Two-phase layered learning recommendation via category structure

Ke Ji, Hong Shen, Hui Tian, Yanbo Wu, Jun Wu

研究成果: Conference article同行評審

7 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)13-24
期刊Lecture Notes in Computer Science
8444 LNAI
發行號PART 2
出版狀態Published - 2014
事件18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China
持續時間: 13 5月 201416 5月 2014


深入研究「Two-phase layered learning recommendation via category structure」主題。共同形成了獨特的指紋。