Adaptive Personalization for Mobile Content Delivery

1. Adaptive Personalization for Mobile Content Delivery Daniel Billsus, Craig Evans, Raymond Klefstad, Michael J. Pazzani billsus@fxpal.com, craig@ez-data.com, klefstad@uci.edu, pazzani@uci.edu Department of Information and Computer ScienceUniversity of CaliforniaIrvine, CA 92612

2. How do you fit a newspaper into a cell phone?

3. Wireless Web vs. Wired Web • Most Wireless Web applications interact with a simplified browser with simplified markup languages • Smaller Screen Sizes • Limited Input Capabilities • Slower Network Connections • Higher Network Latencies • Less Reliable Network Connections • More Expensive Network Connections • Less Memory • Less Processing Power

4. Qualcomm Brew • Programming language for cell phone applications (C or C++ with cell phone API and libraries) • Many capabilities not present in browsers • Persistent file system • Alarms • Access to servers via http but no predefined browser or markup language • Use wireless downloads and installs application • Launching in US with Verizon, Alltel, US Cellular plus Japan, Brazil, Korea, etc. • However, still quite limited • Limited Program space, file system

5. Adaptive News Browser • Capabilities • Interactive news reading: selecting sections (e.g., sports) and headlines • Batch downloading a user specified number of articles at a user specified time into cache • Uses off-peak minutes • Uses minutes more efficiently • Reduces latency when selecting headlines or sections • Content available when wireless isn’t • Personalization • Reorders articles based on learned user preferences • Determines which articles to download

6. Personalization Constraints • Learns quickly about new users • Smoothly transitions from general to personalized display • Adapts quickly as interests change • Requires little or no work by user • Requires no manual tagging of content • Leverages existing editorial judgment so important novel events are shown to all • Supports search, diversity,related content • High performance & scalable

7. Mobile ContentOne Size Fits All (SMALL)

8. Personalization Middleware • Recommendation • short-term • long-term • diversity • editorial rankings • previous sessions • Interaction layer • search • history • related items • Batch download • Data Management • user models • content cache t r a n s c o d i n g r e t r i e v a l

9. Personalized Content

10. User Customization • Less than 2% of the users do it

11. User Configuration:Inaccurate and hard to use • Check boxes • Keywords Too coarse-grained Ambiguous: Metallica Concert at Verizon Wireless Amphitheater Sold Out Filters vs. prioritizes Requires regular web access Requires constant maintenance

12. Adaptive Personalization

13. Adaptive Personalization II

14. Intelligent Search

15. Adaptive Personalization Solution • Solution: Hybrid Model to Sort Articles without classification • Rank read (and skipped) stories from behavior • Predict rankings of unseen • Short-term: Similarity-based • Long-term: Probabilistic • Editorial or Marketing input: Exponentially decaying bonus • Variety by Similarity: Penalty for being too similar to other recommended article

