31,517 research outputs found
Complex Embeddings for Simple Link Prediction
In statistical relational learning, the link prediction problem is key to
automatically understand the structure of large knowledge bases. As in previous
studies, we propose to solve this problem through latent factorization.
However, here we make use of complex valued embeddings. The composition of
complex embeddings can handle a large variety of binary relations, among them
symmetric and antisymmetric relations. Compared to state-of-the-art models such
as Neural Tensor Network and Holographic Embeddings, our approach based on
complex embeddings is arguably simpler, as it only uses the Hermitian dot
product, the complex counterpart of the standard dot product between real
vectors. Our approach is scalable to large datasets as it remains linear in
both space and time, while consistently outperforming alternative approaches on
standard link prediction benchmarks.Comment: 10+2 pages, accepted at ICML 201
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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