Personalized ranking methods are at the core of many systems that learn to produce recommendations from user feedbacks. Their primary objective is to identify relevant items from very large vocabularies and to assist users in discovering new content. These ...
The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly ...
Artificial intelligence (AI) and machine learning (ML) have become de facto tools in many real-life applications to offer a wide range of benefits for individuals and our society. A classic ML model is typically trained with a large-scale static dataset in ...
Recommenders personalize the web content using collaborative filtering to relate users (or items). This work proposes to unify user-based, item-based and neural word embeddings types of recommenders under a single abstraction for their input, we name Consu ...
Social media (SM) platforms have demonstrated their ability to facilitate knowledge sharing on the global scale. They are increasingly often employed in educational and humanitarian domains where, despite their general benefits, they expose challenges pecu ...
Modern technologies enable us to record sequences of online user activity at an unprecedented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating-prediction paradigm, ignoring temporal ...
In the last decade, online social networks have enabled people to interact in many ways with each other and with content. The digital traces of such actions reveal people's preferences towards online content such as news or products. These traces often res ...
The rapidly increasing amount of user-generated content in social tagging systems provides a huge source of information. Yet, performing effective search in these systems is very challenging, especially when we seek the most appropriate items that match a ...
This article presents P4Q, a fully decentralized gossip-based protocol to personalize query processing in social tagging systems. P4Q dynamically associates each user with social acquaintances sharing similar tagging behaviors. Queries are gossiped among s ...
Environmental engineers from different organizations work in interdisciplinary projects having the need of information exchange. In particular, a collaborative environment with personalized access to information is needed, which supports strongly varying i ...
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