16. Representation and Similarity Lawmakers Fine-Tuning Energy Plan SACRAMENTO, Calif. -- With California's energy reserves remaining all but depleted, lawmakers prepared to work through the weekend fine-tuning a plan Gov. Gray Davis says will put the state in the power business for "a long time to come." The proposal involves partially taking over California's two largest utilities and signing long-term contracts of up to 10 years to buy power from wholesalers… util-0.339 power-0.329 megawatt-0.309 electr-0.217 energi-0.206 caiso-0.192 california-0.181 florio-0.176 bui-0.156 debt-0.128 lawmak-0.128 state-0.122 wholesal-0.119 partial-0.106 consum-0.105 tune-0.104 alert-0.103 scroung-0.096 bottorff-0.096 iso-0.093 advoc-0.09 testi-0.088 bailout-0.088 crisi-0.085 amid-0.084 price-0.083 long-0.082 bond-0.081 plan-0.081 term-0.08 grid-0.078 reserv-0.077 blackout-0.076 bid-0.076 market-0.074 fine-0.073 deregul-0.07 spiral-0.068 deplet-0.068 liar-0.066 edison-0.065 contract-0.063 condit-0.062 largest-0.061 rate-0.06 takeov-0.059 stock-0.059 michel-0.059 offici-0.058 audit-0.057 billion-0.056 apolog-0.056 auction-0.055 costli-0.055 rip-0.055 shed-0.055 drain-0.055 cost-0.054 skeptic-0.053 anymor-0.053 announc-0.052 craft-0.052 pai-0.051 hour-0.05 take-0.05 super-0.049 howard-0.049 midnight-0.049 dai-0.048 percent-0.048 desper-0.048 flow-0.047 fridai-0.047 sacramento-0.046 sundai-0.046 grai-0.046 unabl-0.045 issu-0.044 set-0.044 shut-0.043 open-0.042 reveal-0.042 mexico-0.042 facil-0.04 tight-0.04 bowl-0.04 calif-0.039 pacif-0.039 expect-0.039 option-0.039 extend-0.039 consecut-0.039 conserv-0.038 roll-0.038 davi-0.038 blame-0.037 bar-0.037 purchas-0.037 credit-0.036 revenu-0.036 stage-0.035 tom-0.035 custom-0.035 grant-0.035 hundr-0.035 fan-0.035 work-0.035 amount-0.035 reduc-0.034 call-0.034 weekend-0.

17. Short Term model

18. Profiles and informative words Health patients (16) study (11) drug (11) doctors (10) food (9) breast (9) surgery (8) cancer (8) health (7) blood (7) women (7) center (7) vitamin (6) graedon (6) exercise (6) cell (6) kids (6) eating (6) body (6) products (6) person (5) procedures (5) nutrition (5) risk (5) pill (5) weight (5) disease (5) care (5) hospital (5) program (5) life (5) diet (5) supplement (5) loss (5) performance (4) bone (4) site (4) surgeon (4) service (4) book (4) tissue (4) anesthesia (4) children (4) meals (4) calcium (4) mestel (4) treated (4) athletes (4) feel (4) blvd (4) correct (4) injuries (4) nurses (4) reserve (4) donned (4) older (3) section Wall Street share (203) percent (192) cents (151) point (136) billion (113) quarter (112) earnings (105) million (101) stock (99) oils (88) sales (83) tires (80) bank (74) company (73) rate (67) yen (64) pound (64) fell (62) price (62) rose (62) ford (60) december (57) bushel (57) soybeans (54) treasury (54) firestone (53) chicago (53) discount (53) euro (52) yield (50) tokyo (50) airlines (49) close (47) bond (47) wheat (47) recalled (45) vehicles (41) loss (41) corn (40) store (40) japan (38) european (37) food (37) workers (36) revenue (35) london (34) plant (33) cars (33) barrel (32) phone (31) year (31) deal (31) merger (30) union (30) crude (28) percentage (28) profit (28) service (28) settlement (28) trade (28) technology (28) growth (27) insurance (27)

19. Bayesian Text Classification

20. Increased Reading per usage After looking at 3 or more screens of headlines, users read 43% more of the personally selected news stories; clearly showing AIS's ability to dramatically increase stickiness of a wireless web application

21. Readership and Stickiness After 6 weeks, 20% more users keep using wireless system when personalized

22. Speed to Effectiveness Initially, AIS is as effective as a static system in finding relevant content. After only one usage, the benefits of AdaptiveInfo's Intelligent Wireless Specific Personalization are clear; after three sessions even more so; and, after 10 sessions the full benefits of Adaptive Personalization are realized

23. Probability a Story is Read 40% probability a user will read one of the top 4 stories selected by an editor, but a 64% chance they'll read one of the top 4 personalized stories - the AIS user is 60% more likely to select a story than a non-AIS user

24. Similarity & Interest

25. Adaptive Personalization Benefits • Consumer • Saves time to find personally valuable information • Enhances the user experience • Carrier • Enhance wireless user’s experience and carrier’s revenue • Increased usage of premium services • Increased retention of customers

26. Evaluating the Hybrid User Model