Bias and debias in recommender system: A survey and future directions
While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …
system (RS), most of the papers focus on inventing machine learning models to better fit …
User simulation for evaluating information access systems
With the emergence of various information access systems exhibiting increasing complexity,
there is a critical need for sound and scalable means of automatic evaluation. To address …
there is a critical need for sound and scalable means of automatic evaluation. To address …
Bias on the web
R Baeza-Yates - Communications of the ACM, 2018 - dl.acm.org
Bias on the web Page 1 54 COMMUNICATIONS OF THE ACM | JUNE 2018 | VOL. 61 | NO.
6 contributed articles IMA GE B Y SVIA TL ANA SHEINA OUR INHERENT HUMAN …
6 contributed articles IMA GE B Y SVIA TL ANA SHEINA OUR INHERENT HUMAN …
Equity of attention: Amortizing individual fairness in rankings
Rankings of people and items are at the heart of selection-making, match-making, and
recommender systems, ranging from employment sites to sharing economy platforms. As …
recommender systems, ranging from employment sites to sharing economy platforms. As …
Recommendations with negative feedback via pairwise deep reinforcement learning
Recommender systems play a crucial role in mitigating the problem of information overload
by suggesting users' personalized items or services. The vast majority of traditional …
by suggesting users' personalized items or services. The vast majority of traditional …
Position bias estimation for unbiased learning to rank in personal search
A well-known challenge in learning from click data is its inherent bias and most notably
position bias. Traditional click models aim to extract the‹ query, document› relevance and …
position bias. Traditional click models aim to extract the‹ query, document› relevance and …
Bias issues and solutions in recommender system: Tutorial on the recsys 2021
Recommender systems (RS) have demonstrated great success in information seeking.
Recent years have witnessed a large number of work on inventing recommendation models …
Recent years have witnessed a large number of work on inventing recommendation models …
Unbiased learning to rank with unbiased propensity estimation
Learning to rank with biased click data is a well-known challenge. A variety of methods has
been explored to debias click data for learning to rank such as click models, result …
been explored to debias click data for learning to rank such as click models, result …
Learning to rank with selection bias in personal search
Click-through data has proven to be a critical resource for improving search ranking quality.
Though a large amount of click data can be easily collected by search engines, various …
Though a large amount of click data can be easily collected by search engines, various …
[图书][B] Modern information retrieval
R Baeza-Yates, B Ribeiro-Neto - 1999 - people.ischool.berkeley.edu
Information retrieval (IR) has changed considerably in recent years with the expansion of the
World Wide Web and the advent of modern and inexpensive graphical user interfaces and …
World Wide Web and the advent of modern and inexpensive graphical user interfaces and …