Recommendation System beyond traditional Collaborative filtering

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

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.

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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

Recommendation System beyond traditional Collaborative filtering

Submitted Jun 12, 2015

Section: Full Talk Technical level: Intermediate

I would be sharing my thoughts and experiences at Snapdeal in building more personalized and relevant recommendation system for e-commerce industry by presenting mathematical, technological, machine learning and various other aspects related to it.

Outline

Though Collaborative filtering works quite well for companies like NetFlix but here in Snapdeal we are catering 12M huge product catalog and more than 100 categories which again comprised of 20-30 subcategories each. For us only Collaborative filtering doesn’t work well, because of the wide catalog and implicit feedback capturing instead of explicit and hence we developed a recommendation system which considers various other factors beyond collabarative Filtering.

In this session I would be discussing other factors (mentioned below) and their mathematical models that we have considered while building custom recommendation system for generating more personalized and relevant recommendations.

User Category Affinity (to some more granular level)
Content based product similarity
product which goes well with already bought products.
predicting the repurchase of already purchased products.
Suggesting trending products based on user’s affinity.
Capturing user’s feedback (implicit) to our served recommendations and use to improve relevancy.
Collaborative filtering (we have also used this but with some weight-age)

Finally I would be concluding session with technical challenges in building scalable recommendation system with massive datasets and serving these recommendations in realtime.

Speaker bio

Gagan Deep Juneja is a Lead Engineer at Snapdeal and is leading several initiatives related to user personalization. He has close to 7 years of experience in the Software Industry. He has worked on several projects using Java/J2ee and Hadoop as the primary technology. He has been working with Big data technologies such as hadoop, spark, cascading, pig, hive, blur for the past couple of years. He has great interest in Machine learning concepts and working with supervised and unsupervised algorithms to get value out of data. He has an inclination to open source technologies and likes to delve into new frameworks. He is a committer and PPMC member to Apache Blur (incubating). He has spoken at various meetup groups in the past. He is a active blogger, and in his leisure time loves exploring new technologies and keeping himself updated with latest trends.