Artificial intelligence in E-Commerce: a bibliometric study and literature review
This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes
guidelines on how information systems (IS) research could contribute to this research …
guidelines on how information systems (IS) research could contribute to this research …
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 …
Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems
Recommendation systems are often challenged by the existence of cold-start items for which
no previous rating is available. The standard content-based or collaborative-filtering …
no previous rating is available. The standard content-based or collaborative-filtering …
Deep learning based recommender system using cross convolutional filters
With the recent development of online transactions, recommender systems have
increasingly attracted attention in various domains. The recommender system supports the …
increasingly attracted attention in various domains. The recommender system supports the …
[HTML][HTML] Hybrid recommendation by incorporating the sentiment of product reviews
Hybrid recommender systems utilize advanced algorithms capable of learning
heterogeneous sources of data and generating personalized recommendations for users …
heterogeneous sources of data and generating personalized recommendations for users …
[HTML][HTML] A novel model based collaborative filtering recommender system via truncated ULV decomposition
F Horasan, AH Yurttakal, S Gündüz - … of King Saud University-Computer and …, 2023 - Elsevier
Collaborative filtering is a technique that takes into account the common characteristics of
users and items in recommender systems. Matrix decompositions are one of the most used …
users and items in recommender systems. Matrix decompositions are one of the most used …
[HTML][HTML] Does metaverse improve recommendations quality and customer trust? A user-centric evaluation framework based on the cognitive-affective-behavioural …
Recommendation agents (RAs) have proven to be effective decision-making tools for
customers, as they can boost trust and loyalty when customers shop online. They can …
customers, as they can boost trust and loyalty when customers shop online. They can …
Configurational patterns for COVID-19 related social media rumor refutation effectiveness enhancement based on machine learning and fsQCA
Z Li, Y Zhao, T Duan, J Dai - Information Processing & Management, 2023 - Elsevier
Infodemics are intertwined with the COVID-19 pandemic, affecting people's perception and
social order. To curb the spread of COVID-19 related false rumors, fuzzy-set qualitative …
social order. To curb the spread of COVID-19 related false rumors, fuzzy-set qualitative …
Modelling electricity consumption during the COVID19 pandemic: Datasets, models, results and a research agenda
The COVID19 pandemic has impacted the global economy, social activities, and Electricity
Consumption (EC), affecting the performance of historical data-based Electricity Load …
Consumption (EC), affecting the performance of historical data-based Electricity Load …
A hybrid recommender system for health supplement e-commerce based on customer data implicit ratings
P Keikhosrokiani, GM Fye - Multimedia Tools and Applications, 2024 - Springer
The personalized product preference and decision-making recommendation systems are
highly demanded to handle big data and to increase service quality of the e-commerce …
highly demanded to handle big data and to increase service quality of the e-commerce …