ValuePick: Towards a Value-Oriented Dual-Goal Recommender System

Leman Akoglu Christos Faloutsos

OEDM in conjunction with ICDM 2010 Sydney, Australia

Recommender Systems

Traditional recommender systems try to achieve high user satisfaction

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Dual-goal Recommender Systems

Dual-goal recommender systems try to achieve (1) high user satisfaction as well as(2) high-“value” vendor gain

-“value”

Trade-off user

satisfaction vs.

vendor profit

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vertices ranked by proximity

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Dual-goal Recommender Systems

network-“value”

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query vertex

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Dual-goal Recommender Systems vertices ranked by

proximity

network-“value”

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Dual-goal Recommender Systems

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network-“value” vertices ranked by

proximity

network-“value”

Trade-off user satisfaction

vs. network

connectivity 6 of 19

Vendor

Main concerns: We cannot make the highest value

recommendations Recommendations should still reflect

users’ likes relatively well

Dual-goal Recommender Systems

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User

User Vendor

Carefully perturb (change the order of) the proximity-ranked list of recommendations

Controlled by a tolerance for each user

ValuePick: Main idea

ζζ

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ValuePick Optimization Framework“valu

e” proximity Total expected

gain (assuming proximity ~ acceptance prob.)

toleranceϵ [0,1]

average proximity score of original top-k

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DETA

ILS

ValuePick ~ 0-1 Knapsackvalue

maximum weight W allowedweight of item

i

We use CPLEX to solve our integer programming optimization problem

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DETA

ILS

Pros and Cons of ValuePickCons: In marketing, it is hard to predict the

effect of an intervention in the marketing scheme, i.e., not clear how users will respond to ‘adjustments’

Pros: Tolerance ζ can flexibly (and even

dynamically) control the `level-of-adjustment’

Users rate same item differently at different times, i.e., users have natural variability in their decisions.

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Experimental Setup I Two real networks

Netscience – collaboration network DBLP – co-authorship network

Four recommendation schemes:1) No Gain Optimization (ζ = 0)2) ValuePick (ζ = 0.01, ζ = 0.02)3) Max Gain Optimization (ζ = 1)4) Random

“value” is centrality

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Experimental Setup II

Given a recommendation scheme At each step

For each node Make a set of recommendations to node using Node links to node ϵ with prob. proximity(,)

Re-compute proximity and centrality scores

Simulation steps:

We use =5 and =30

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Comparison of schemes

ValuePick provides a balance between user satisfaction (high E), and vendor gain (small diameter).

EX

PER

IMEN

TS

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Recommend by heuristic

Simple perturbation heuristics do not balance user satisfaction and vendor gain properly.

EX

PER

IMEN

TS

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Computational complexityEX

PER

IMEN

TS

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Making ValuePick recommendations to a given node involves:1 - finding PPR scores

O(#edges)2 - solving ValuePick optimization w/ CPLEX

1/10 sec. to solve among top 1K nodes

Conclusions Problem formulation: incorporate the

“value” of recommendations into the system Design of ValuePick:

parsimonious single parameter ζ flexible adjust ζ for each user

dynamically general use any “value” metric

Performance study: experiments to show proper trade of user

acceptance in exchange for higher gain CPLEX with fast solutions

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User Vendor

ζ

THANK YOUwww.cs.cmu.edu/~lakoglu

lakoglu@cs.cmu.edu

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