1.2k citations · 3.4k across the 52 of their papers we have counts for
10 papers · 1 filter
The Impact of Group Membership Bias on the Quality and Fairness of Exposure in Ranking
Ali Vardasbi, Maarten de Rijke, Fernando Diaz +1
When learning to rank from user interactions, search and recommender systems must address biases in user behavior to provide a high-quality ranking. One type of bias that has recen…
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…
Neural Networks for Information Retrieval
Tom Kenter, Alexey Borisov, Christophe Van Gysel +3
Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many approac…
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…
Learning to Attend, Copy, and Generate for Session-Based Query Suggestion
Mostafa Dehghani, Sascha Rothe, Enrique Alfonseca +1
Users try to articulate their complex information needs during search sessions by reformulating their queries. To make this process more effective, search engines provide related q…
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…