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Ridesharing: from the simple matching scenario to the real world application

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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/

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Ridesharing: from the simple matching scenario to the real world application

  1. 1. Ridesharing from the simple matching scenario to the real world application Sofia Ceppi Michael Rovatsos Pavlos Andreadis
  2. 2. Andy Betty Charles Diana Edward
  3. 3. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
  4. 4. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
  5. 5. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
  6. 6. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
  7. 7. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
  8. 8. Andy Betty Charles Diana Edward from Edinburgh to Glasgow
  9. 9. Uncertainty due to human behaviour
  10. 10. Uncertainty due to human behaviour imposing a solution recommending a set of solutions
  11. 11. Recommending a set of solutions: Problems
  12. 12. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems
  13. 13. 1) “optimal” set of solutions 2) influence users choices 3) How to learn users preferences Recommending a set of solutions: Problems
  14. 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. 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. 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. 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. 18. Single User and Collective of Users
  19. 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. 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. 21. Set of Solutions Influence the Users Learning
  22. 22. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning
  23. 23. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Constraints feasibility
  24. 24. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Constraints feasibility
  25. 25. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Constraints feasibility
  26. 26. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Constraints Constraints feasibility MIP*
  27. 27. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Constraints Constraints feasibility MIP*
  28. 28. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Objective Constraints Constraints feasibility MIP*
  29. 29. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Objective Constraints Constraints Constraints feasibility MIP* MIPfirst
  30. 30. MIPfirst MIPothers MIP* Set of Solutions Influence the Users Learning Objective Objective Objective Constraints Constraints Constraints feasibility MIP* MIPfirst
  31. 31. Set of Solutions Influence the Users Learning
  32. 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. 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. 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. 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. 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. 37. Set of Solutions Influence the Users Learning
  38. 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. 39. Set of Solutions Influence the Users Learning MIPfirst MIPothers modify users utility MIP* standard learning techniques
  40. 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. 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. 42. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
  43. 43. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
  44. 44. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
  45. 45. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
  46. 46. Utility Model Response Model Belief Update Noiseless Constant Noise Logit Noise Model
  47. 47. MIPs + Taxation MIPfirst MIPothers MIP*
  48. 48. MIPs + Taxation MIPfirst MIPothers MIP* Sponsored Solution
  49. 49. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective ConstraintsSponsored Solution
  50. 50. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Sponsored Solution
  51. 51. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
  52. 52. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
  53. 53. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
  54. 54. MIPs + Taxation MIPfirst MIPothers MIP* MIPfirst Objective Constraints Noiseless and Constant Noise Models Logit Model Sponsored Solution
  55. 55. MIPs + Taxation + Active Learning
  56. 56. Select the Set of Solutions such that the Expected Value Of Information (EVOI) is maximized MIPs + Taxation + Active Learning
  57. 57. Select the Set of Solutions such that the Expected Value Of Information (EVOI) is maximized MIPothers MIPs + Taxation + Active Learning
  58. 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. 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. 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. 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. 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. 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. 64. Conclusion Ridesharing as real world application uncertainty due to human behaviour imposing → recommend recommendation set + influence users + learning Thank you - Questions?
  65. 65. Conclusion Ridesharing as real world application uncertainty due to human behaviour imposing → recommending recommendation set + influence users + learning Thank you - Questions?
  66. 66. Ridesharing as real world application uncertainty due to human behaviour imposing → recommending recommendation set + influence users + learning
  67. 67. Ridesharing as real world application uncertainty due to human behaviour imposing → recommending recommendation set + influence users + learning

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