Jongwon Yoon 2011. 05. 04

1. A Recommendation Mechanism forContextualized mobile advertisingS.-T. Yuan et al., Expert Systems with Applications, vol. 24, no. 4, pp. 399-414, 2003. Jongwon Yoon 2011. 05. 04

2. Outline • Introduction • Proposed method • Architecture • Vector-based Representation • Recommendation Mechanism • User stereotype KB and profile • Learn user profile • Recommendation function • Experiments • Experimental measurements • Experimental user types • Results • Summary

3. Introduction • Mobile advertising • One of fields in mobile commerce • Possible to target users according to user’s contexts • It is essential that fully personalized mobile advertising infrastructure • Proposed method • A personalized contextualized mobile advertising infrastructure for advertising the commercial/non-commercial activities (MALCR) • Contributions • 1) Interactive advertising with customized recommendation • 2) Provide a representation space • 3) Recommendation mechanism using implicit user behaviors

4. Proposed method Architecture • Learn users’ profiles from implicit browsing behaviors • Difficult to obtain direct keypad inputs for every request • Two ways of service • Pull mode : the dominating mode / requests recommendations • Push mode : provide SMS if permission from users is granted

5. Proposed method Vector-based Representation Space • Features in commercial/non-commercial advertisements • Mobile Ad representation • User profile representation n : total number of features mi : the number of possible values for ith feature WIiaj : User’s interest in the jth value of ith feature

6. Proposed method Recommendation Mechanism • Concepts • 1) Minimize users’ inputs : Use implicit behaviors • 2) Understand users’ interests • 3) Top-N scored advertisements • Browsing interface to capture implicit behaviors • Behaviors : Clicking order, clicking depth, and clicking count

7. Proposed method: Recommendation Mechanism User Stereotype KB and Profile • User Stereotype KB • Used to expedite the learning of the users’ interests • Stores a variety of typical users’ interests • Initially pre-defined (see next slide) and adjusted during usages • User profile • Use multiple user stereotype vectors Rj : the ratio of the reference of the jth stereotype vector

8. Proposed method: Recommendation Mechanism An Example of User Stereotype KB

9. Proposed method Learn User Profile: Overview • Two-level neural networks approach • One-level : Requires explicit user scoring to train (Not appropriate for mobile devices) • Two-level neural networks • User_score NN (USNN) : Calculate score using user’s implicit behaviors • Preference_weight NN (PWNN) : Calculate preference weights for the certain Ad • Flow of user profile learning • 1) Obtain user scores • 2) Use the Ads and corresponding scores as training examples of PWNN • 3) Obtain preference weights • 4) Perform sensitivity analysis and update the user profile

10. Proposed method Learn User Profile: Usage of Two-level NNs • On the request of a new stereotype • Use pre-trained user stereotype vector and NN weights • Compute customized stereotype by training PWNN • PWNN structure • Use USNN to obtain user’s score as training examples : (M_AD, ScoreU) • Pre-trained USNN generates reasonable score from the value of (O, D, C) • On the use of existing stereotype • Evolve the customized user stereotype vector by training PWNN

11. Proposed method Learn User Profile: Sensitivity Analysis • Purpose • To transform PWNN outputs into the vector-based representation • Process • 1) Calculate score for each input attribute • 2) Compute ScoreSum • 3) Compute the preference weights Xi: Each input value Scorei : The output value of PWNN Wi: Preference weight of Xi in the user streotype

12. Proposed method Recommendation Function • Recommend top-N scored advertisements • Ranks Mobile Ads relevant to a designated location • Process • 1) Compute score for each Mobile Ads • 2) Rank the scores of all Ads • 3) Recommend Top-N Ads if in the Pull mode • 4) Push Top-1 Ads to the user if in the Push mode

13. Experiments Experimental Measurements • Averaged ScoreU Growth • Score computed from a user’s implicit browsing behaviors • Shows how close the Top-N match the user’s interests • Instance precision, recall, and fallout • Using learned vector representation(Top-1) and target vector representation • Instance precision = Found/(Found + False alarm) • Instance recall = Found/(Found + Missed) • Instance fallout = False alarm/(False alarm + Correctly rejected)

14. Experiments Experimental User Types • Three types : 50 users in each type • First use (Login0) ▶ 10 Trials (Login1 ~ Login10) • Extremely focused(U1) • Interests are highly concentrated • A general query is generated only at Login0 • Extremely Scattered (U2) • 3 general queries are generated • Middle (U3) • Two general queries are generated in each use from Login1 to Login5 • Assume that recommendations conform to the user’s interests after Login5

15. Experiments: Results Stable Interests and User Type

16. Experiments: Results Unstable Interests but Stable User Type • Interests are randomly changed at Login3 • Four situations • Implicit change and no(L0)/yes(L1) weighting on the most current stereotype • Explicit change and no(L3)/yes(L4) weighting on the most current stereotype

17. Experiments: Results Unstable Interests and User Type • User type changing • Login1-3 : Extremely Focused (U1) / Login4-10 : Extremely Scattered • Interests changing • Randomly changed at Login5

18. Summary • Proposed MALCR • Mobile advertising infrastructure • Furnish a new customized recommendations • Provide a representation space • vector-based Ad and user profile representations • Devise a recommendation mechanism • Two-level NNs • Future works • Testing advertising effect measurement • L is 1 if a user exerts after receiving Top-1 • L is 0 otherwise • T is the lapse of time between the push and exertion