[HTML][HTML] Boosting the item-based collaborative filtering model with novel similarity measures
Collaborative filtering (CF), one of the most widely employed methodologies for
recommender systems, has drawn undeniable attention due to its effectiveness and …
recommender systems, has drawn undeniable attention due to its effectiveness and …
Cross-domain recommendation for cold-start users via neighborhood based feature mapping
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 …
preference to the items in a single domain such as the movie domain or the music domain. A …
Social network and tag sources based augmenting collaborative recommender system
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 …
their needs, have received great attention in today's online business world. However, most …
An impact of time and item influencer in collaborative filtering recommendations using graph-based model
MK Najafabadi, A Mohamed, CW Onn - Information Processing & …, 2019 - Elsevier
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 …
relevant sources in the wide range of web services. They help users by predicting their …
A collaborative filtering approach based on Naïve Bayes classifier
P Valdiviezo-Diaz, F Ortega, E Cobos… - IEEE …, 2019 - ieeexplore.ieee.org
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 …
for users on the web. Collaborative filtering recommends items to users based on their …
Climf: learning to maximize reciprocal rank with collaborative less-is-more filtering
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 …
relevance data, when only a few (k) items are recommended to individual users. Past work …
Alleviating new user cold-start in user-based collaborative filtering via bipartite network
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 …
information. User-based collaborative filtering (UBCF) is widely employed in practical RSs …
Deep learning techniques for recommender systems based on collaborative filtering
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 …
management and information filtering. In this context, Collaborative Filtering (CF) persists as …
An explicit trust and distrust clustering based collaborative filtering recommendation approach
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 …
scalable to large-scale datasets. However, due to the employment of dimensionality …
Unified collaborative filtering model based on combination of latent features
Collaborative filtering (CF) has been studied extensively in the literature and is
demonstrated successfully in many different types of personalized recommender systems. In …
demonstrated successfully in many different types of personalized recommender systems. In …