Can I read Limits to Surprise in Recommender Systems on EtoBox?

Limits to Surprise in Recommender Systems by de Lima, Andre Paulino; Peres, Sarajane Marques is a scholarly article available to read on EtoBox.

What is Limits to Surprise in Recommender Systems about?

In this study, we address the challenge of measuring the ability of a recommender system to make surprising recommendations. Although current evaluation methods make it possible to determine if two algorithms can make recommendations with a significant difference in their average surprise measure, it could be of interest to our community to know how competent an algorithm is at embedding surprise in its recommendations, without having to resort to making a direct comparison with another algorithm. We argue that a) surprise is a finite resource in a recommender system, b) there is a limit to how much surprise any algorithm can embed in a recommendation, and c) this limit can provide us with a scale against which the performance of any algorithm can be measured. By exploring these ideas, it is possible to define the concepts of maximum and minimum potential surprise and design a surprise metric called "normalised surprise" that employs these limits to potential surprise. Two experiments were conducted to test the proposed metric. The aim of the first was to validate the quality of the estimates of minimum and maximum potential surprise produced by a greedy algorithm. The purpose of t

Author

de Lima, Andre Paulino; Peres, Sarajane Marques

Published

2018

Language

EN