All Outputs (2)

Integrating selection-based aspect sentiment and preference knowledge for social recommender systems. (2019)
Journal Article
CHEN, Y.Y., WIRATUNGA, N. and LOTHIAN, R. 2020. Integrating selection-based aspect sentiment and preference knowledge for social recommender systems. Online information review [online], 44(2), pages 399-416. Available from: https://doi.org/10.1108/OIR-02-2017-0066

Purpose: Recommender system approaches such as collaborative and content-based filtering rely on user ratings and product descriptions to recommend products. More recently, recommender system research has focussed on exploiting knowledge from user-ge... Read More about Integrating selection-based aspect sentiment and preference knowledge for social recommender systems..

Aspect-based sentiment analysis for social recommender systems. (2019)
Thesis
CHEN, Y.Y. 2019. Aspect-based sentiment analysis for social recommender systems. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

Social recommender systems harness knowledge from social content, experiences and interactions to provide recommendations to users. The retrieval and ranking of products, using similarity knowledge, is central to the recommendation architecture. To e... Read More about Aspect-based sentiment analysis for social recommender systems..