activity
20172020
most citedEffects of Foraging in Personalized Content-based Image Recommendation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

8 papers

cs.IR2020

Reinforcement Learning-driven Information Seeking: A Quantum Probabilistic Approach

Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz

Understanding an information forager's actions during interaction is very important for the study of interactive information retrieval. Although information spread in uncertain inf…

cs.IR2020

ECIR 2020 Workshops: Assessing the Impact of Going Online

Sérgio Nunes, Suzanne Little, Sumit Bhatia +11

ECIR 2020 https://ecir2020.org/ was one of the many conferences affected by the COVID-19 pandemic. The Conference Chairs decided to keep the initially planned dates (April 14-17, 2…

cs.IR2020

Bibliometric-enhanced Information Retrieval 10th Anniversary Workshop Edition

Guillaume Cabanac, Ingo Frommholz, Philipp Mayr

The Bibliometric-enhanced Information Retrieval workshop series (BIR) was launched at ECIR in 2014 \cite{MayrEtAl2014} and it was held at ECIR each year since then. This year we or…

cs.IR2020

Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation

Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz

User implicit feedback plays an important role in recommender systems. However, finding implicit features is a tedious task. This paper aims to identify users' preferences through…

cs.IR2019

Report on the 8th International Workshop on Bibliometric-enhanced Information Retrieval (BIR 2019)

Guillaume Cabanac, Ingo Frommholz, Philipp Mayr

The Bibliometric-enhanced Information Retrieval workshop series (BIR) at ECIR tackled issues related to academic search, at the crossroads between Information Retrieval and Bibliom…

cs.IR20191 cited

Effects of Foraging in Personalized Content-based Image Recommendation

Amit Kumar Jaiswal, Haiming Liu, Ingo Frommholz

A major challenge of recommender systems is to help users locating interesting items. Personalized recommender systems have become very popular as they attempt to predetermine the…