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RecSys 2019 Tutorial on Recommendations in a Marketplace


RecSys 2019, Copenhagen, Denmark


Multi-sided marketplaces are steadily emerging as viable business models in many applications (e.g. Amazon, AirBnb, YouTube), wherein the platforms have customers not only on the demand side (e.g. users), but also on the supply side (e.g. retailer). Such marketplaces involve interaction between multiple stakeholders among which there are diferent individuals with assorted needs. While traditional recommender systems focused specifcally towards increasing consumer satisfaction by providing relevant content to the consumers, multi-sided marketplaces face an interesting problem of optimizing for multiple stakeholder objectives.

In thistutorial, we consider a number ofresearch problems which need to be address when developing a recommendation framework powering a multi-stakeholder marketplace. We begin by contrasting traditional recommendations systems with those needed for marketplaces, and identify four key research areas which need to be addressed. First, we highlight the importance of a multi-objective ranking/recommendation module which jointly optimizes the different objectives of stakeholders while serving recommendations. Second, we discuss diferent ways in which stakeholders specify their objectives, and highlight key issues faced when quantifying such objectives. Third, we discuss user specifc characteristics (e.g. user receptivity) which could be leveraged while jointly optimizing business metrics with user satisfaction metrics. Furthermore, we highlight important research questions to be addressed around evaluation of such systems. Finally, we end the tutorial by discussing various diferent case studies and highlight recent fndings.

Outline of the tutorial

1. Introduction
  - (Quick) Overview of traditional RecSys approaches
  - Introduction to Marketplace
  - Types & examples of marketplaces
  - Recommendation in a marketplace

2. Optimization Objectives in a Marketplace
  - Industrial Use-cases (I - VII)
    - Stakeholders & their objectives
  - Families of objectives
  - Interplay between Objectives
    - Correlation analysis
    - Supporting vs Competing objectives

3. Methods for Multi-Objective Ranking & Recommendations
  - Pareto optimality
  - Multi-objective models
    - Scalarization: Vectorial objectives to single objective
    - Multi-objective (constraint) optimization
    - Multi-task Learning
    - Multi-objective gradient descent
    - Multi-objective bandits
    - Multi-objective RL
  - Search & Recommendation applications

4. Matching Consumers to Suppliers
  - Bi-directional matching
  - Extracting Consumer Characteristics
  - User affinity models 

5. Multi-sided evaluation
  - Simulation based evaluation 
  - Offline & online A/B evaluation techniques 
  - Counterfactual evaluation 
  - Trade-of analytics 

6. Conclusion
  - Open Research questions


Parts 1: link to slides (TBA)

Part 2: link to slides (TBA)

Part 3: link to slides (TBA)

Parts 4 & 5: link to slides (TBA)