Results 231 to 240 of about 228,630 (266)
Some of the next articles are maybe not open access.
Biases in Recommendation System
Fifteenth ACM Conference on Recommender Systems, 2021Recommendation systems shape what people consume and experience online, which makes it critical to assess their effect on society and whether they are affected by any potential source of bias. My research focuses on a specific source of bias — popularity — that is especially relevant in two online contexts: news consumption, and cultural markets ...
openaire +1 more source
Multimedia recommender systems
Proceedings of the 12th ACM Conference on Recommender Systems, 2018This tutorial introduces multimedia recommender systems (MMRS), in particular, recommender systems that leverage multimedia content to recommend different media types. In contrast to the still most frequently adopted collaborative filtering approaches, we focus on content-based MMRS and on hybrids of collaborative filtering and content-based filtering.
Yashar Deldjoo +3 more
openaire +1 more source
Asynchronous recommendation systems
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing, 2007We consider the following abstraction of recommendation systems. There are n players and m objects, and each player has an arbitrary binary preference grade (“likes” or “dislikes”) for each object. The problem is that these preferences are not known, and the goal of the players is to discover their own preferences.
Baruch Awerbuch +2 more
openaire +1 more source
The Review of Recommendation System
2019With the development of the Internet, the amount of information continues to increase, and the problem of “information overloading” is becoming more and more obvious. Simple information retrieval can no longer satisfies the needs of users to search for accurate information, and the recommendation system emerges.
Ning Wang, Hui Zhao, Xue Zhu, Nan Li
openaire +1 more source
Information Technology & Tourism, 2010
Mobile phones are becoming a primary platform for information access and when coupled with recommender systems technologies they can become key tools for mobile users both for leisure and business applications. Recommendation techniques can increase the usability of mobile systems providing personalized and more focussed content, hence limiting the ...
openaire +1 more source
Mobile phones are becoming a primary platform for information access and when coupled with recommender systems technologies they can become key tools for mobile users both for leisure and business applications. Recommendation techniques can increase the usability of mobile systems providing personalized and more focussed content, hence limiting the ...
openaire +1 more source
Introduction to recommender systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data, 2008Recommender systems help users find the information, products, and other people they most want to find. This tutorial provides participants with a hands-on learning experience about using recommender system technologies. After completing this tutorial, participants will understand the range of technologies being used for recommender systems, including ...
openaire +1 more source
A survey on personality-aware recommendation systems
Artificial Intelligence Review, 2021Mohammed Amine Bouras +2 more
exaly
Recommendation Systems for Education: Systematic Review
Electronics (Switzerland), 2021Maria Cora Urdaneta Ponte +2 more
exaly
Privacy in Recommender Systems
2018The upsurge in the number of web users over the last two decades has resulted in a significant growth of online information. Recommenders are machine learning approach and are becoming one of the main ways to navigate the Internet. They recommend appropriate items to users based on their clicks, i.e., likes, ratings, purchases, etc.
openaire +1 more source