Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review
Cold Start problems in recommender systems pose various challenges in the adoption and
use of recommender systems, especially for new item uptake and new user engagement …
use of recommender systems, especially for new item uptake and new user engagement …
Optimized recommendations by user profiling using apriori algorithm
Collaborative filtering has been the most straightforward and most preferable approach in
the recommender systems. This technique recommends an item to a target user from the …
the recommender systems. This technique recommends an item to a target user from the …
Two new collaborative filtering approaches to solve the sparsity problem
Collaborative filtering which is the most successful technique of the Recommender System,
has recently attracted great attention, especially in the field of e-commerce. CF is used to …
has recently attracted great attention, especially in the field of e-commerce. CF is used to …
Collaborative Tag-Aware Graph Neural Network for Long-Tail Service Recommendation
Long-tail service recommendation provides an unexpected but reasonable experience for
potential developers when they construct mashups. However, the lack of available …
potential developers when they construct mashups. However, the lack of available …
LBCF: A link-based collaborative filtering for overfitting problem in recommender system
Recommender system (RS) suggests relevant objects to generate personalized service and
minimize information overload issue. User-based collaborative filtering (UBCF) plays a …
minimize information overload issue. User-based collaborative filtering (UBCF) plays a …
A systematic review of movie recommender systems
Y Ariyanto, T Widiyaningtyas - ITEGAM-JETIA, 2024 - itegam-jetia.org
Recommender systems are vital to everyone's information selection. Managing massive
amounts of data is common with recommendation system technology. Annual film releases …
amounts of data is common with recommendation system technology. Annual film releases …
Context-enhanced probabilistic diffusion for urban point-of-interest recommendation
Point-of-interest (POI) recommendation has a wide range of application values in smart city
services computing. However, extreme sparsity of user-POI matrix seriously affects the …
services computing. However, extreme sparsity of user-POI matrix seriously affects the …
Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems
KV Rodpysh, SJ Mirabedini, T Banirostam - Electronic Commerce …, 2023 - Springer
The recommender system's primary purpose is to estimate the user's desire and provide a
list of items predicted from the appropriate information. Also, context-aware recommendation …
list of items predicted from the appropriate information. Also, context-aware recommendation …
Model-driven approach running route two-level SVD with context information and feature entities in recommender system
KV Rodpysh, SJ Mirabedini, T Banirostam - Computer Standards & …, 2022 - Elsevier
In digital businesses, the offers of goods and services to users in recommendation systems
are generally based on the features of the items and the demands of the users …
are generally based on the features of the items and the demands of the users …
Exploiting Rating Prediction Certainty for Recommendation Formulation in Collaborative Filtering
D Margaris, K Sgardelis, D Spiliotopoulos… - Big Data and Cognitive …, 2024 - mdpi.com
Collaborative filtering is a popular recommender system (RecSys) method that produces
rating prediction values for products by combining the ratings that close users have already …
rating prediction values for products by combining the ratings that close users have already …