3,919 research outputs found
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
Rule-Guided Compositional Representation Learning on Knowledge Graphs
Representation learning on a knowledge graph (KG) is to embed entities and
relations of a KG into low-dimensional continuous vector spaces. Early KG
embedding methods only pay attention to structured information encoded in
triples, which would cause limited performance due to the structure sparseness
of KGs. Some recent attempts consider paths information to expand the structure
of KGs but lack explainability in the process of obtaining the path
representations. In this paper, we propose a novel Rule and Path-based Joint
Embedding (RPJE) scheme, which takes full advantage of the explainability and
accuracy of logic rules, the generalization of KG embedding as well as the
supplementary semantic structure of paths. Specifically, logic rules of
different lengths (the number of relations in rule body) in the form of Horn
clauses are first mined from the KG and elaborately encoded for representation
learning. Then, the rules of length 2 are applied to compose paths accurately
while the rules of length 1 are explicitly employed to create semantic
associations among relations and constrain relation embeddings. Besides, the
confidence level of each rule is also considered in optimization to guarantee
the availability of applying the rule to representation learning. Extensive
experimental results illustrate that RPJE outperforms other state-of-the-art
baselines on KG completion task, which also demonstrate the superiority of
utilizing logic rules as well as paths for improving the accuracy and
explainability of representation learning.Comment: The full version of a paper accepted to AAAI 202
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