ValuePick : Towards a Value-Oriented Dual-Goal Recommender System
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Transcript of ValuePick : Towards a Value-Oriented Dual-Goal Recommender System
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ValuePick: Towards a Value-Oriented Dual-Goal Recommender System
Leman Akoglu Christos Faloutsos
OEDM in conjunction with ICDM 2010 Sydney, Australia
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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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v253v162v261v327
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Dual-goal Recommender Systems vertices ranked by
proximity
network-“value”
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Dual-goal Recommender Systems
v253v162v261v327
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network-“value” vertices ranked by
proximity
network-“value”
Trade-off user satisfaction
vs. network
connectivity 6 of 19
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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
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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
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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
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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
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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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