##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 email@example.com
Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.
Using Locations for Online-Behaviour Prediction with Sparse Data
User behaviour varies with their current location, as a consequence their engagement with online media (say Ads) varies with where they are; Knowing the type of location can help us target the user better and recommend better.
After the advent of GPS technology, it is easy and inexpensive to get location information from devices, but the data is often sparse for an ad network as they only get a partial view of user data. We present a novel algorithm to classify locations using the GPS data along with anonymous ad-request data such that it performs optimally with sparse GPS signals with no supervised data at all.
Our results show that the algorithm has a better prediction accuracy compared to existing methods and correlates well with the user behaviours.
- Introduction / Context Setting
- Why the need to classify the locations
- Location as the key attribute for recommendations
- Data exploration and modeling.
- Deploying the model using Spark
- How users interact differently at different locations
- Visualization of results for Bangalore and Bay Area.
- Results and take home message
Nishant Oli is a Data Scientist at InMobi where he works on user inferences and geo analytics. He graduated with a Master’s degree from IIIT Bangalore. Previously he worked at Siemens Corporate Research & Mazumdar Shaw Center of Translational Research in the field of Meta-Learning for computer vision and biomedical computer vision.