PQMPMS: a preference-enabled querying mechanism for personalized mobile search

Authors:
Beibei Hu;Yves Vanrompay;Marie-Aude Aufaure
Affiliations:
MAS Laboratory, Ecole Centrale Paris, France;MAS Laboratory, Ecole Centrale Paris, France;MAS Laboratory, Ecole Centrale Paris, France
Venue:
RR'13 Proceedings of the 7th international conference on Web Reasoning and Rule Systems
Year:
2013

Citing 5
Cited 0

What Do You Prefer? Using Preferences to Enhance Learning Technology

IEEE Transactions on Learning Technologies
Time is of the essence: improving recency ranking using Twitter data

Proceedings of the 19th international conference on World wide web
Querying RDF streams with C-SPARQL

ACM SIGMOD Record
Querying the semantic web with preferences

ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Incremental reasoning on streams and rich background knowledge

ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

A key challenge for personalized mobile search is to tailor the answers to the specific user by considering her contextual situation. To adapt the retrieved items to user's context, this paper presents a preference-enabled querying mechanism for personalized mobile search. By exploiting the user's dialogue history, we infer the weighted user preferences and interests. To further compute personalized answers, we aim to continuously collect the ratings given by the user's friends regarding relevant topics from stream-based data sources such as Twitter. An experiment shows that our approach allows to compute the most relevant answers, providing an increased quality of search experience for the user.