Jul 2018
23 Mon
24 Tue
25 Wed
26 Thu 07:45 AM – 06:15 PM IST
27 Fri 07:45 AM – 05:35 PM IST
28 Sat
29 Sun
Jul 2018
23 Mon
24 Tue
25 Wed
26 Thu 07:45 AM – 06:15 PM IST
27 Fri 07:45 AM – 05:35 PM IST
28 Sat
29 Sun
##About the conference and topics for submitting talks:
The Fifth Elephant is rated as India’s best data conference. It is a conference for practitioners, by practitioners. In 2018, The Fifth Elephant will complete its seventh edition.
The Fifth Elephant is an evolving community of stakeholders invested in data in India. Our goal is to strengthen and grow this community by presenting talks, panels and Off The Record (OTR) sessions that present real insights about:
**
##Target audience:
You should attend and speak at The Fifth Elephant if your work involves:
##Perks for submitting proposals:
Submitting a proposal, especially with our process, is hard work. We appreciate your effort.
We offer one conference ticket at discounted price to each proposer, and a t-shirt.
We only accept one speaker per talk. This is non-negotiable. Workshops may have more than one instructor.
In case of proposals where more than one person has been mentioned as collaborator, we offer the discounted ticket and t-shirt only to the person with who the editorial team corresponded directly during the evaluation process.
##Format:
The Fifth Elephant is a two-day conference with two tracks on each day. Track details will be announced with a draft schedule in February 2018.
We are accepting sessions with the following formats:
##Selection criteria:
The first filter for a proposal is whether the technology or solution you are referring to is open source or not. The following criteria apply for closed source talks:
The criteria for selecting proposals, in the order of importance, are:
No one submits the perfect proposal in the first instance. We therefore encourage you to:
Our editorial team helps potential speakers in honing their speaking skills, fine tuning and rehearsing content at least twice - before the main conference - and sharpening the focus of talks.
##How to submit a proposal (and increase your chances of getting selected):
The following guidelines will help you in submitting a proposal:
To summarize, we do not accept talks that gloss over details or try to deliver high-level knowledge without covering depth. Talks have to be backed with real insights and experiences for the content to be useful to participants.
##Passes and honorarium for speakers:
We pay an honorarium of Rs. 3,000 to each speaker and workshop instructor at the end of their talk/workshop. Confirmed speakers and instructors also get a pass to the conference and networking dinner. We do not provide free passes for speakers’ colleagues and spouses.
##Travel grants for outstation speakers:
Travel grants are available for international and domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans. If you require a grant, request it when you submit your proposal in the field where you add your location. The Fifth Elephant is funded through ticket purchases and sponsorships; travel grant budgets vary.
##Last date for submitting proposals is: 31 March 2018.
You must submit the following details along with your proposal, or within 10 days of submission:
##Contact details:
For more information about the conference, sponsorships, or any other information contact support@hasgeek.com or call 7676332020.
Hosted by
Surabhi Punjabi
Building recommender systems for the task of computational advertising for Walmart.com has been an extraordinary journey. Particularly fascinating is the aspect of designing algorithms that cater to audiences who are at different stages of their purchase journey, or who might not have interacted with the site recently. This coupled with the scalability challenges and the interplay of factors like recency, seasonality, product pricing, trending items makes it very interesting, both from data science and core systems perspective. There is a sharp decline in clickthrough rates for successive ad slots. Hence, achieving high precision is imperative for driving performance for the ad campaigns.
In this talk, we focus on how our recommendation systems have evolved over time and the key lessons we learnt along the way. We share our insights on the relative performance and suitability of the collaborative filtering, graph based recommenders across the retargeting and prospecting efforts. We describe the challenges faced while ingesting item-signals along with user-item affinity in our Spark steaming pipelines and how we optimized those to meet the latency constraints. We also elaborate on the inherent counterfactual nature of recommendations which makes it pivotal to build robust offline evaluation systems and carefully design the A/B experiments. We summarize the observations in the experiments performed on algorithms incentivizing ad coherence v/s diversity and emphasize the role of online evaluation.
Recommender Systems for Computational Advertising
(i) Motivation
(ii) eCommerce : Diverse audience profiles - Browsers, Cart Abandoners, Dormant users.
Power of Associations
(i) Viewed Also Viewed / Bought Also Bought
(ii) Spark Streaming pipeline architecture.
(iii) Bulk purchase skew.
(iv) Cold start problem.
Graph based recommendations
(i) Random walk model.
(ii) Promoting diversity via reinforced random walks.
(iii) Spark GraphX Pregel API: Flexibility, Challenges.
Key Lessons : Data Science
(i) Impact of Item Attributes.
(ii) Role of context : Seasonality, Upcoming Trends.
(iii) CTR decline with ad slots : Importance of the first few!
(iv) Coherence v/s Diversity : Case Study.
(v) Fallbacks in case of few relevant items : Category affinity.
(vi) Fatigue : Refresh Important!
Key Lessons : Systems
(i) Model Complexity, Additional features => Latency!
(ii) Optimizations : Caching, Reduce shuffles.
(iii) Seamless A/B experiments : Dealing with multiple models.
Role of evaluation
(i) Metrics : MAP/NDCG/CTR
(ii) Offline
- Counterfactual - How to reduce bias?
(iii) Online
- Experiment design.
- Optimal time for running experiments.
- Does the effect of a new change persist over time? Case Study.
Recent experiments with Deep learning
Member of data science team at @WalmartLabs. 4 years experience in tackling diverse large scale machine learning problems in computational advertising domain, with user-level recommendations, bidding and budget optimization being key focus areas. Masters graduate from IISc Bangalore with specialization in Data Mining and Pattern Recognition.
https://www.linkedin.com/in/surabhi-punjabi-851b8493/
https://drive.google.com/open?id=1Xu2MT1uEbBLs-R2ghOoQSCC2sYS6jarW
Jul 2018
23 Mon
24 Tue
25 Wed
26 Thu 07:45 AM – 06:15 PM IST
27 Fri 07:45 AM – 05:35 PM IST
28 Sat
29 Sun
Hosted by
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