##About the 2019 edition:
The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule
The conference has three tracks:
- Talks in the main conference hall track
- Poster sessions featuring novel ideas and projects in the poster session track
- Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:
- Myths and realities of labelling datasets for Deep Learning.
- Practical experience with using Knowledge Graphs for different use cases.
- Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
- Pros and cons of using custom and open source tooling for AI/DL/ML.
#Who should attend Anthill Inside:
Anthill Inside is a platform for:
- Data scientists
- AI, DL and ML engineers
- Cloud providers
- Companies which make tooling for AI, ML and Deep Learning
- Companies working with NLP and Computer Vision who want to share their work and learnings with the community
For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to sales@hasgeek.com
#Sponsors:
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Anthill Inside 2019 sponsors:
#Bronze Sponsor
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#Community Sponsor
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Hosted by
Anthill Inside is a forum for conversations about risk mitigation and governance in Artificial Intelligence and Deep Learning. AI developers, researchers, startup founders, ethicists, and AI enthusiasts are encouraged to: more
GS
Gunjan Sharma
@gunjan_sharma
Non-Intent User Similarity for recommendation systems
Submitted Apr 25, 2019
Section:
Full talk
Technical level:
Intermediate
In the world of Ad Business’s recommendation systems it is easier comparatively to recommend to user who have shown some intent. But what about the users who have not shown any intent? How do you target them? In this talk I will like to talk about a novel approach to use user similarity from supply data to work out significant recommendation for these users
Outline
Define the problem
Define non efficient solutions
Introduce the novel user similarity approach
Connect it to the original problem to show how it helps
Results
Future work
Speaker bio
Gunjan Sharma
Architect InMobi
Comments
Hosted by
Anthill Inside is a forum for conversations about risk mitigation and governance in Artificial Intelligence and Deep Learning. AI developers, researchers, startup founders, ethicists, and AI enthusiasts are encouraged to: more
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