Personalizing the Web for Mobile Users Corin Anderson Pedro Domingos Dan Weld

2 The PC-centric web Assume 1024x768 –Micromanage page layout –Lots of real estate for important links Assume fast net –Images are okay –Following links okay Assume fast CPU –Client-side Javascript

3 Doesn’t work on mobile devices Very few lines of text –Lots of scrolling Few pixels, colors –Hard to find desired link Slow net connectivity –Following links slow No client-side scripting –Page functionality lost

4 First approach: syntactic translation Tag-by-tag, transcode for small screen –Replace images with alt text –Flatten s Problem: not all content is created equally –Shortcut links are useful –Links to “Our sponsors” aren’t –Lacks awareness of needs of each visitor

5 Our approach: web personalizers An intermediary between server and user Personalizes content for each user Personalizations include: –Making frequently-visited pages easier to reach –Highlighting content of interest –Eliding links and content of no interest

6 A personalizer in context  GET /  palm.net Access Logs Personalizer User Models Personalize content Model users  GET / 

7 Our implementation: Proteus 1.Build user model –Sequence of pages visited –Textual content viewed 2.Search space of web sites –“State” is personalized web site –Evaluate site with expected utility for user

8 User model – raw data Sequence of pages requested –Server or proxy logs (“Corey, the proxy’s down…”) –Assume 1 computer :: 1 user –Temporally-close requests are related (sessions) Text of each page some-pc.cs22/Feb/ :49:13 -/ some-pc.cs22/Feb/ :49:23 some-pc.cs22/Feb/ :49:34 some-other-pc.cs22/Feb/ :49:55 -/ some-pc.cs22/Feb/ :50:08 some-other-pc.cs22/Feb/ :50:20 some-pc.cs22/Feb/ :12:36

9 User model – derived data Frequency of page visits –# times user visited each page Probability of page visit –# times visited page / # sessions Content word vector –“interest” value in each word seen

10 Site evaluation cnethelp newsfree hardware downloadclick builderhere gamemonday jobnovember auctionadvertisement price tech

11 Expected utility Value of site = expected utility of site E[U(site)] = E[U(first page)] E[U(page)] = E[U(first screen)] Utility from screen content: intrinsic utility Utility from screen navigation: extrinsic utility

12 Intrinsic utility Content similarity –Words on screen vs. words seen before –Scale using TFIDF –Scale by position in session – later pages’ words worth more Frequency of visits –how often user views screen

13 Extrinsic utility User may take one or many actions –Follow link L 1, follow link L 2, … –Scroll down Each action leads to new screen:E[U(dest.)] Actions have probabilities:P(action) Actions have cost:  (action)

14 Search for optimal site Search control –Steepest descent –Run about 20 iterations Search operators –Add shortcut link –Elide content

15 Shortcut link A link that makes a long path shorter –A  B  C  D suggests adding A  D

16 Elide content Remove unnecessary content Don’t make irrevocable changes! –Replace content with link to original 13 lines 1 line

17 Exp’t 1: live human subjects Observe real users on the desktop –Behavior based on seeded questions Measure performance on mobile device –Unmodified vs. personalized sites –Tasks taken from same seed distribution

19 Analysis of exp’t 1 Content elision overly aggressive –Proteus favors words on page over links on page Elision links inconspicuous –No different from normal links Shortcut links underused –Proteus didn’t always make them –Users didn’t scroll enough to find them

20 Exp’t 2: Simulated users Deterministic automaton “Scripts” made from sessionized logs –Use non-seeded behavior from exp’t 1’s logs –Goal: get to last page in session Sim user follows script, except: –If a shortcut link is there and useful, take it –If the next link has been elided, find it

23 Continuing work: clustering Single user model may be starved for data But we have many user models available Cluster users, then build cluster models Site evaluation depends on user and cluster

24 Clusters at

26 Questions about clustering Is model-based clustering the right choice? What model should we use? –0 th, 1 st, 2 nd order Markov Can we model at page granularity? What about non-sequence user attributes –User domain, registration information

27 Questions about Personalizers What other personalizations would be useful? –Create new pages? –Adapt layout of page? How should we measure intrinsic utility? –Word vectors? How are the terms scaled? –Past-visit dwell time?

28 More questions How can we make search faster? –Evaluation is slow – can we approximate? –Search infeasible as sites, adaptations scale How do we evaluate Proteus? –User studies are tricky –Can simulated web behavior be believable?

30 Conclusions Mobile web experience must be personalized Personalizers have many benefits Proteus successfully improves mobile web experience

33 Desktop vs. mobile browser