Real-time user interest modeling for real-time ranking
| Autor: | Xiaozhong Liu, Howard R. Turtle |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Information retrieval
Basis (linear algebra) Computer Networks and Communications Computer science Information needs Ranking (information retrieval) Human-Computer Interaction Search engine Ranking Artificial Intelligence Social media Relevance (information retrieval) Language model Baseline (configuration management) Software Information Systems |
| Zdroj: | Journal of the American Society for Information Science and Technology. 64:1557-1576 |
| ISSN: | 1532-2882 |
| DOI: | 10.1002/asi.22862 |
| Popis: | User interest as a very dynamic information need is often ignored in most existing information retrieval systems. In this research, we present the results of experiments designed to evaluate the performance of a real-time interest model (RIM) that attempts to identify the dynamic and changing query level interests regarding social media outputs. Unlike most existing ranking methods, our ranking approach targets calculation of the probability that user interest in the content of the document is subject to very dynamic user interest change. We describe 2 formulations of the model (real-time interest vector space and real-time interest language model) stemming from classical relevance ranking methods and develop a novel methodology for evaluating the performance of RIM using Amazon Mechanical Turk to collect (interest-based) relevance judgments on a daily basis. Our results show that the model usually, although not always, performs better than baseline results obtained from commercial web search engines. We identify factors that affect RIM performance and outline plans for future research. |
| Databáze: | OpenAIRE |
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