Real time personalized search on social networks

Abstract

Abstract-Internet 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.

On main-memory flushing in microblogs data management systems

“…Our fo cus is supporting basic search queries on mi croblogs [5,16,19,28]. Such queries retrieve individual microblogs that are associated with certain key value(s), e.g., keywords or user IDs.…”

“…In that case, a query COining to any of the nine keywords kwl to kw9 will be fully answered from in-memory contents, which significantly increases the system memory hit ratio. kFlushing enables existing algorithms for top-k microblog search queries (e.g., [5,6,16,24,19,28]) to reach to their full potential and significantly increasing their memory hit ratio. different terminologies, e.g., buffer management in database management systems (DBMSs) [9], anti-caching in main memory databases [8,15,30], and load shedding in data stream management systems (DSMSs) [1, 12,13].…”

“…Existing work for (top-k) search queries on microblogs [5,16,19,28] mainly focus on building scalable indexing tech niques in main-memory to digest incoming microblogs with their high arrival rates. Existing index structures along with their query processing techniques either explicitly or implicitly assume the following two assumptions: (1) Memory is so large that almost all queries of interest will be answered from in memory contents.…”

“…Such important usage of microblogs has motivated researchers to spend maj or efforts to efficiently support search queries for large numbers of microblogs. Search queries on microblogs include keyword search queries "Find microblogs that contain certain key word(s)" [5,6,16], location search queries "Find microblogs that are posted within a certain location" [19,24], and user timeline search queries "Find microblogs that are posted by a certain user" [28] . Due to the large number of returned results for any of these search queries, all proposed techniques agree This research is capitally supported by NSF grants I1 S-0952977, I1 S-1218168, IIS-1525953, CNS-1512877, Amazon Web Service grant, and the University of Minnesota Doctoral Dissertation Fellowship.…”

Developing of the Related Data Search Lsa-Based Algorithm and Its Programmed Realization

“…The authors of the article [3] investigated the problem that social networks have got two mutually exclusive characteristics which prevent search algorithms simultaneously from support of them. To solve this problem, the authors developed a framework that provides K-query in real time.…”