TY - GEN
T1 - Infusing latent user-concerns from user reviews into collaborative filtering
AU - Pradhan, Ligaj
AU - Zhang, Chengcui
AU - Bethard, Steven
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/11/8
Y1 - 2017/11/8
N2 - Traditionally, Collaborative Filtering (CF) based recommendation employs past rating behaviors of users on items to discover similar users and similar items. We can further improve on discovering user similarities by better understanding user behaviors through analyzing user reviews. In their reviews, users generally mention about things that are of greater interest to them, and these cues can provide an effective medium to discover users with similar interests and concerns. In this paper, we extract latent User-Concerns from user reviews and construct their hierarchical tree (UC-Tree). By associating each user with the corresponding concerns in the UC-Tree, we then generate vectors that represent intricate user behaviors. Finally, we infuse such additional knowledge about the users into the conventional CF-based rating prediction process. Our experiments and results show that such additional behavioral knowledge assists the discovery of similar users and improves the accuracy of conventional CF-based rating prediction.
AB - Traditionally, Collaborative Filtering (CF) based recommendation employs past rating behaviors of users on items to discover similar users and similar items. We can further improve on discovering user similarities by better understanding user behaviors through analyzing user reviews. In their reviews, users generally mention about things that are of greater interest to them, and these cues can provide an effective medium to discover users with similar interests and concerns. In this paper, we extract latent User-Concerns from user reviews and construct their hierarchical tree (UC-Tree). By associating each user with the corresponding concerns in the UC-Tree, we then generate vectors that represent intricate user behaviors. Finally, we infuse such additional knowledge about the users into the conventional CF-based rating prediction process. Our experiments and results show that such additional behavioral knowledge assists the discovery of similar users and improves the accuracy of conventional CF-based rating prediction.
UR - http://www.scopus.com/inward/record.url?scp=85044235903&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044235903&partnerID=8YFLogxK
U2 - 10.1109/IRI.2017.24
DO - 10.1109/IRI.2017.24
M3 - Conference contribution
AN - SCOPUS:85044235903
T3 - Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
SP - 471
EP - 477
BT - Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017
A2 - Khan, Latifur
A2 - Palanisamy, Balaji
A2 - Zhang, Chengcui
A2 - Sarvestani, Sahra Sedigh
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE International Conference on Information Reuse and Integration, IRI 2017
Y2 - 4 August 2017 through 6 August 2017
ER -