Two-phase layered learning recommendation via category structure

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

Research output: Contribution to journalConference articlepeer-review

7 Citations (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.

Original languageEnglish
Pages (from-to)13-24
Number of pages12
JournalLecture Notes in Computer Science
Volume8444 LNAI
Issue numberPART 2
Publication statusPublished - 2014
Externally publishedYes
Event18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China
Duration: 13 May 201416 May 2014


  • Collaborative filtering
  • Layered Learning
  • Matrix Factorization
  • Recommender Systems


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