Search structures and algorithms for personalized ranking

Gae won You, Seung won Hwang

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, while web search engines traditionally retrieve the same results for all users, they began to offer beta services to personalize the results to adapt to user-specific contexts such as prior search history or other application contexts. In a clear contrast to search engines dealing with unstructured text data, this paper studies how to enable such personalization in the context of structured data retrieval. In particular, we adopt contextual ranking model to formalize personalization as a cost-based optimization over collected contextual rankings. With this formalism, personalization can be abstracted as a cost-optimal retrieval of contextual ranking, closely matching user-specific retrieval context. With the retrieved matching context, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. Our empirical evaluations over synthetic and real-life data validate both the efficiency and effectiveness of our framework.

Original languageEnglish
Pages (from-to)3925-3942
Number of pages18
JournalInformation sciences
Issue number20
Publication statusPublished - 2008 Oct 15

Bibliographical note

Funding Information:
This work is supported by the Korean Research Foundation Granted funded by the Korean Government (MOEHRD, Basic Promotion Fund; KRF-2007-331-D00377).

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


Dive into the research topics of 'Search structures and algorithms for personalized ranking'. Together they form a unique fingerprint.

Cite this