Maria Grineva Senior Scientist @ Yandex CTO Office

Social Media Projects at Yandex Maria Grineva Senior Scientist @ Yandex CTO Office Monday, November 12, 12

Outline

1. Experience of building TweetedTimes - personalized

news recommendation based on Twitter 2. Search Beyond the Web: Data From Social Networks

and Native Applications

2

Monday, November 12, 12

TweetedTimes news recommendation based on Twitter social graph

3


Playing with Social Network APIs • Twitter opened it’s API in 2007 - opened many new opportunities for research and building new products • API calls for example: – get all tweets from the user’s friend – get all tweets from the given user – the user can “login with Twitter id” in your application and application can act on behalf of the user – limited 150 calls/hour on behalf of the user => have to be creative

4


TweetSieve: Bursts of a Keyword on Twitter

5


Reading news on Twitter

• Noticed that people mostly share news, also a lot of people use Twitter to receive highly relevant news • Twitter became news medium: – Twitter provided smart instrumentation for managing your social channels: follow/unfollow. – Retweet - for fast spread of news • Build a tool to help reading news on Twitter!

6


TweetedTimes - personalized newspaper

• Уровень один – Уровень два

7


TweetedTimes - personalized newspaper

• Уровень один – Уровень два

8


TweetedTimes: on the backend

–

Interesting observation: 500 users are enough to have 2000 most popular friends of friends among friends

9


resolving all shortened URLs from tweets

Group by URL

Group by URL

Group by URL

Group by URL

Rank URLs

Rank URLs

Rank URLs

Rank URLs

Mine news content from RSS




Search over Data from Social Networks and Native Apps

10


Data from Applications flow into Social Networks

11


Applications share their custom actions on Facebook • Application registers its verb and its object on Facebook • Then it posts on behalf of the users: Maria took a photo with Instagram

12


New World of Data which is not the Web – –

The size of data generated by native apps is now comparable to the size of the Web! As of September 2012: around 500 apps share up to 1 billion entities every day

13


Facebook is the central destination of applications data –

Facebook does not build much upon their data, so far. But there is a huge potential

14


Search over Data from Native Apps

• Those data are too large to be pushed to users (like in Newsfeed) • But it is useful! (“What music clubs do my friends visit in New York?”, “What music does my friend John like?”) • We need to search over these data

15


Social Search Project at Yandex Labs

16


Social Search Project at Yandex Labs

17


Challenges in building Search for Social and Native Apps Data

1. 2. 3. 4. 5.

Collecting Data Representing real-world objects in search results Natural language as a search interface Result ranking Aggregating data into meaningful stories, using data mining techniques, natural language generation and visualization

18


Collecting Data from Social Networks – – –

It is not the Web where you can crawl Major social networks open APIs for building applications upon user’s data Common practice: the user gives access to his account to your application, application can get the data of the user’s visibility scope

19


Representing real-world objects in search results •

•

Native apps typically operate in a particular domain Foursquare -> places, Foodspotting -> food, TripAdvisors -> trips Objects are media rich (places contain name, address, category, photos ...)

20


Representing real-world objects in search results

• Уровень один – Уро

21


Asking Queries in Natural Language • Data is graph => keyword search does not fit

– –

“Show me restaurants in San Francisco visited by my friend Martin” “Who of my friends likes Lady Gaga?”

22


Application publish via verbs

• “What do my friends like to cook?” 23


Asking Queries in Natural Language • Objects are typed entities and have multiple parameters, different for every type => visual interface is very complex! • “Open restaurants nearby where they serve sashimi”

24


Building Stories from Data • Each data item is an elementary signal. Does not bring much value => aggregating into a coherent story gives more value

Michael’s favorite place in SF!

25


Building Stories from Data • Visualization of friends data on the map Recce maps application

26


Ranking Search Results • Popularity among friends + recency (TweetedTimes) • Social Graph: – Not all of your friends are equal => EdgeRank for friends relationships – Categorization of friends: music, sport, news... – Kind of “Klout score” for the user’s friends • Interests inferred from the user’s profile. Identifying the user’s interests by her posts • User’s current context (geo, time, events in calendar) • User’s feedback 27


Maria Grineva [email protected]

Thank you Monday, November 12, 12