Accepted RecSys Papers & RecSys Bias Workshop at the ECIR2022 Conference
The 44th European Conference on Information Retrieval (ECIR) is starting in a few days, and there will be lots of interesting presentations relating to recommender systems.
- Recommendation of Compatible Outfits Conditioned on Style. Debopriyo Banerjee, Lucky Dhakad, Harsh Maheshwari, Muthusamy Chelliah, Niloy Ganguly and Arnab Bhattacharya
- Influence-based Deep Network for Next POIs Prediction. Sayda Elmi and Kian-Lee Tan
- Joint Personalized Search and Recommendation with Hypergraph Convolutional Networks. Thibaut Thonet, Jean-Michel Renders, Mario Choi and Jinho Kim.
- Local Citation Recommendation with Hierarchical-Attention Text Encoder and SciBERT-based Reranking. Nianlong Gu, Yingqiang Gao and Richard H.R. Hahnloser
- [IRJ] Kernel Density Estimation based Factored Relevance Model for Multi-Contextual Point-of-Interest Recommendation. Anirban Chakraborty, Debasis Ganguly, Annalina Caputo, Gareth J. F. Jones
- Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations. Ludovico Boratto, Gianni Fenu, Mirko Marras and Giacomo Medda
- Revisiting Popularity and Demographic Biases in Recommender Evaluation and Effectiveness. Nicola Neophytou, Bhaskar Mitra and Catherine Stinson
- Do Lessons from Metric Learning Generalize to Image-Caption Retrieval? Maurits Bleeker and Maarten de Rijke
Also, the Third International Workshop on Algorithmic Bias in Search and Recommendation (BIAS@ECIR2022) will take place at ECIR’22.
ACM/IEEE Joint Conference on Digital Libraries (JCDL) 2022 has a RecSys Track
More than a decade of the ACM RecSys Conference [Alan Said, 2017]
ACM SAC 2022 with “Track on Recommender Systems: Theory, User Interactions & Applications”
About The Author
Joeran Beel
I am the founder of Recommender-Systems.com and head of the Intelligent Systems Group (ISG) at the University of Siegen, Germany https://isg.beel.org. We conduct research in recommender-systems (RecSys), personalization and information retrieval (IR) as well as on automated machine learning (AutoML), meta-learning and algorithm selection. Domains we are particularly interested in include smart places, eHealth, manufacturing (industry 4.0), mobility, visual computing, and digital libraries. We founded or maintain, among others, LensKit-Auto, Darwin & Goliath, Mr. DLib, and Docear, each with thousand of users; we contributed to TensorFlow, JabRef and others; and we developed the first prototypes of automated recommender systems (AutoSurprise and Auto-CaseRec) and Federated Meta Learning (FMLearn Server and Client).