A Recommendation Mechanism forContextualized mobile advertising

S.-T. Yuan et al., Expert Systems with Applications, vol. 24, no. 4, pp. 399-414, 2003.

Jongwon Yoon2011. 05. 04

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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

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Introduction

• Mobile advertising– One of fields in mobile commerce– Possible to target users according

to user’s contexts• It is essential that fully personal-

ized mobile advertising infrastruc-ture

• Proposed method– A personalized contextualized

mobile advertising infrastructure for advertising the commercial/non-commercial activities (MALCR)

– Contributions• 1) Interactive advertising with cus-

tomized recommendation• 2) Provide a representation space• 3) Recommendation mechanism

using implicit user behaviors

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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

Proposed method

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Vector-based Representation Space

• Features in commercial/non-commercial advertisements

• Mobile Ad representation

• User profile representation

Attributes Attribute valuesCategory Wholesale and retail, arts and entertainments, others

Day Weekdays, weekendTime A time slot(17:00 pm before), B time slot (17:00 pm after)Place Outdoors, indoors and formal, indoors and informalFee Free, fee-based

Performer Top celebrities, others

Proposed method

n : total number of featuresmi : the number of possible values for ith feature

WIiaj : User’s interest in the jth value of ith feature

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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

Proposed method

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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

Proposed method: Recommendation Mechanism

Rj : the ratio of the reference of the jth stereotype vector

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An Example of User Stereotype KBProposed method: Recommendation Mechanism

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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) : Calcu-

late 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

Proposed method

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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

Proposed method

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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

Proposed method

Xi : Each input valueScorei : The output value of PWNN

Wi : Preference weight of Xi in the user streotype

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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

Proposed method

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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)

Experiments

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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

Experiments

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Stable Interests and User TypeExperiments: Results

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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 cur-rent stereotype

– Explicit change and no(L3)/yes(L4) weighting on the most cur-rent stereotype

Experiments: Results

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Unstable Interests and User Type

• User type changing– Login1-3 : Extremely Focused (U1) / Login4-10 : Extremely

Scattered• Interests changing

– Randomly changed at Login5

Experiments: Results

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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