Strategies for Landing an Oracle DBA Job as a Fresher
1 content optimization-hug-2010-07-21
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2. What is Yahoo?…Listen Yahoo is a great company that is very, very strong in content for its users, uses amazing. …. . For instance, on our today module in the front page, every 5 minutes we have 32,000 different variations of that module. So you don’t even know what I’m seeing in fact we serve a million different front page modules a day and that’s just through content optimization. And that’s just the beginning…Customized because we know the things you’re interested in ….
3. What do we do ? “ Deliver right CONTENT to the right USER at the right TIME ” Keep Carol Bartz excited
4. Relevance at Yahoo! Important Editors Popular Personal / Social People 10s of Items Science Millions of Items
5. Ranking Problems Most Popular Most engaging overall based on objective metrics Related Items Behavioral Affinity: People who did X, did Y Deep Personalization Most relevant to me based on my deep interests Real-time Dashboard Business Optimization Light Personalization More relevant to me based on my age, gender and property usage Most Popular + Per User History Engaging overall, and aware of what I’ve already seen X Y
7. How it happens ? At time ‘ t ’ User ‘ u ’ (user attr : age, gen, loc ) interacted with Content ‘ id ’ at Position ‘ o ’ Property/ S ite ‘ p ’ Section - s Module – m International - i ’ User Events Item Metadata Modeling ITEM Model USER Model Content ‘ id ’ Has associated metadata ‘ meta ’ m eta = {entity, keyword, geo, topic, category} Feature Generation Additional Content & User Feature Generation STORE: PNUTS 5 min latency Ranking B-Rules Request 5 – 30 min latency SLA 50 ms – 200 ms Item BASE M F ATTR CAT_Sports id 1 0.8 +1.2 -1.5 -0.9 1.0 id 2 -0.9 -0.9 +2.6 +0.3 1.0 Item BASE M F ATTR CAT_Sports u 1 0.8 1 1 0.2 u 2 -0.9 1 -1.2
This is the Title slide. Please use the name of the presentation that was used in the abstract submission.
Instant My! add from D White
Instant My! add from D White
Instant My! add from D White
This is the agenda slide. There is only one of these in the deck. NOTES: What does X stories to run mean ? Can we be more clear on that Also – This should be a more a punch line of what we do. This slide to me is very broad and not clear. Following are the things that I would describe Problem of matching the best content to the interest of a user Scale Millions of content slices Millions of users
This is the agenda slide. There is only one of these in the deck. NOTES: What does X stories to run mean ? Can we be more clear on that Also – This should be a more a punch line of what we do. This slide to me is very broad and not clear. Following are the things that I would describe Problem of matching the best content to the interest of a user Scale Millions of content slices Millions of users
This is the agenda slide. There is only one of these in the deck. NOTES: What does X stories to run mean ? Can we be more clear on that Also – This should be a more a punch line of what we do. This slide to me is very broad and not clear. Following are the things that I would describe Problem of matching the best content to the interest of a user Scale Millions of content slices Millions of users
This is the agenda slide. There is only one of these in the deck. NOTES: What does X stories to run mean ? Can we be more clear on that Also – This should be a more a punch line of what we do. This slide to me is very broad and not clear. Following are the things that I would describe Problem of matching the best content to the interest of a user Scale Millions of content slices Millions of users
This is the agenda slide. There is only one of these in the deck. NOTES: What does X stories to run mean ? Can we be more clear on that Also – This should be a more a punch line of what we do. This slide to me is very broad and not clear. Following are the things that I would describe Problem of matching the best content to the interest of a user Scale Millions of content slices Millions of users
This is the agenda slide. There is only one of these in the deck. NOTES: What does X stories to run mean ? Can we be more clear on that Also – This should be a more a punch line of what we do. This slide to me is very broad and not clear. Following are the things that I would describe Problem of matching the best content to the interest of a user Scale Millions of content slices Millions of users
This is the agenda slide. There is only one of these in the deck. NOTES: What does X stories to run mean ? Can we be more clear on that Also – This should be a more a punch line of what we do. This slide to me is very broad and not clear. Following are the things that I would describe Problem of matching the best content to the interest of a user Scale Millions of content slices Millions of users
This is the agenda slide. There is only one of these in the deck. NOTES: What does X stories to run mean ? Can we be more clear on that Also – This should be a more a punch line of what we do. This slide to me is very broad and not clear. Following are the things that I would describe Problem of matching the best content to the interest of a user Scale Millions of content slices Millions of users
This is the final slide; generally for questions at the end of the talk. Please post your contact information here.
![Online Content Optimization using Hadoop ,[object Object],[object Object]](https://image.slidesharecdn.com/1content-optimization-hug-2010-07-21-100722173047-phpapp02/85/1-content-optimization-hug-2010-07-21-1-320.jpg)
