1. A Recommendation Mechanism for Contextualized mobile advertising S.-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 2

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 3

4. 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 4 Proposed method

5. Vector-based Representation Space Features in commercial/non-commercial advertisements Mobile Ad representation User profile representation 5 AttributesAttribute values CategoryWholesale and retail, arts and entertainments, others DayWeekdays, weekend TimeA time slot(17:00 pm before), B time slot (17:00 pm after) PlaceOutdoors, indoors and formal, indoors and informal FeeFree, fee-based PerformerTop celebrities, others Proposed method n : total number of features m i : the number of possible values for ith feature n : total number of features m i : the number of possible values for ith feature W Iiaj : User’s interest in the jth value of ith feature

6. 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 6 Proposed method

7. 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 7 Proposed method: Recommendation Mechanism R j : the ratio of the reference of the jth stereotype vector

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

9. 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 9 Proposed method

10. 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, Score U ) 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 10 Proposed method

11. 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 Score Sum –3) Compute the preference weights 11 Proposed method X i : Each input value Score i : The output value of PWNN X i : Each input value Score i : The output value of PWNN W i : Preference weight of X i in the user streotype

12. 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 12 Proposed method

13. 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) 13Experiments

14. 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 14Experiments

15. Stable Interests and User Type 15 Experiments: Results

16. 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 16 Experiments: Results

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

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 18