A systematic literature review of sparsity issues in recommender systems

N Idrissi, A Zellou - Social Network Analysis and Mining, 2020 - Springer
The tremendous expansion of information available on the web voraciously bombards
users, leaving them unable to make decisions and having no way of stepping back to …

Robust collaborative filtering recommendation with user-item-trust records

F Wang, H Zhu, G Srivastava, S Li… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
The ever-increasing popularity of recommendation systems allows users to find appropriate
services without excessive effort. However, due to the unstable and complex network …

Combining review-based collaborative filtering and matrix factorization: A solution to rating's sparsity problem

R Duan, C Jiang, HK Jain - Decision Support Systems, 2022 - Elsevier
An important factor affecting the performance of collaborative filtering for recommendation
systems is the sparsity of the rating matrix caused by insufficient rating data. Improving the …

Fake review detection in e-Commerce platforms using aspect-based sentiment analysis

P Hajek, L Hikkerova, JM Sahut - Journal of Business Research, 2023 - Elsevier
Consumers rely on internet user reviews. Existing sentiment-based detection systems fail to
capture consumer feelings regarding numerous aspects of products or services which …

A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution

C Wangwatcharakul, S Wongthanavasu - Expert Systems with Applications, 2021 - Elsevier
Recommender systems are challenging research problems being exploited to suggest new
items or services, such as books, music and movies, and even people, to users based on …

A two-stage locality-sensitive hashing based approach for privacy-preserving mobile service recommendation in cross-platform edge environment

L Qi, X Zhang, W Dou, C Hu, C Yang, J Chen - Future Generation …, 2018 - Elsevier
With the increasing popularity of service computing paradigm, tremendous resources or
services are emerging rapidly on the Web, imposing heavy burdens on the service selection …

A distributed locality-sensitive hashing-based approach for cloud service recommendation from multi-source data

L Qi, X Zhang, W Dou, Q Ni - IEEE Journal on Selected Areas …, 2017 - ieeexplore.ieee.org
To maximize the economic benefits, a cloud service provider needs to recommend its
services to as many users as possible based on the historical user-service quality data …

[HTML][HTML] Exploring consumers' buying behavior in a large online promotion activity: The role of psychological distance and involvement

Q Liu, X Zhang, S Huang, L Zhang… - Journal of theoretical and …, 2020 - SciELO Chile
As a key marketing tool, online sales promotion has been widely used by online retailers to
increase sales of products and brands. Most previous researches on online sales promotion …

Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment

W Zhong, X Yin, X Zhang, S Li, W Dou, R Wang… - Computer …, 2020 - Elsevier
With the advance of mobile edge computing (MEC), the number of edge services running on
mobile devices grows explosively. In this situation, it is becoming a necessity to recommend …

Privacy‐preserving and scalable service recommendation based on SimHash in a distributed cloud environment

Y Xu, L Qi, W Dou, J Yu - Complexity, 2017 - Wiley Online Library
With the increasing volume of web services in the cloud environment, Collaborative Filtering‐
(CF‐) based service recommendation has become one of the most effective techniques to …