[HTML][HTML] Similarity measures for Collaborative Filtering-based Recommender Systems: Review and experimental comparison
Collaborative Filtering (CF) filters the flow of data that can be recommended, by a
Recommender System (RS), to a target user according to his taste and his preferences. The …
Recommender System (RS), to a target user according to his taste and his preferences. The …
Recent developments in recommender systems: A survey
In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …
comprehensively summarized. The objective of this study is to provide an overview of the …
Presentation of a recommender system with ensemble learning and graph embedding: a case on MovieLens
Abstract Information technology has spread widely, and extraordinarily large amounts of
data have been made accessible to users, which has made it challenging to select data that …
data have been made accessible to users, which has made it challenging to select data that …
Towards comprehensive approaches for the rating prediction phase in memory-based collaborative filtering recommender systems
LNH Nam - 2022 - dl.acm.org
Recommender systems play an indispensable role in today's online businesses. In these
systems, memory-based (neighborhood-based) collaborative filtering is an important …
systems, memory-based (neighborhood-based) collaborative filtering is an important …
Extending collaborative filtering recommendation using word embedding: A hybrid approach
Collaborative filtering recommendation systems, which analyze sets of user ratings, have
been applied to various domains and have resulted in considerable improvements in the …
been applied to various domains and have resulted in considerable improvements in the …
A social-aware Gaussian pre-trained model for effective cold-start recommendation
The use of pre-training is an emerging technique to enhance a neural model's performance,
which has been shown to be effective for many neural language models such as BERT. This …
which has been shown to be effective for many neural language models such as BERT. This …
Continuously evolving dropout with multi-objective evolutionary optimisation
Dropout is an effective method of mitigating over-fitting while training deep neural networks
(DNNs). This method consists of switching off (drop**) some of the neurons of the DNN …
(DNNs). This method consists of switching off (drop**) some of the neurons of the DNN …
A collaborative filtering recommender systems: Survey
In the current digital landscape, both information consumers and producers encounter
numerous challenges, underscoring the importance of recommender systems (RS) as a vital …
numerous challenges, underscoring the importance of recommender systems (RS) as a vital …
The state-of-the-art and challenges on recommendation system's: principle, techniques and evaluation strategy
In this digital era, users and service providers are facing various decisions that prompt data
over-burden. The choices should be separated and focused on or altered so that the actual …
over-burden. The choices should be separated and focused on or altered so that the actual …
A BP neural network based recommender framework with attention mechanism
Recently, some attempts have been made in introducing deep neural networks (DNNs) to
recommender systems for generating more accurate prediction due to the nonlinear …
recommender systems for generating more accurate prediction due to the nonlinear …