Ridesharing: from the simple matching scenario to the real world application
•
2 likes•832 views
Sharing economy applications need to coordinate humans, each of whom may have different preferences over the provided service. Traditional approaches model this as a resource allocation problem and solve it by identifying matches between users and resources. These require knowledge of user preferences and, crucially, assume that they act deterministically or, equivalently, that each of them is expected to accept the proposed match. This assumption is unrealistic for applications like ridesharing and house sharing (like airbnb), where user coordination requires handling of the diversity and uncertainty in human behaviour. We address this shortcoming by proposing a diversity-aware recommender system that leaves the decision-power to users but still assists them in coordinating their activities. We achieve this through taxation, which indirectly modifies users’ preferences over options by imposing a penalty on them. This is applied on options that, if selected, are expected to lead to less favorable outcomes, from the perspective of the collective. The framework we used to identify the options to recommend is composed by three optimisation steps, each of which has a mixed integer linear program at its core. Using a combination of these three programs, we are also able to compute solutions that permit a good trade-off between satisfying the global goals of the collective and the individual users’ interests. We demonstrate the effectiveness of our approach with two experiments in a simulated ridesharing scenario, showing: (a) significantly better coordination results with the approach we propose, than with a set of recommendations in which taxation is not applied and each solution maximises the goal of the collective, (b) that we can propose a recommendation set to users instead of imposing them a single allocation at no loss to the collective, and (c) that our system allows for an adaptive trade-off between conflicting criteria. For the full paper, see http://www.smart-society-project.eu/diversityawarerecommendation/
Recommended
Recommended
More Related Content
What's hot
What's hot (18)
Similar to Ridesharing: from the simple matching scenario to the real world application
Similar to Ridesharing: from the simple matching scenario to the real world application (20)
More from Smart-Society-Project
More from Smart-Society-Project (12)
Recently uploaded
Recently uploaded (20)
Ridesharing: from the simple matching scenario to the real world application
- 1. Ridesharing from the simple matching scenario to the real world application Sofia Ceppi Michael Rovatsos Pavlos Andreadis
- 9. Uncertainty due to human behaviour
- 10. Uncertainty due to human behaviour imposing a solution recommending a set of solutions
- 11. Recommending a set of solutions: Problems
- 12. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems
- 13. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems
- 14. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems Andy Betty Charles
- 15. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems Andy Betty Charles
- 16. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems Andy Betty Charles
- 17. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems Andy Betty Charles
- 18. Single User and Collective of Users
- 19. Single User and Collective of Users User's utility function: - how good a solution is for the user - depends on user's requirements and preferences System's utility function: - depends on social welfare - other factors
- 20. Single User and Collective of Users User's utility function: - how good a solution is for the user - depends on user's requirements and preferences System's utility function: - depends on social welfare - other factors
- 21. Set of Solutions Influence the Users Learning
- 22. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning
- 23. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Constraints feasibility
- 24. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Constraints feasibility
- 25. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Constraints feasibility
- 26. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Constraints Constraints feasibility MIP*
- 27. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Constraints Constraints feasibility MIP*
- 28. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Objective Constraints Constraints feasibility MIP*
- 29. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Objective Constraints Constraints Constraints feasibility MIP* MIPfirst
- 30. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Objective Constraints Constraints Constraints feasibility MIP* MIPfirst
- 31. Set of Solutions Influence the Users Learning
- 32. Set of Solutions Influence the Users Learning IDEA: to artificially modify users' utility AIM: all users prefer a solution that lead to a feasible global solution Explicit Approaches: intervention (possible) future reward Implicit Approaches: discounts taxation
- 33. Set of Solutions Influence the Users Learning IDEA: to artificially modify users' utility AIM: all users prefer a solution that lead to a feasible global solution Explicit Approaches: intervention (possible) future reward Implicit Approaches: discounts taxation
- 34. Set of Solutions Influence the Users Learning IDEA: to artificially modify users' utility AIM: all users prefer a solution that lead to a feasible global solution Explicit Approaches: intervention (possible) future reward Implicit Approaches: discounts taxation
- 35. Set of Solutions Influence the Users Learning IDEA: to artificially modify users' utility AIM: all users prefer a solution that lead to a feasible global solution Explicit Approaches: intervention (possible) future reward Implicit Approaches: discounts taxation
- 36. Set of Solutions Influence the Users Learning IDEA: to artificially modify users' utility AIM: all users prefer a solution that lead to a feasible global solution Explicit Approaches: intervention (possible) future reward Implicit Approaches: discounts taxation
- 37. Set of Solutions Influence the Users Learning
- 38. Set of Solutions Influence the Users Learning 1) utility model: what users like 2) response model: how users behave 3) belief update: done after each user's action
- 39. Set of Solutions Influence the Users Learning MIPfirst MIPothers modify users utility MIP* standard learning techniques
- 40. PROBLEM: properties guaranteed by the MIPs may not hold when taxation is applied Set of Solutions Influence the Users Learning MIPfirst MIPothers modify users utility MIP* standard learning techniques
- 41. MISSING: active learning PROBLEM: properties guaranteed by the MIPs may not hold when taxation is applied Set of Solutions Influence the Users Learning MIPfirst MIPothers modify users utility MIP* standard learning techniques
- 42. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
- 43. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
- 44. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
- 45. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
- 46. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
- 48. MIPs + Taxation MIPfirst MIPothers MIP* Sponsored Solution
- 50. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Sponsored Solution
- 51. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
- 52. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
- 53. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
- 54. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
- 55. MIPs + Taxation + Active Learning
- 56. Select the Set of Solutions such that the Expected Value Of Information (EVOI) is maximized MIPs + Taxation + Active Learning
- 57. Select the Set of Solutions such that the Expected Value Of Information (EVOI) is maximized MIPothers MIPs + Taxation + Active Learning
- 58. Select the Set of Solutions such that the Expected Value Of Information (EVOI) is maximized MIPothers “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) max EVOI → max Expected Utility MIPs + Taxation + Active Learning
- 59. “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) Query Set of Options Recommend an Option MIPs + Taxation + Active Learning
- 60. “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) Query Set of Options Recommend an Option In the Ridesharing scenario: commend a set of options - only global solutions have an utility for the system - any solution may have an utility for the system MIPs + Taxation + Active Learning
- 61. “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) Query Set of Options Recommend an Option In the Ridesharing scenario: ● apply V. and B. work commend a set of options - only global solutions have an utility for the system - any solution may have an utility for the system same results the result holds also in this case MIPs + Taxation + Active Learning
- 62. “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) Query Set of Options Recommend an Option In the Ridesharing scenario: ● apply V. and B. work ● recommend a set of options - only global solutions have an utility for the system - any solution may have an utility for the system same results the result holds also in this case MIPs + Taxation + Active Learning
- 63. “Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Set” Paolo Viappiani and Craig Boutilier (NIPS 2010) Query Set of Options Recommend an Option In the Ridesharing scenario: ● apply V. and B. work ● recommend a set of options - only global solutions have an utility for the system - any solution may have an utility for the system same results same results the result holds also in this case MIPs + Taxation + Active Learning
- 64. Conclusion Ridesharing as real world application uncertainty due to human behaviour imposing → recommend recommendation set + influence users + learning Thank you - Questions?
- 65. Conclusion Ridesharing as real world application uncertainty due to human behaviour imposing → recommending recommendation set + influence users + learning Thank you - Questions?
- 66. Ridesharing as real world application uncertainty due to human behaviour imposing → recommending recommendation set + influence users + learning
- 67. Ridesharing as real world application uncertainty due to human behaviour imposing → recommending recommendation set + influence users + learning