67,164 research outputs found
Learning and Transferring IDs Representation in E-commerce
Many machine intelligence techniques are developed in E-commerce and one of
the most essential components is the representation of IDs, including user ID,
item ID, product ID, store ID, brand ID, category ID etc. The classical
encoding based methods (like one-hot encoding) are inefficient in that it
suffers sparsity problems due to its high dimension, and it cannot reflect the
relationships among IDs, either homogeneous or heterogeneous ones. In this
paper, we propose an embedding based framework to learn and transfer the
representation of IDs. As the implicit feedbacks of users, a tremendous amount
of item ID sequences can be easily collected from the interactive sessions. By
jointly using these informative sequences and the structural connections among
IDs, all types of IDs can be embedded into one low-dimensional semantic space.
Subsequently, the learned representations are utilized and transferred in four
scenarios: (i) measuring the similarity between items, (ii) transferring from
seen items to unseen items, (iii) transferring across different domains, (iv)
transferring across different tasks. We deploy and evaluate the proposed
approach in Hema App and the results validate its effectiveness.Comment: KDD'18, 9 page
Learning Domain-Specific Word Embeddings from Sparse Cybersecurity Texts
Word embedding is a Natural Language Processing (NLP) technique that
automatically maps words from a vocabulary to vectors of real numbers in an
embedding space. It has been widely used in recent years to boost the
performance of a vari-ety of NLP tasks such as Named Entity Recognition,
Syntac-tic Parsing and Sentiment Analysis. Classic word embedding methods such
as Word2Vec and GloVe work well when they are given a large text corpus. When
the input texts are sparse as in many specialized domains (e.g.,
cybersecurity), these methods often fail to produce high-quality vectors. In
this pa-per, we describe a novel method to train domain-specificword embeddings
from sparse texts. In addition to domain texts, our method also leverages
diverse types of domain knowledge such as domain vocabulary and semantic
relations. Specifi-cally, we first propose a general framework to encode
diverse types of domain knowledge as text annotations. Then we de-velop a novel
Word Annotation Embedding (WAE) algorithm to incorporate diverse types of text
annotations in word em-bedding. We have evaluated our method on two
cybersecurity text corpora: a malware description corpus and a Common
Vulnerability and Exposure (CVE) corpus. Our evaluation re-sults have
demonstrated the effectiveness of our method in learning domain-specific word
embeddings
Deep recommender engine based on efficient product embeddings neural pipeline
Predictive analytics systems are currently one of the most important areas of
research and development within the Artificial Intelligence domain and
particularly in Machine Learning. One of the "holy grails" of predictive
analytics is the research and development of the "perfect" recommendation
system. In our paper, we propose an advanced pipeline model for the multi-task
objective of determining product complementarity, similarity and sales
prediction using deep neural models applied to big-data sequential transaction
systems. Our highly parallelized hybrid model pipeline consists of both
unsupervised and supervised models, used for the objectives of generating
semantic product embeddings and predicting sales, respectively. Our
experimentation and benchmarking processes have been done using pharma industry
retail real-life transactional Big-Data streams.Comment: 2018 17th RoEduNet Conference: Networking in Education and Research
(RoEduNet
Transitioning Applications to Semantic Web Services: An Automated Formal Approach
Semantic Web Services have been recognized as a promising technology that exhibits huge commercial potential, and attract significant attention from both industry and the research community. Despite expectations being high, the industrial take-up of Semantic Web Service technologies has been slower than expected. One of the main reasons is that many systems have been developed without considering the potential of the web in integrating services and sharing resources. Without a systematic methodology and proper tool support, the migration from legacy systems to Semantic Web Service-based systems can be a very tedious and expensive process, which carries a definite risk of failure. There is an urgent need to provide strategies which allow the migration of legacy systems to Semantic Web Services platforms, and also tools to support such a strategy. In this paper we propose a methodology for transitioning these applications to Semantic Web Services by taking the advantage of rigorous mathematical methods. Our methodology allows users to migrate their applications to Semantic Web Services platform automatically or semi-automatically
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