Diversity-Aware Recommendation for Human Collectives

Pavlos Andreadis, Sofia Ceppi, Michael Rovatsos, Subramanian RamamoorthySchool of Informatics, University of Edinburgh

Robust Autonomy and Decisions group

CISA Agents group

(FOCAS) (ICT-2011.9.10), as a Collaborative Project (generic), under the 7th Framework programme, Grant agreement n. 600854.

The SmartSociety project is supported by the European Commission, in the area "FET Proactive: Fundamentals of Collective Adaptive Systems"

ECAI, DIVERSITY WorkshopHague – August, 29 2016

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Sharing Economy Applications

Requests Potential allocation

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Ridesharing Example

13:35

14:00

S

D

13:35

14:00

12:00

17:00

Requests Potential allocation

Arrival:

Arrival:

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Diversity-Aware Recommendation

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Diversity-Aware Recommendation

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Diversity-Aware Recommendation

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Problems to Address

Selecting set of solutions

Aiding user coordination

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Selecting Set of Solutions

Multiple criteria

Goal: Adaptive trade-of of system-level utility and fairness

system-level utility:

fairness:Social Welfare

number of Allocated Passengers

number of Drivers

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Aiding User Coordination Users select according to solution utility

How? Taxation

Taxation scheme depends on user selection behaviour

– Noiseless

– Constant noise

– Logit noise

modify

Goal: Sponsor a solution using minimal taxation

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Constraints: feasibility

Generating the Recommendation Set

MILPsystem

Constraints: MILPsystem +

MILPfirst

MILPothersk-1 x

Constraints: MILPfirst + + taxation constraint

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Experiment Design

Metrics: System utility; Fairness; Num Passengers allocated; Num Drivers w. passengers

Evaluations performed after user selections.

Num of users (10, 20); percentage of which drivers (20, 30, 40 %);

Utility threshold (50, 75, 100 %);

User selection model (constant, logit); For logit noise, probability (60, 80 %).

100 experiment instances per configuration.

VS VS

with rejection

Set Recommendation Benchmark Allocation Benchmark

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Set Recommendation Benchmark,Logit Noise

(no

n-)

We can outperform the benchmark in

terms of both system utility and fairness.

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Set Recommendation Benchmark,Logit Noise

(no

n-)

We can outperform the benchmark in

terms of both system utility and fairness.

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Set Recommendation Benchmark,Logit Noise

(no

n-)

We can outperform the benchmark in

terms of both system utility and fairness.

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Set Recommendation Benchmark,Logit Noise

(no

n-)

We can outperform the benchmark in

terms of both system utility and fairness.

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Allocation Benchmark,Logit Noise

We can allow users to have a choice at no

cost to the system or users.

with rejection(n

on

-)

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Allocation Benchmark,Logit Noise

with rejection(n

on

-)

We can allow users to have a choice at no

cost to the system or users.

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We presented a methodology for aiding the coordination of user collectives, in the absence of agent communication.

Ongoing work Expand uncertainty to consider beliefs over preferences;

Incorporate active learning procedures in the MILPs;

Examine robustness to varying degrees of incorrect assumptions.

Set Recommendation requires explicitly handling the uncertainty in user behaviour;

Our procedure can match the performance of a direct allocation (given rejection);

We can allow users to have a choice at no cost to the system;

We allow for adaptively trading-of system-level utility and fairness.

Conclusions

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Why Diversity-Aware?

We present users with options, and we are robust to innacurate

representations of their preferences. Further, we are able to learn from

their choices. We can achieve this at no cost to the users or the collective,

and we can adaptively trade-of between collective and user-specific

criteria.