TY - GEN
T1 - A constrained optimization approach for calibrated recommendations
AU - Seymen, Sinan
AU - Abdollahpouri, Himan
AU - Malthouse, Edward C.
N1 - Publisher Copyright:
© 2021 Owner/Author.
PY - 2021/9/13
Y1 - 2021/9/13
N2 - In recommender systems (RS) it is important to ensure that the various (past) areas of interest of a user are reflected with their corresponding proportions in the recommendation lists. In other words, when a user has watched, say, 60 romance movies and 40 Comedy movies, then it is reasonable to expect the personalized list of recommended movies to contain about 60% romance and 40% comedy movies as well. This property is known as calibration, and it has recently received much attention in the RS community. Greedy heuristic approaches have been proposed to calibrate recommendations, and although they provide great improvements, they can result in inefficient solutions in that a better one can be missed because of the myopic nature of these algorithms. This paper addresses the calibration problem from a constrained optimization perspective and provides a model to combine both accuracy and calibration. Experimental results show that our approach outperforms the state-of-the-art heuristics for calibration in most cases on both accuracy of the recommendations and the level of calibrations the recommendation lists achieve. We give a small example to illustrate why the heuristic fails to find the optimal solution.
AB - In recommender systems (RS) it is important to ensure that the various (past) areas of interest of a user are reflected with their corresponding proportions in the recommendation lists. In other words, when a user has watched, say, 60 romance movies and 40 Comedy movies, then it is reasonable to expect the personalized list of recommended movies to contain about 60% romance and 40% comedy movies as well. This property is known as calibration, and it has recently received much attention in the RS community. Greedy heuristic approaches have been proposed to calibrate recommendations, and although they provide great improvements, they can result in inefficient solutions in that a better one can be missed because of the myopic nature of these algorithms. This paper addresses the calibration problem from a constrained optimization perspective and provides a model to combine both accuracy and calibration. Experimental results show that our approach outperforms the state-of-the-art heuristics for calibration in most cases on both accuracy of the recommendations and the level of calibrations the recommendation lists achieve. We give a small example to illustrate why the heuristic fails to find the optimal solution.
KW - Calibration
KW - Multi-objective
KW - Optimization
KW - Recommender systems
UR - http://www.scopus.com/inward/record.url?scp=85115617154&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85115617154&partnerID=8YFLogxK
U2 - 10.1145/3460231.3478857
DO - 10.1145/3460231.3478857
M3 - Conference contribution
AN - SCOPUS:85115617154
T3 - RecSys 2021 - 15th ACM Conference on Recommender Systems
SP - 607
EP - 612
BT - RecSys 2021 - 15th ACM Conference on Recommender Systems
PB - Association for Computing Machinery, Inc
T2 - 15th ACM Conference on Recommender Systems, RecSys 2021
Y2 - 27 September 2021 through 1 October 2021
ER -