38 citations · 146 across the 18 of their papers we have counts for
13 papers · 1 filter
Learning to Rank from Samples of Variable Quality
Mostafa Dehghani, Jaap Kamps
Training deep neural networks requires many training samples, but in practice, training labels are expensive to obtain and may be of varying quality, as some may be from trusted ex…
On Search Powered Navigation
Mostafa Dehghani, Glorianna Jagfeld, Hosein Azarbonyad +3
Query-based searching and browsing-based navigation are the two main components of exploratory search. Search lets users dig in deep by controlling their actions to focus on and fi…
Finding Talk About the Past in the Discourse of Non-Historians
Alex Olieman, Kaspar Beelen, Jaap Kamps
A heightened interest in the presence of the past has given rise to the new field of memory studies, but there is a lack of search and research tools to support studying how and wh…
Good Applications for Crummy Entity Linkers? The Case of Corpus Selection in Digital Humanities
Alex Olieman, Kaspar Beelen, Milan van Lange +2
Over the last decade we have made great progress in entity linking (EL) systems, but performance may vary depending on the context and, arguably, there are even principled limitati…
Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking
Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps +1
Deep neural networks have become a primary tool for solving problems in many fields. They are also used for addressing information retrieval problems and show strong performance in…
Neural Ranking Models with Weak Supervision
Mostafa Dehghani, Hamed Zamani, Aliaksei Severyn +2
Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for info…
Hierarchical Re-estimation of Topic Models for Measuring Topical Diversity
Hosein Azarbonyad, Mostafa Dehghani, Tom Kenter +3
A high degree of topical diversity is often considered to be an important characteristic of interesting text documents. A recent proposal for measuring topical diversity identifies…
On Horizontal and Vertical Separation in Hierarchical Text Classification
Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps +1
Hierarchy is a common and effective way of organizing data and representing their relationships at different levels of abstraction. However, hierarchical data dependencies cause di…
Generalized Group Profiling for Content Customization
Mostafa Dehghani, Hosein Azarbonyad, Jaap Kamps +1
There is an ongoing debate on personalization, adapting results to the unique user exploiting a user's personal history, versus customization, adapting results to a group profile s…
Beyond Movie Recommendations: Solving the Continuous Cold Start Problem in E-commerceRecommendations
Julia Kiseleva, Alexander Tuzhilin, Jaap Kamps +6
Many e-commerce websites use recommender systems or personalized rankers to personalize search results based on their previous interactions. However, a large fraction of users has…
The Impact of Technical Domain Expertise on Search Behavior and Task Outcome
Julia Kiseleva, Alejandro Montes García, Jaap Kamps +1
Domain expertise is regarded as one of the key factors impacting search success: experts are known to write more effective queries, to select the right results on the result page,…
LocLinkVis: A Geographic Information Retrieval-Based System for Large-Scale Exploratory Search
Alex Olieman, Jaap Kamps, Rosa Merino Claros
In this paper we present LocLinkVis (Locate-Link-Visualize); a system which supports exploratory information access to a document collection based on geo-referencing and visualizat…
The Continuous Cold Start Problem in e-Commerce Recommender Systems
Lucas Bernardi, Jaap Kamps, Julia Kiseleva +1
Many e-commerce websites use recommender systems to recommend items to users. When a user or item is new, the system may fail because not enough information is available on this us…