Building Recommender system

Jul 2015

13 Mon

14 Tue

15 Wed

16 Thu 08:30 AM – 06:35 PM IST

17 Fri 08:30 AM – 06:30 PM IST

18 Sat 09:00 AM – 06:30 PM IST

19 Sun

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NIMHANS Convention center

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

The deadline for submitting a proposal is 15th June 2015

We are looking for talks and workshops from academics and practitioners who are in the business of making sense of data, big and small.

Track 1: Discovering Insights and Driving Decisions #

This track is about general, novel, fundamental, and advanced techniques for making sense of data and driving decisions from data. This could encompass applications of the following ML paradigms:

Statistical Visualizations
Unsupervised Learning
Supervised Learning
Semi-Supervised Learning
Active Learning
Reinforcement Learning
Monte-carlo techniques and probabilistic programming
Deep Learning

Across various data modalities including multi-variate, text, speech, time series, images, video, transactions, etc.

Track 2: Speed at Scale #

This track is about tools and processes for collecting, indexing, and processing vast amounts of data. The theme includes:

Distributed and Parallel Computing
Real Time Analytics and Stream Processing
MapReduce and Graph Computing frameworks
Kafka, Spark, Hadoop, MPI
Stories of parallelizing sequential programs
Cost/Security/Disaster Management of Data

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 license. If your software is commercially licensed or available under a combination of commercial and restrictive open source licenses (such as the various forms of the GPL), please consider picking up a sponsorship. We recognize 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.

Workshops #

If you are interested in conducting a hands-on session on any of the topics falling under the themes of the two tracks described above, please submit a proposal under the workshops section. We also need you to tell us about your past experience in teaching and/or conducting workshops.

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

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This submission has been added to the schedule

Submitted May 21, 2015

Section: Crisp Talk Technical level: Beginner

Will talk about classical and state-of-the-art recommender systems. The audience will also get a flavour of the mathematical computations that go into recommender systems.

Outline #

Recommender Systems solves matrix sparsity problem. And this idea of predicting sparse values can be applied for various problems across domains. I have used recommender systems to identify audience clusters for a conference, recommending new jokes to users based on the past jokes they liked, and few kaggle problems.

One of the key events that energized research in recommender systems was the Netflix prize. Netflix sponsored a competition, that could take an offered dataset of over 100 million movie ratings and return recommendations that were 10% more accurate than those offered by the company’s existing recommender system.

Recommender systems typically produce a list of recommendations in one of the two ways - through collaborative or content-based filtering. Would like to cover both of them with the implementation and mathematics involved.