28,639 research outputs found
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
Outfit Recommender System
The online apparel retail market size in the United States is worth about seventy-two billion US dollars. Recommendation systems on retail websites generate a lot of this revenue. Thus, improving recommendation systems can increase their revenue. Traditional recommendations for clothes consisted of lexical methods. However, visual-based recommendations have gained popularity over the past few years. This involves processing a multitude of images using different image processing techniques. In order to handle such a vast quantity of images, deep neural networks have been used extensively. With the help of fast Graphics Processing Units, these networks provide results which are extremely accurate, within a small amount of time. However, there are still ways in which recommendations for clothes can be improved. We propose an event-based clothing recommendation system which uses object detection. We train a model to identify nine events/scenarios that a user might attend: White Wedding, Indian Wedding, Conference, Funeral, Red Carpet, Pool Party, Birthday, Graduation and Workout. We train another model to detect clothes out of fifty-three categories of clothes worn at the event. Object detection gives a mAP of 84.01. Nearest neighbors of the clothes detected are recommended to the user
Algorithm Selection Framework for Cyber Attack Detection
The number of cyber threats against both wired and wireless computer systems
and other components of the Internet of Things continues to increase annually.
In this work, an algorithm selection framework is employed on the NSL-KDD data
set and a novel paradigm of machine learning taxonomy is presented. The
framework uses a combination of user input and meta-features to select the best
algorithm to detect cyber attacks on a network. Performance is compared between
a rule-of-thumb strategy and a meta-learning strategy. The framework removes
the conjecture of the common trial-and-error algorithm selection method. The
framework recommends five algorithms from the taxonomy. Both strategies
recommend a high-performing algorithm, though not the best performing. The work
demonstrates the close connectedness between algorithm selection and the
taxonomy for which it is premised.Comment: 6 pages, 7 figures, 1 table, accepted to WiseML '2
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they
are ubiquitous in today's online environment, and effective at finding items
that meet user needs and tastes. As the reach of recommender systems has
extended, it has become apparent that the single-minded focus on the user
common to academic research has obscured other important aspects of
recommendation outcomes. Properties such as fairness, balance, profitability,
and reciprocity are not captured by typical metrics for recommender system
evaluation. The concept of multistakeholder recommendation has emerged as a
unifying framework for describing and understanding recommendation settings
where the end user is not the sole focus. This article describes the origins of
multistakeholder recommendation, and the landscape of system designs. It
provides illustrative examples of current research, as well as outlining open
questions and research directions for the field.Comment: 64 page
The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey
Graph processing is becoming increasingly prevalent across many application
domains. In spite of this prevalence, there is little research about how graphs
are actually used in practice. We performed an extensive study that consisted
of an online survey of 89 users, a review of the mailing lists, source
repositories, and whitepapers of a large suite of graph software products, and
in-person interviews with 6 users and 2 developers of these products. Our
online survey aimed at understanding: (i) the types of graphs users have; (ii)
the graph computations users run; (iii) the types of graph software users use;
and (iv) the major challenges users face when processing their graphs. We
describe the participants' responses to our questions highlighting common
patterns and challenges. Based on our interviews and survey of the rest of our
sources, we were able to answer some new questions that were raised by
participants' responses to our online survey and understand the specific
applications that use graph data and software. Our study revealed surprising
facts about graph processing in practice. In particular, real-world graphs
represent a very diverse range of entities and are often very large,
scalability and visualization are undeniably the most pressing challenges faced
by participants, and data integration, recommendations, and fraud detection are
very popular applications supported by existing graph software. We hope these
findings can guide future research
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