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Real time personalized search on social networks
conference contribution
posted on 2023-05-23, 11:08 authored by Li, Y, Bao, Z, Li, G, Tan, KLInternet users are shifting from searching on traditional media to social network platforms (SNPs) to retrieve up-to-date and valuable information. SNPs have two unique characteristics: frequent content update and small world phenomenon. However, existing works are not able to support these two features simultaneously. To address this problem, we develop a general framework to enable real time personalized top-k query. Our framework is based on a general ranking function that incorporates time freshness, social relevance and textual similarity. To ensure efficient update and query processing, there are two key challenges. The first is to design an index structure that is update-friendly while supporting instant query processing. The second is to efficiently compute the social relevance in a complex graph. To address these challenges, we first design a novel 3D cube inverted index to support efficient pruning on the three dimensions simultaneously. Then we devise a cube based threshold algorithm to retrieve the top-k results, and propose several pruning techniques to optimize the social distance computation, whose cost dominates the query processing. Furthermore, we optimize the 3D index via a hierarchical partition method to enhance our pruning on the social dimension. Extensive experimental results on two real world large datasets demonstrate the efficiency and the robustness of our proposed solution.
History
Publication title
Proceedings of the 31st IEEE International Conference on Data Engineering (ICDE 2015)Editors
Huang, JHPagination
639-650ISSN
1084-4627Publisher
IEEE Computer SocietyPlace of publication
United StatesEvent title
31st IEEE International Conference on Data Engineering 2015Event Venue
Seoul, South KoreaDate of Event (Start Date)
2015-04-13Date of Event (End Date)
2015-04-17Rights statement
Copyright 2015 IEEERepository Status
- Restricted