[HTML][HTML] How do context-aware artificial intelligence algorithms used in fitness recommender systems? A literature review and research agenda
P Venkatachalam, S Ray - International Journal of Information Management …, 2022 - Elsevier
Recommender Systems (RS) help the user in the decision-making process when there is a
problem of plenty or lack of information. The context-aware recommender systems (CARS) …
problem of plenty or lack of information. The context-aware recommender systems (CARS) …
[HTML][HTML] Shaping the future of sustainable energy through AI-enabled circular economy policies
MSS Danish, T Senjyu - Circular Economy, 2023 - Elsevier
The energy sector is enduring a momentous transformation with new technological
advancements and increasing demand leading to innovative pathways. Artificial intelligence …
advancements and increasing demand leading to innovative pathways. Artificial intelligence …
A deep reinforcement learning based long-term recommender system
L Huang, M Fu, F Li, H Qu, Y Liu, W Chen - Knowledge-based systems, 2021 - Elsevier
Recommender systems aim to maximize the overall accuracy for long-term
recommendations. However, most of the existing recommendation models adopt a static …
recommendations. However, most of the existing recommendation models adopt a static …
RBPR: A hybrid model for the new user cold start problem in recommender systems
J Feng, Z Xia, X Feng, J Peng - Knowledge-Based Systems, 2021 - Elsevier
The recommender systems aim to predict potential demands of users by analyzing their
preferences and provide personalized recommendation services. User preferences can be …
preferences and provide personalized recommendation services. User preferences can be …
Matrix factorization in recommender systems: algorithms, applications, and peculiar challenges
FO Isinkaye - IETE Journal of Research, 2023 - Taylor & Francis
Traditional Collaborative filtering (CF) is one of the techniques of recommender systems that
has been successfully exploited in various applications, but sometimes they fail to provide …
has been successfully exploited in various applications, but sometimes they fail to provide …
Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learning
A commercially viable multi-stakeholder recommendation system maximizes the utility gain
by learning the personalized preferences of multiple stakeholders, such as consumers and …
by learning the personalized preferences of multiple stakeholders, such as consumers and …
Customer satisfaction of recommender system: Examining accuracy and diversity in several types of recommendation approaches
Information technology and the popularity of mobile devices allow for various types of
customer data, such as purchase history and behavior patterns, to be collected. As customer …
customer data, such as purchase history and behavior patterns, to be collected. As customer …
Privacy-aware factorization-based hybrid recommendation method for healthcare services
With the advancements of the Health 2.0 technology, large-scale healthcare services are
available online. Recommender systems for healthcare services have emerged for decision …
available online. Recommender systems for healthcare services have emerged for decision …
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 …
Personalized knowledge distillation for recommender system
Abstract Nowadays, Knowledge Distillation (KD) has been widely studied for recommender
system. KD is a model-independent strategy that generates a small but powerful student …
system. KD is a model-independent strategy that generates a small but powerful student …