A review on individual and multistakeholder fairness in tourism recommender systems
A Banerjee, P Banik, W Wörndl - Frontiers in big Data, 2023 - frontiersin.org
The growing use of Recommender Systems (RS) across various industries, including e-
commerce, social media, news, travel, and tourism, has prompted researchers to examine …
commerce, social media, news, travel, and tourism, has prompted researchers to examine …
Estimating position bias without intrusive interventions
Presentation bias is one of the key challenges when learning from implicit feedback in
search engines, as it confounds the relevance signal. While it was recently shown how …
search engines, as it confounds the relevance signal. While it was recently shown how …
Off-policy evaluation for large action spaces via conjunct effect modeling
We study off-policy evaluation (OPE) of contextual bandit policies for large discrete action
spaces where conventional importance-weighting approaches suffer from excessive …
spaces where conventional importance-weighting approaches suffer from excessive …
Knowledge graphs: An information retrieval perspective
In this survey, we provide an overview of the literature on knowledge graphs (KGs) in the
context of information retrieval (IR). Modern IR systems can benefit from information …
context of information retrieval (IR). Modern IR systems can benefit from information …
Efficient and effective tree-based and neural learning to rank
As information retrieval researchers, we not only develop algorithmic solutions to hard
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …
problems, but we also insist on a proper, multifaceted evaluation of ideas. The literature on …
Offline evaluation to make decisions about playlistrecommendation algorithms
Evaluating algorithmic recommendations is an important, but difficult, problem. Evaluations
conducted offline using data collected from user interactions with an online system often …
conducted offline using data collected from user interactions with an online system often …
A survey of query auto completion in information retrieval
F Cai, M De Rijke - Foundations and Trends® in Information …, 2016 - nowpublishers.com
In information retrieval, query auto completion (QAC), also known as typeahead [Xiao et al.,
2013, Cai et al., 2014b] and auto-complete suggestion [Jain and Mishne, 2010], refers to the …
2013, Cai et al., 2014b] and auto-complete suggestion [Jain and Mishne, 2010], refers to the …
Neural information retrieval: At the end of the early years
KD Onal, Y Zhang, IS Altingovde, MM Rahman… - Information Retrieval …, 2018 - Springer
A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art
performance in many machine learning tasks, spanning speech recognition, computer …
performance in many machine learning tasks, spanning speech recognition, computer …
Social collaborative viewpoint regression with explainable recommendations
A recommendation is called explainable if it not only predicts a numerical rating for an item,
but also generates explanations for users' preferences. Most existing methods for …
but also generates explanations for users' preferences. Most existing methods for …
Entity-duet neural ranking: Understanding the role of knowledge graph semantics in neural information retrieval
This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces
knowledge graphs to neural search systems. EDRM represents queries and documents by …
knowledge graphs to neural search systems. EDRM represents queries and documents by …