Personalization on Yahoo: Customized Yahoo experience for users

1. PERSONALIZATION “Experience with Personalization on Yahoo” And “Personalized Search” Presenter: Rob Drum

2. Three Personalized Yahoos! • My Yahoo! • Yahoo! Companion • Inside Yahoo! Search

3. My Yahoo • Customized personal copy of Yahoo • Hundreds of choices for modules • i.e. news, stocks, weather, sports scores • Updated automatically giving the user all the most current info. • Personalization • As specific as TV channels shown in your guide and what local provider you use to tell exact channels, or as general as top health news • Layout can be personalized as well • Automatic personalization? • Sports module can show teams found in users profile • Updates at specific times from 15 min to several hours • Easy to add easy to remove • Modules can be selected from any yahoo site (technology news will say next to it “add to my yahoo”). Can be removed or edited easily from your personal home page

4. Yahoo! Companion • Toolbar at the top by the browser • Several choices of interfaces (currently two: regular and stock market) • Customizable mini yahoo that allows users to access yahoo content from anywhere on the web • Can be accessed from any computer because all settings including bookmarks are stored on the server

5. Inside Yahoo! Search • Able to complement the usual search with “direct focused content that can sometimes be personalized” • Example query “300” • Returns pointer to “yahoo movies” • Yahoo movies shows a movie image, cast and movies show times • If the user has been to this page before and entered in a zip code then this will be remembered and automatically used to update show times. Of course zip code can be changed by user.

6. Large Scale • Access • Speed is of the utmost importance • Custom built UDB • Allows for hot backup at all times • Optimized, cached, fully redundant communication between UDB and MY Yahoo! Page display machines • Privacy and security • Encrypted sensitive data on machines with restricted access • Both internal and external people devoted specifically to privacy and security measures • User interface • Predictability is important • Users must understand how system works so that they will be able to experiment with personalization • News • Specific Vs. Broad • Because I read a medical journal, articles about cancer are displayed. This can be confusing and can raise privacy concerns • “I live in 35244 my weather is sunny” • This is obvious, easy, and always helpful

7. General Observations and Lessons Learned • Most users take what is given to them and never customize. Why? • Default page is so good there is no need for additional effort • Customization tools are too difficult • Many people do not need complex personalization • A great deal of effort should go to the default page • Zip codes are crucial on default page. They give great personal news based on geographic location • Power users will do amazing things—never underestimate them • Design everything for infinite growth, do not use artificial limits unless absolutely necessary • Some users have sites as big as 500KB

8. General Observations and Lessons Learned Cont. • People generally don’t understand the concept of personalization • Such personalization did not previously exist • We must do everything possible to make sure it is presented in an intuitive and useful way • Make sure you address all your users • Site must be able to adapt • Users outside U.S. must be able to use the features in the same way • Users on other platforms (variety of browsers, linux, mac ) • Learn from users • Must constantly look at logs to see user activity • It is often surprising- Users will always find an unexpected way to use any new technology

9. Conclusion to Yahoo! Personalization • “The challenge is to combine business, technology, and society issues in a way that benefits consumers without violating their privacy.” • Power users get the most out of current customization • The entry bar must be lowered to allow lower level users to experience the same benefits it must be made clear that personalization is possible

10. Personalized Search • Contextual Computing- refers to the enhancement of a users interactions by understanding the user, the context, and the applications of the information being used, typically across a wide set of user goals. Contextual computing is not just about modeling user preferences and behavior or embedding computation everywhere, it’s about actively adapting the computational environment—for each and every user—at each and every point of computation • Contextual computing approach to search • move from consensus relevancy to personal specific relevancy • This will decrease the time that it takes both novices and experts to find information

11. Information Retrevial • Currently Google techniques • Hyperlinks and Citation • Author relevancy • Problem entire population does not value terms in the same way as certain authors or authoring community • Usage based • Frequency, recently and or duration of interaction by users • Computes relevancy for entire community • Forgets to take into account that relevance is relative for each user • What is needed is a way to compute PERSONAL RELEVANCY

12. The Outride Approach • Contextualization- interrelated conditions that occur within an activity • Includes- nature of information, the applications in use, when, and so on • Individualization- totality of characteristics that distinguishes and individual • User’s goals, prior and tacit knowledge, past information-seeking behaviors, among others • Careful integration into user interface • What the system is doing to personalize as well as to undo personalization • Personalize search for an active searcher • Query augmentation • Result processing

13. The Outride Approach Cont. • “Personalization engine sits between a user interface and an intra/Internet search engine. Once a user has entered a query, the query can be compared against the contextual information available to determine if the query can be refined to include other terms” • EX: user is looking at a series of pages on car information and queries “contour” the system may augment the query by adding the term “car” or “ford” to provide the user with results about the Ford Contour car. • EX: if search is on a topic previously seen, the system can reinforce query with similar terms of suggest results from prior searches. • Will not augment if it is a new topic • Final result of a query augmentation is a more precise query that will be submitted to a search engine for processing

14. Outride Approach Cont. • Results can be individualized based on information in the users model • Demographic information • Prevent minors from seeing adult content • Also re-rank search results based on similarity of content and the user’s profile • Java programmer gets info related to developers • Teacher of programming languages gets info about Java tutorials and overviews • Re-ranking results based on frequency, how long ago, or duration of usage • Allows users to identify the most popular, faddish and time-consuming pages they have visited • Divided into “have seen, haven’t seen” • Show me only what I know • Show me only what I don’t know • Searches can be aggregated across multiple profiles • Most popular among hikers • Most popular among entire population

15. Outride Personalized Search System • Generalized architecture for the personalization of search across a variety of information ecologies • As a part of the browser the component supports direct manipulation and has access to all user interactions • Four separate info spaces • Personal hierarch of user’s links (personal) • Catalog of links (directory) • User’s surf history (history) • Search results from the entire web (web) • Browse or search under each tab • Effectively defines the scope of activities available for search

16. Open Directory Project (ODP) • Each users has own weighting across top 1,000 categories of the ODP • If user starts with favorites they are classified and this is the base of the ODP • If no content is stored in the beginning then the sidebar will store the next 1,000 clicks and keep this database updated

17. Query Augmentation • Query augmentation is preformed by integrating various clues provided by the instrumentation of the interface and user models • Comparing the query to the users’ content profile using vector methods provide better results

18. Result Set Processing • Performed across typically 1,000 results • Filtered by “have seen, have not seen” • Usage based re-rankings • Re-ranking by user model profile • Titles and other metadata are compared via vector methods against the user profile • “We found the combination of query augmentation and result processing with a contextually designed interface to be quite effective in making search easier and faster”

19. Testing Methodology and Results • Tested using metrics (number of actions taken) • Tested by independent company • Measured time and action until finding sufficient information • Query topics • Football • Car shopping • Travel planning • Default user model was used for all participants

20. Results

21. Issues • Some tasks were directly supported by the functionality provided by the Outride system • Use of default profile • User interface for Outride is constantly seen

22. Future Directions • Modeling a user’s changing interests over time • How long ago and frequency helps, but there will always be exceptions • Carefully designed interfaces will help • Success of query augmentation rests on detecting user context switches • Incorrect augmentation can be fixed by users deciding to turn off personalization • Privacy • All users can see their profiles and histories as well as ODP interest weights • Users control what is used by turning it on and off • Result sharing is no longer possible • Can also be addressed at the interface level

23. Conclusion to Outride • “We have presented here a new type of IR system that personalizes the search experience for each user across their interactions. We have shown initial evidence to support our firm conviction that the contextualized computing approach toward the personalization of search is the next frontier toward significantly increasing search efficiency”