Session-based recommendation systems are used in environments where system recommendation actions are interleaved with user choice reactions. Domains include radio-style song recommendation, session-aware related-items in a shopping context, and next video recommendation. In many situations, interactions logged from a production policy can be used to train and evaluate such session-based recommendation systems. This paper presents several concerns with interpreting logged interactions as reflecting user preferences and provides possible mitigation to those concerns.
bibtex
Copied!
@inproceedings{diaz:sbrs,
year = {2021},
title = {On Evaluating Session-Based Recommendation with Implicit Feedback},
booktitle = {Perspectives on the Evaluation of Recommender Systems Workshop (PERSPECTIVES 2021), September 25th, 2021, co-located with the 15th ACM Conference on Recommender Systems, Amsterdam, The Netherlands},
author = {Fernando Diaz}
}