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PUBLICATION

StreamMyRelevance! Prediction of Result Relevance from Real-Time Interactions and its Application to Hotel Search

Type

Conference Paper

Year

2014

Authors

speicher

Sebastian Nuck

Sebastian Nuck

Andreas Both

Andreas Both

gaedke

Research Area

Web Engineering

Event

14th International Conference on Web Engineering

Published in

Proceedings of 14th International Conference on Web Engineering (ICWE2014)

ISBN/ISSN

978-3-319-08244-8

Download

PDF

Abstract

The prime aspect of quality for search-driven web applications is to provide users with the best possible results for a given query. Thus, it is necessary to predict the relevance of results a priori. Current solutions mostly engage clicks on results for respective predictions, but research has shown that it is highly beneficial to also consider additional features of user interaction. Nowadays, such interactions are produced in steadily growing amounts by internet users. Processing these amounts calls for streaming-based approaches and incrementally updateable relevance models. We present StreamMyRelevance!--a novel streaming-based system for ensuring quality of ranking in search engines. Our approach provides a complete pipeline from collecting interactions in real-time to processing them incrementally on the server side. We conducted a large-scale evaluation with real-world data from the hotel search domain. Results show that our system yields predictions as good as those of competing state-of-the-art systems, but by design of the underlying framework at higher efficiency, robustness, and scalability.

Reference

Speicher, Maximilian; Nuck, Sebastian; Both, Andreas; Gaedke, Martin: StreamMyRelevance! Prediction of Result Relevance from Real-Time Interactions and its Application to Hotel Search. Proceedings of 14th International Conference on Web Engineering (ICWE2014), pp. 272-289, 2014.





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