TY - JOUR
T1 - Two-phase layered learning recommendation via category structure
AU - Ji, Ke
AU - Shen, Hong
AU - Tian, Hui
AU - Wu, Yanbo
AU - Wu, Jun
PY - 2014
Y1 - 2014
N2 - Context and social network information have been introduced to improve recommendation systems. However, most existing work still models users' rating for every item directly. This approach has two disadvantages: high cost for handling large amount of items and unable to handle the dynamic update of items. Generally, items are classified into many categories. Items in the same category have similar/relevant content, and hence may attract users of the same interest. These characteristics determine that we can utilize the item's content similarity to overcome the difficultiess of large amount and dynamic update of items. In this paper, aiming at fusing the category structure, we propose a novel two-phase layered learning recommendation framework, which is matrix factorization approach and can be seen as a greedy layer-wise training: first learn user's average rating to every category, and then, based on this, learn more accurate estimates of user's rating for individual item with content and social relation ensembled. Based on two kinds of classifications, we design two layered gradient algorithms in our framework. Systematic experiments on real data demonstrate that our algorithms outperform other state-of-the-art methods, especially for recommending new items.
AB - Context and social network information have been introduced to improve recommendation systems. However, most existing work still models users' rating for every item directly. This approach has two disadvantages: high cost for handling large amount of items and unable to handle the dynamic update of items. Generally, items are classified into many categories. Items in the same category have similar/relevant content, and hence may attract users of the same interest. These characteristics determine that we can utilize the item's content similarity to overcome the difficultiess of large amount and dynamic update of items. In this paper, aiming at fusing the category structure, we propose a novel two-phase layered learning recommendation framework, which is matrix factorization approach and can be seen as a greedy layer-wise training: first learn user's average rating to every category, and then, based on this, learn more accurate estimates of user's rating for individual item with content and social relation ensembled. Based on two kinds of classifications, we design two layered gradient algorithms in our framework. Systematic experiments on real data demonstrate that our algorithms outperform other state-of-the-art methods, especially for recommending new items.
KW - Collaborative filtering
KW - Layered Learning
KW - Matrix Factorization
KW - Recommender Systems
UR - http://www.scopus.com/inward/record.url?scp=84901266644&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-06605-9_2
DO - 10.1007/978-3-319-06605-9_2
M3 - Conference article
AN - SCOPUS:84901266644
SN - 0302-9743
VL - 8444 LNAI
SP - 13
EP - 24
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
IS - PART 2
T2 - 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014
Y2 - 13 May 2014 through 16 May 2014
ER -