| Vol-3549 urn:nbn:de:0074-3549-2 Copyright © 2023 for the individual papers by the papers' authors. Copyright © 2023 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# ORSUM 2023
6th Workshop on Online Recommender Systems and User Modeling 2023

### Proceedings of the 6th Workshop on Online Recommender Systems and User Modeling
co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023)

### Singapore, September 19th, 2023.

Edited by

### João Vinagre (1)
Marie Al-Ghossein (2)
Ladislav Peška (3)
Alípio Mário Jorge (4,5)
Albert Bifet (6,7)

(1) European Commission, Joint Research Centre, Seville, Spain
(2) Crossing Minds, Paris, France
(3) Charles University, Department of Software Engineering, Prague, Czechia
(4) University of Porto, Faculty of Sciences - Department of Computer Science, Porto, Portugal
(5) INESC TEC, Laboratory of Artificial Intelligence and Decision Support (LIAAD), Porto, Portugal
(6) University of Waikato, Te Ipu o te Mahara Institute, Hamilton, New Zealand
(7) Télécom Paris, Laboratoire Traitement et Communication de l’Information (LTCI), Paris, France

## Table of Contents

### Keynotes

- Sequential and Session-based Recommendation: Past, Present, Future
  Dietmar Jannach
- Wild Wild Tests
  Jacopo Tagliabue

### Contributed papers

- Deep Mutual Learning across Task Towers for Effective Multi-Task Recommender Learning
  Yi Ren, Ying Du, Bin Wang, Shenzheng Zhang
- TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback
  Yuxiang Qiu, Karim Djemili, Denis Elezi, Aaneel Shalman, María Pérez-Ortiz, Sahan Bulathwela
- Hierarchical Multi-Task Learning Framework for Session-based Recommendations
  Sejoon Oh, Walid Shalaby, Amir Afsharinejad, Xiquan Cui
- Quantifying Exploration Preference for E-Commerce Recommendation
  Amy B.Z. Zhang, Siyun Wang, Raphael Louca, Karl Ni, Diane Hu
- Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems
  Blaž Škrlj, Nir Ki-Tov, Lee Edelist, Natalia Silberstein, Hila Weisman-Zohar, Blaž Mramor, Davorin Kopič, Naama Ziporin

### Short Paper (Brainstorming Session)

- Next-basket Recommendation Constrained by Total Cost
  Denis Krasilnikov, Oleg Lashinin, Marina Ananyeva, Sergey Kolesnikov

2023-10-24: submitted by João Vinagre, metadata incl. bibliographic data published under Creative Commons CC0
2023-11-16: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|