Work

Responsible RecSys by Design: Approximation Algorithms for Calibrated Recommendations with Sponsored Items

Year: 2025

Type: conference-paper

Abstract: Calibrated Recommendation Systems (CRS) balance user preferences with constraints like diversity, fairness, and novelty to create inclusive recommendation lists. However, existing research often overlooks the mandatory inclusion of sponsored items, assuming unrestricted product selection. In practice, sponsored items, paid for by advertisers, must be included, which can conflict with CRS goals when advertisers' priorities misalign with system objectives. This paper addresses this gap by formulating CRS with sponsored items as a combinatorial optimization problem. We develop efficient approximation algorithms to generate the most calibrated recommendation lists while meeting sponsorship requirements.

Source: Proceedings of the International AAAI Conference on Web and Social Media

DOI: 10.1609/icwsm.v19i1.35928

Authors: Jing Yuan, Shaojie Tang, Shuzhang Cai, Yao Wang

Institutions: University of North Texas, University at Buffalo, State University of New York, The University of Texas at Dallas, Xi'an Jiaotong University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4411120644

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