Determining whether a pair of garments are compatible with each other is a challenging matching problem. Past works explored various embedding methods for learning such a relationship. This paper introduces using discriminative methods to learn compatibility, by formulating the task as a simple binary classification problem. We evaluate our approach using an established dataset of outfits created by non-experts and demonstrated an improvement of 2.5 on established metrics over the state-of-the-art method. We introduce three new datasets of professionally curated outfits and show the consistent performance of our approach on expert-curated datasets. To facilitate comparing across outfit datasets, we propose a new metric which, unlike previously used metrics, is not biased by the average size of outfits. We also demonstrate that compatibility between two types of items can be query indirectly, and such query strategy yield improvements.
The established approach for learning representations of complex relationships involves constructing an embedding space by training as a siamese structure @cite_3 or using triplet loss @cite_14 with samples of positive and negative pairs. Embedding methods are also commonly used to capture hard-to-define relationships in the fashion domain such as style, fashionability, and the matching between clothing items @cite_0 @cite_26 @cite_22 @cite_24 @cite_25 @cite_12 . @cite_13 trained a bidirectional LSTM model to predict the next compatible clothing item within an outfit, always regarding an outfit as a whole. Noticed the non-transitive nature of the compatibility relationship, @cite_9 recently demonstrated that enforcing type-awareness in the embeddings produce better performance over prior works.
@cite_14 @cite_26 @cite_22 @cite_9 @cite_3 @cite_0 @cite_24 @cite_13 @cite_25 @cite_12
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We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (, 2013) and TransE (, 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2 vs. 54.7 by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as "BornInCity(a,b) and CityInCountry(b,c) => Nationality(a,c)". We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-of-the-art confidence-based rule mining approach in mining Horn rules that involve compositional reasoning. Abstract: We consider learning representations of entities and relations in KBs using the neural-embedding approach. We show that most existing models, including NTN (, 2013) and TransE (, 2013b), can be generalized under a unified learning framework, where entities are low-dimensional vectors learned from a neural network and relations are bilinear and or linear mapping functions. Under this framework, we compare a variety of embedding models on the link prediction task. We show that a simple bilinear formulation achieves new state-of-the-art results for the task (achieving a top-10 accuracy of 73.2 vs. 54.7 by TransE on Freebase). Furthermore, we introduce a novel approach that utilizes the learned relation embeddings to mine logical rules such as "BornInCity(a,b) and CityInCountry(b,c) => Nationality(a,c)". We find that embeddings learned from the bilinear objective are particularly good at capturing relational semantics and that the composition of relations is characterized by matrix multiplication. More interestingly, we demonstrate that our embedding-based rule extraction approach successfully outperforms a state-of-the-art confidence-based rule mining approach in mining Horn rules that involve compositional reasoning. This paper proposes a novel translation-based knowledge graph embedding that preserves the logical properties of relations such as transitivity and symmetricity. The embedding space generated by existing translation-based embeddings do not represent transitive and symmetric relations precisely, because they ignore the role of entities in triples. Thus, we introduce a role-specific projection which maps an entity to distinct vectors according to its role in a triple. That is, a head entity is projected onto an embedding space by a head projection operator, and a tail entity is projected by a tail projection operator. This idea is applied to TransE, TransR, and TransD to produce lppTransE, lppTransR, and lppTransD, respectively. According to the experimental results on link prediction and triple classification, the proposed logical property preserving embeddings show the state-of-the-art performance at both tasks. These results prove that it is critical to preserve logical properties of relations while embedding knowledge graphs, and the proposed method does it effectively. Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs. In this work, we propose holographic embeddings (HolE) to learn compositional vector space representations of entire knowledge graphs. The proposed method is related to holographic models of associative memory in that it employs circular correlation to create compositional representations. By using correlation as the compositional operator HolE can capture rich interactions but simultaneously remains efficient to compute, easy to train, and scalable to very large datasets. In extensive experiments we show that holographic embeddings are able to outperform state-of-the-art methods for link prediction in knowledge graphs and relational learning benchmark datasets.