X Wang, Z Peng, S Wang, PS Yu, W Fu… - Database Systems for …, 2018 - Springer
Abstract Traditional Collaborative Filtering (CF) models mainly focus on predicting a user's preference to the items in a single domain such as the movie domain or the music domain. A …
T Ma, J Zhou, M Tang, Y Tian… - IEICE transactions on …, 2015 - search.ieice.org
Recommender systems, which provide users with recommendations of content suited to their needs, have received great attention in today's online business world. However, most …
Recommender Systems deal with the issue of overloading information by retrieving the most relevant sources in the wide range of web services. They help users by predicting their …
Recommender system is an information filtering tool used to alleviate information overload for users on the web. Collaborative filtering recommends items to users based on their …
In this paper we tackle the problem of recommendation in the scenarios with binary relevance data, when only a few (k) items are recommended to individual users. Past work …
Z Zhang, M Dong, K Ota, Y Kudo - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
The recommender system (RS) can help us extract valuable data from a huge amount of raw information. User-based collaborative filtering (UBCF) is widely employed in practical RSs …
Abstract In the Big Data Era, recommender systems perform a fundamental role in data management and information filtering. In this context, Collaborative Filtering (CF) persists as …
X Ma, H Lu, Z Gan, J Zeng - Electronic Commerce Research and …, 2017 - Elsevier
Clustering based recommender systems have been demonstrated to be efficient and scalable to large-scale datasets. However, due to the employment of dimensionality …
J Zhong, X Li - Expert Systems with Applications, 2010 - Elsevier
Collaborative filtering (CF) has been studied extensively in the literature and is demonstrated successfully in many different types of personalized recommender systems. In …