-valued vectors are of less representation capacity and no intrinsic asymmetrical properties, thus may limit the modeling of interactions between entities and relations in KG. In this paper, we propose Quaternion-based Knowledge Graph Network (QKGN) for recommendation, which represents users and items with quaternion embeddings in hypercomplex space, so that the latent inter-dependencies between entities and relations could be captured effectively. In the core of our model, a semantic matching principle based on Hamilton product is applied to learn expressive quaternion representations from the unified user-item KG. On top of this, those embeddings are attentively updated by a customized preference propagation mechanism with structure information concerned. Finally, we apply the proposed QKGN to three real-world datasets of music, movie and book, and experimental results show the validity of our method.

Nowadays, we have witnessed the early progress on learning the association between voice and face automatically, which brings a new wave of studies to the computer vision community. However, most of the prior arts along this line (a) merely adopt local information to perform modality alignment and (b) ignore the diversity of learning difficulty across different subjects. In this paper, we propose a novel framework to jointly address the above-mentioned issues. Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered. Compared with the existing methods, we introduce a global loss into the modality alignment process. The global component of the loss is driven by the identity classification. Theoretically, we show that minimizing the loss could maximize the distance between embeddings across different identities while minimizing the distance between embeddings belonging to the same identity, in a global sense (instead of a mini-batch). Targeting at (b), we propose a dynamic reweighting scheme to better explore the hard but valuable identities while filtering out the unlearnable identities. Experiments show that the proposed method outperforms the previous methods in multiple settings, including voice-face matching, verification and retrieval.