TY - JOUR
T1 - CAESAR
T2 - context-aware explanation based on supervised attention for service recommendations
AU - Li, Lei
AU - Chen, Li
AU - Dong, Ruihai
N1 - Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2021/8
Y1 - 2021/8
N2 - Explainable recommendations have drawn more attention from both academia and industry recently, because they can help users better understand recommendations (i.e., why some particular items are recommended), therefore improving the persuasiveness of the recommender system and users’ satisfaction. However, little work has been done to provide explanations from the angle of a user’s contextual situations (e.g., companion, season, and destination if the recommendation is a hotel). To fill this research gap, we propose a new context-aware recommendation algorithm based on supervised attention mechanism (CAESAR), which particularly matches latent features to explicit contextual features as mined from user-generated reviews for producing context-aware explanations. Experimental results on two large datasets in hotel and restaurant service domains demonstrate that our model improves recommendation performance against the state-of-the-art methods and furthermore is able to return feature-level explanations that can adapt to the target user’s current contexts.
AB - Explainable recommendations have drawn more attention from both academia and industry recently, because they can help users better understand recommendations (i.e., why some particular items are recommended), therefore improving the persuasiveness of the recommender system and users’ satisfaction. However, little work has been done to provide explanations from the angle of a user’s contextual situations (e.g., companion, season, and destination if the recommendation is a hotel). To fill this research gap, we propose a new context-aware recommendation algorithm based on supervised attention mechanism (CAESAR), which particularly matches latent features to explicit contextual features as mined from user-generated reviews for producing context-aware explanations. Experimental results on two large datasets in hotel and restaurant service domains demonstrate that our model improves recommendation performance against the state-of-the-art methods and furthermore is able to return feature-level explanations that can adapt to the target user’s current contexts.
KW - Context-aware recommender systems
KW - Explainable recommendation
KW - Multi-task learning
KW - Neural network
UR - http://www.scopus.com/inward/record.url?scp=85096525581&partnerID=8YFLogxK
U2 - 10.1007/s10844-020-00631-8
DO - 10.1007/s10844-020-00631-8
M3 - Journal article
AN - SCOPUS:85096525581
SN - 0925-9902
VL - 57
SP - 147
EP - 170
JO - Journal of Intelligent Information Systems
JF - Journal of Intelligent Information Systems
IS - 1
ER -