PerK: personalized keyword search in relational databases through preferences

Keyword-based search in relational databases allows users to discover relevant information without knowing the database schema or using complicated queries. However, such searches may return an overwhelming number of results, often loosely related to the user intent. In this paper, we propose personalizing keyword database search by utilizing user preferences. Query results are ranked based on both their relevance to the query and their preference degree for the user. To further increase the quality of results, we consider two new metrics that evaluate the goodness of the result as a set, namely coverage of many user interests and content diversity. We present an algorithm for processing preference queries that uses the preferential order between keywords to direct the joining of relevant tuples from multiple relations. We then show how to reduce the complexity of this algorithm by sharing computational steps. Finally, we report evaluation results of the efficiency and effectiveness of our approach.

[1]  Werner Kießling,et al.  Foundations of Preferences in Database Systems , 2002, VLDB.

[2]  Moni Naor,et al.  Optimal aggregation algorithms for middleware , 2001, PODS '01.

[3]  Rakesh Agrawal,et al.  A framework for expressing and combining preferences , 2000, SIGMOD '00.

[4]  Georgia Koutrika,et al.  Personalized queries under a generalized preference model , 2005, 21st International Conference on Data Engineering (ICDE'05).

[5]  Nicolas Spyratos,et al.  Efficient Rewriting Algorithms for Preference Queries , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[6]  Liang Jeff Chen,et al.  Context-sensitive ranking , 2012 .

[7]  Evaggelia Pitoura,et al.  Fast contextual preference scoring of database tuples , 2008, EDBT '08.

[8]  Vikram Gadi,et al.  SPARK2: Top-k Keyword Query in Relational Databases , 2012 .

[9]  Sean M. McNee,et al.  Improving recommendation lists through topic diversification , 2005, WWW '05.

[10]  Vagelis Hristidis,et al.  DISCOVER: Keyword Search in Relational Databases , 2002, VLDB.

[11]  Mi Zhang,et al.  Avoiding monotony: improving the diversity of recommendation lists , 2008, RecSys '08.

[12]  Luis Gravano,et al.  Efficient IR-Style Keyword Search over Relational Databases , 2003, VLDB.

[13]  Georgia Koutrika,et al.  Précis: from unstructured keywords as queries to structured databases as answers , 2007, The VLDB Journal.

[14]  S. Sudarshan,et al.  Bidirectional Expansion For Keyword Search on Graph Databases , 2005, VLDB.

[15]  Erhan Erkut,et al.  A comparison of p-dispersion heuristics , 1994, Comput. Oper. Res..

[16]  Riccardo Torlone,et al.  Management of User Preferences in Data Intensive Applications , 2003, SEBD.

[17]  Evaggelia Pitoura,et al.  Adding Context to Preferences , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[18]  Kevin Chen-Chuan Chang,et al.  RankSQL: query algebra and optimization for relational top-k queries , 2005, SIGMOD '05.

[19]  Yi Chen,et al.  Structured Search Result Differentiation , 2009, Proc. VLDB Endow..

[20]  Surajit Chaudhuri,et al.  DBXplorer: a system for keyword-based search over relational databases , 2002, Proceedings 18th International Conference on Data Engineering.

[21]  Philip S. Yu,et al.  BLINKS: ranked keyword searches on graphs , 2007, SIGMOD '07.

[22]  Werner Kießling,et al.  Personalized Keyword Search with Partial-Order Preferences , 2002, SBBD.

[23]  Jan Chomicki,et al.  Preference formulas in relational queries , 2003, TODS.

[24]  Wolf-Tilo Balke,et al.  Multi-objective Query Processing for Database Systems , 2004, VLDB.

[25]  S. Sudarshan,et al.  Keyword searching and browsing in databases using BANKS , 2002, Proceedings 18th International Conference on Data Engineering.

[26]  Gerhard Weikum,et al.  Probabilistic information retrieval approach for ranking of database query results , 2006, TODS.

[27]  Clement T. Yu,et al.  Effective keyword search in relational databases , 2006, SIGMOD Conference.

[28]  Werner Kießling,et al.  Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications , 2003, PKDD.

[29]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[30]  Yoram Singer,et al.  Learning to Order Things , 1997, NIPS.

[31]  Vagelis Hristidis,et al.  ObjectRank: Authority-Based Keyword Search in Databases , 2004, VLDB.

[32]  Sihem Amer-Yahia,et al.  Efficient Computation of Diverse Query Results , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[33]  Cong Yu,et al.  It takes variety to make a world: diversification in recommender systems , 2009, EDBT '09.