Beyond personalization: Research directions in multistakeholder recommendation
Recommender systems are personalized information access applications; they are
ubiquitous in today's online environment, and effective at finding items that meet user needs …
ubiquitous in today's online environment, and effective at finding items that meet user needs …
Fast greedy map inference for determinantal point process to improve recommendation diversity
The determinantal point process (DPP) is an elegant probabilistic model of repulsion with
applications in various machine learning tasks including summarization and search …
applications in various machine learning tasks including summarization and search …
[HTML][HTML] Futures of artificial intelligence through technology readiness levels
Artificial Intelligence (AI) offers the potential to transform our lives in radical ways. However,
the main unanswered questions about this foreseen transformation are its depth, breadth …
the main unanswered questions about this foreseen transformation are its depth, breadth …
Recommender systems in industry: A netflix case study
Recommender Systems are a prime example of the mainstream industry use of large-scale
machine learning and data mining. Diverse applications in areas such as e-commerce …
machine learning and data mining. Diverse applications in areas such as e-commerce …
Personalization in text information retrieval: A survey
Personalization of information retrieval (PIR) is aimed at tailoring a search toward individual
users and user groups by taking account of additional information about users besides their …
users and user groups by taking account of additional information about users besides their …
Past, present, and future of recommender systems: An industry perspective
When the Netflix Prize launched in 2006, it put a spotlight on the importance and use of
recommender systems in real-world applications. The competition provided many lessons …
recommender systems in real-world applications. The competition provided many lessons …
Adaptive, personalized diversity for visual discovery
Search queries are appropriate when users have explicit intent, but they perform poorly
when the intent is difficult to express or if the user is simply looking to be inspired. Visual …
when the intent is difficult to express or if the user is simply looking to be inspired. Visual …
Learning to rank an assortment of products
KJ Ferreira, S Parthasarathy… - Management Science, 2022 - pubsonline.informs.org
We consider the product-ranking challenge that online retailers face when their customers
typically behave as “window shoppers.” They form an impression of the assortment after …
typically behave as “window shoppers.” They form an impression of the assortment after …
Recommending personalized news in short user sessions
News organizations employ personalized recommenders to target news articles to specific
readers and thus foster engagement. Existing approaches rely on extensive user profiles …
readers and thus foster engagement. Existing approaches rely on extensive user profiles …
Adaptive collaborative topic modeling for online recommendation
Collaborative filtering (CF) mainly suffers from rating sparsity and from the cold-start
problem. Auxiliary information like texts and images has been leveraged to alleviate these …
problem. Auxiliary information like texts and images has been leveraged to alleviate these …