An Approach for recommending TopK Digital Artworks

Jul 2016

25 Mon

26 Tue

27 Wed

28 Thu 08:30 AM – 06:25 PM IST

29 Fri 08:30 AM – 06:15 PM IST

30 Sat 08:45 AM – 05:00 PM IST

31 Sun 08:15 AM – 06:00 PM IST

Make a submission

NIMHANS Convention Centre

The Fifth Elephant is India’s most renowned data science conference. It is a space for discussing some of the most cutting edge developments in the fields of machine learning, data science and technology that powers data collection and analysis.

Machine Learning, Distributed and Parallel Computing, and High-performance Computing continue to be the themes for this year’s edition of Fifth Elephant.

We are now accepting submissions for our next edition which will take place in Bangalore 28-29 July 2016.

#Tracks

We are looking for application level and tool-centric talks and tutorials on the following topics:

Deep Learning
Text Mining
Computer Vision
Social Network Analysis
Large-scale Machine Learning (ML)
Internet of Things (IoT)
Computational Biology
ML in healthcare
ML in education
ML in energy and ecology
ML in agriculrure
Analytics for emerging markets
ML in e-governance
ML in smart cities
ML in defense

The deadline for submitting proposals is 30th April 2016

Format

This year’s edition spans two days of hands-on workshops and conference. We are inviting proposals for:

Full-length 40 minute talks.
Crisp 15-minute talks.
Sponsored sessions, 15 minute duration (limited slots available; subject to editorial scrutiny and approval).
Hands-on Workshop sessions, 3 and 6 hour duration.

Selection process

Proposals will be filtered and shortlisted by an Editorial Panel. We urge you to add links to videos / slide decks when submitting proposals. This will help us understand your past speaking experience. Blurbs or blog posts covering the relevance of a particular problem statement and how it is tackled will help the Editorial Panel better judge your proposals.

We expect you to submit an outline of your proposed talk – either in the form of a mind map or a text document or draft slides within two weeks of submitting your proposal.

We will notify you about the status of your proposal within three weeks of submission.

Selected speakers must participate in one-two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.

There is only one speaker per session. Entry is free for selected speakers. As our budget is limited, we will prefer speakers from locations closer home, but will do our best to cover for anyone exceptional. HasGeek will provide a grant to cover part of your travel and accommodation in Bangalore. Grants are limited and made available to speakers delivering full sessions (40 minutes or longer).

Commitment to open source

HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), please consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.

Key dates and deadlines

Revised paper submission deadline: 17 June 2016
Confirmed talks announcement (in batches): 13 June 2016
Schedule announcement: 30 June 2016
Conference dates: 28-29 July 2016

##Venue
The Fifth Elephant will be held at the NIMHANS Convention Centre, Dairy Circle, Bangalore.

##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.

Hosted by

The Fifth Elephant

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

All submissions

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An Approach for recommending TopK Digital Artworks

Submitted May 2, 2016

Section: Crisp talk Technical level: Beginner

We have shown how recommender systems apply to the online digital artwork domain. The goal was to test the ability of recommender systems to aid artists in discovering artwork relevant to their likings. The users were from the online digital artwork sharing community, using the PENUP application. We have used information retrieval based metrics to measure the performance of a few key algorithms implemented in Apache Mahout. The approach shows how we can integrate trust based social information into the standard Nearest Neighbor (NN) and Matrix factorization (MF) algorithms for artworks. It also shows how such a system can be measured for quality of recommendations, allowing one to deploy a practical system on productiont.

Outline

Recommender systems enable online users to have a more personalized experience. To the user, the system appears to somehow understand and capture the user’s likes and dislikes. There are various explicit and implicit “signals” picked from the user’s online activities. With this information, relevant content is automatically chosen and customized for the user.
Digital art is an artistic work or practice that uses digital technology as an essential part of the creative or presentation process. Samsung S Pen with Note is a tool for creating digital arts. Samsungs PEN.UP is a social networking service for people who create digital art. It has a community of over 1.7 million users and has more than a million artworks.
Interests in subjects related to artwork vary greatly and are subjective. What are very interesting to some users may be of little to others. For e.g. some people are interested in gaming related themes while others are interested in nature related themes. As the number of artworks grew, discovery of artworks and presenting the relevant artworks to the users based on their interests became a challenge. Usual methods of classification based on categories and tags did not work very well as the tags are user contributed and users who want more coverage for their arts add many categories and tags.
This paper studies the attempts towards improving the performance of standard recommender algorithm on artworks by the incorporation of social information. When an artist follows another artist, it can be said with more confidence that the likes and dislikes are in agreement. Such top – k recommender systems using social information have been studied before, but our work applies them to the field of digital artworks.