TY - JOUR
T1 - Sequence-based trust in collaborative filtering for document recommendation
AU - Liu, Duen-Ren
AU - Lai, Chin Hui
AU - Chiu, Hsuan
PY - 2011/8/1
Y1 - 2011/8/1
N2 - Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust values based on users past ratings on items. A user is more trustworthy if s/he has contributed more accurate predictions than other users. Nevertheless, conventional trust-based CF methods do not address the issue of deriving the trust values based on users various information needs on items over time. In knowledge-intensive environments, users usually have various information needs in accessing required documents over time, which forms a sequence of documents ordered according to their access time. We propose a sequence-based trust model to derive the trust values based on users sequences of ratings on documents. The model considers two factors time factor and document similarity in computing the trustworthiness of users. The proposed model enhanced with the similarity of user profiles is incorporated into a standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method in comparison with other trust-based recommender systems.
AB - Collaborative filtering (CF) recommender systems have emerged in various applications to support item recommendation, which solve the information-overload problem by suggesting items of interest to users. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. They propose trust computation models to derive the trust values based on users past ratings on items. A user is more trustworthy if s/he has contributed more accurate predictions than other users. Nevertheless, conventional trust-based CF methods do not address the issue of deriving the trust values based on users various information needs on items over time. In knowledge-intensive environments, users usually have various information needs in accessing required documents over time, which forms a sequence of documents ordered according to their access time. We propose a sequence-based trust model to derive the trust values based on users sequences of ratings on documents. The model considers two factors time factor and document similarity in computing the trustworthiness of users. The proposed model enhanced with the similarity of user profiles is incorporated into a standard collaborative filtering method to discover trustworthy neighbors for making predictions. The experiment result shows that the proposed model can improve the prediction accuracy of CF method in comparison with other trust-based recommender systems.
KW - Collaborative filtering
KW - Document recommendation
KW - Recommender system
KW - Sequence-based trust
UR - http://www.scopus.com/inward/record.url?scp=79959548871&partnerID=8YFLogxK
U2 - 10.1016/j.ijhcs.2011.06.001
DO - 10.1016/j.ijhcs.2011.06.001
M3 - Article
AN - SCOPUS:79959548871
SN - 1071-5819
VL - 69
SP - 587
EP - 601
JO - International Journal of Human Computer Studies
JF - International Journal of Human Computer Studies
IS - 9
ER -