| Vol-3303 urn:nbn:de:0074-3303-6 Copyright © 2022 for the individual papers by the papers' authors. Copyright © 2022 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 2022
5th Workshop on Online Recommender Systems and User Modeling 2022

### Proceedings of the 5th Workshop on Online Recommender Systems and User Modeling
co-located with the 16th ACM Conference on Recommender Systems (RecSys 2022)

### Seattle, WA, USA, September 23rd, 2022.

Edited by

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

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

## Table of Contents

### Keynotes

- Scientific Challenges, Practical Methodologies and Policy Perspectives for Trustworthy Artificial Intelligence
  Emilia Gómez
- Online Recommender Systems: Is the Juice Worth the Squeeze?
  Eugene Yan

### Contributed papers

- Page-Wise Personalized Recommendations in an Industrial e-Commerce Setting
  Liying Zheng, Yingji Pan, Yuri M. Brovman
- An Application of Causal Bandit to Content Optimization
  Sameer Kanase, Yan Zhao, Shenghe Xu, Mitchell Goodman, Manohar Mandalapu, Benjamyn Ward, Chan Jeon, Shreya Kamath, Ben Cohen, Yujia Liu, Hengjia Zhang, Yannick Kimmel, Saad Khan, Brent Payne, Patricia Grao
- Bundle Recommender from Recipes to Shopping Carts - Optimizing Ingredients, Kitchen Gadgets and their Quantities
  Chahak Sethi, Melvin Vellera, Diane Myung-kyung Woodbridge, Joey Jonghoon Ahnn
- Interaction-Grounded Learning for Recommender Systems
  Jessica Maghakian, Kishan Panaganti, Paul Mineiro, Akanksha Saran, Cheng Tan
- On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models
  Rohan Anil, Sandra Gadanho, Da Huang, Nijith Jacob, Zhuoshu Li, Dong Lin, Todd Phillips, Cristina Pop, Kevin Regan, Gil I. Shamir, Rakesh Shivanna, Qiqi Yan
- A Neighbourhood-based Location- and Time-aware Recommender System
  Len Feremans, Robin Verachtert, Bart Goethals
- Monolith: Real Time Recommendation System with Collisionless Embedding Table
  Zhuoran Liu, Leqi Zou, Xuan Zou, Caihua Wang, Biao Zhang, Da Tang, Bolin Zhu, Yijie Zhu, Peng Wu, Ke Wang, Youlong Cheng
- Extracting User Preferences and Personality from Text for Restaurant Recommendation
  Evripides Christodoulou, Andreas Gregoriades, Herodotos Herodotou, Maria Pampaka
- Towards Interaction-based User Embeddings in Sequential Recommender Models
  Marina Ananyeva, Oleg Lashinin, Veronika Ivanova, Sergey Kolesnikov, Dmitry I. Ignatov

### Short Paper (Brainstorming Session)

- Next-basket Recommendation Constrained by Total Cost
  Oleg Lashinin, Marina Ananyeva

2022-10-30: submitted by João Vinagre, metadata incl. bibliographic data published under Creative Commons CC0
2022-12-15: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|