[HTML][HTML] A/B testing: A systematic literature review
A/B testing, also referred to as online controlled experimentation or continuous
experimentation, is a form of hypothesis testing where two variants of a piece of software are …
experimentation, is a form of hypothesis testing where two variants of a piece of software are …
Cookiepocalypse: What are the most effective strategies for advertisers to reshape the future of display advertising?
A third-party cookie is a tracking code set by a third-party platform or a technology provider
on the website the user is visiting. This small amount of text identifies users online, reveals …
on the website the user is visiting. This small amount of text identifies users online, reveals …
Towards the evaluation of recommender systems with impressions
In Recommender Systems, impressions are a relatively new type of information that records
all products previously shown to the users. They are also a complex source of information …
all products previously shown to the users. They are also a complex source of information …
Multi-granularity Fatigue in Recommendation
Personalized recommendation aims to provide appropriate items according to user
preferences mainly from their behaviors. Excessive homogeneous user behaviors on similar …
preferences mainly from their behaviors. Excessive homogeneous user behaviors on similar …
Characterizing impression-aware recommender systems
Impression-aware recommender systems (IARS) are a type of recommenders that learn user
preferences using their interactions and the recommendations (also known as impressions) …
preferences using their interactions and the recommendations (also known as impressions) …
Incorporating impressions to graph-based recommenders
Graph-based approaches have become an effective strategy to model the users'
preferences in recommender systems accurately; however, despite their excellent …
preferences in recommender systems accurately; however, despite their excellent …
Modeling User Fatigue for Sequential Recommendation
Recommender systems filter out information that meets user interests. However, users may
be tired of the recommendations that are too similar to the content they have been exposed …
be tired of the recommendations that are too similar to the content they have been exposed …
Impression-Aware Recommender Systems
Novel data sources bring new opportunities to improve the quality of recommender systems.
Impressions are a novel data source containing past recommendations (shown items) and …
Impressions are a novel data source containing past recommendations (shown items) and …
Workshop on Learning and Evaluating Recommendations with Impressions (LERI)
Recommender systems typically rely on past user interactions as the primary source of
information for making predictions. However, although highly informative, past user …
information for making predictions. However, although highly informative, past user …
Ad close mitigation for improved user experience in native advertisements
Verizon Media native advertising (also known as Yahoo Gemini native) serves billions of ad
impressions daily, reaching several hundreds of millions USD in revenue yearly. Although …
impressions daily, reaching several hundreds of millions USD in revenue yearly. Although …