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A Novel Approach for Making Recommendation using Skyline Query based on user Location and Preference


  • Department of Information Technology, CSPIT, CHARUSAT, Anand - 388421, Gujarat, India


Objectives: To propose a method to handle large number of user and to improve the accuracy and quality of recommendation system. Methods/Statistical Analysis: This paper presents an effective method to identify user location based on his/her preference using Skyline query outline Dominated object. Dominance object suggests that an object falls under good or better in all dimension or good at least one dimension. Skyline query using Recommendation system has increased in recent years. Skyline query using recommendation system mainly used location-based services to find the nearest location, based on user preference. Location-based Services are information services and have a number of uses in social networking. Location-based Service finds the nearest location based on user preferences but not provide location based on similarity and rating. So, the user is not satisfied by the given result. Findings: To resolve above problem, the collaborative filtering technique, K-nearest neighbor algorithm and Ranking Scheme being used by us. Using Collaborative filtering technique, we find the similarity and rating of an item. Using K-nearest neighbor approach finds the nearest distance of the similar item and ranking technique being used by us, to choose the most nearest location. In this paper we take temporary dataset and mathematically evaluate our proposed system. Application/Improvements: In future, we will develop web tool which identify location and display result on map. We will also check user s' past movement history based on content based recommendation system. Skyline query using recommendation system is use various domain i.e. House Rent/buying, travel and tourism business.


Collaborative Filtering Technique, Dominated Object, K-Nearest Neighbor, Recommendation System, Skyline Query.

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  • Fayyad U, Piatetsky-shapiro G, Smyth P. From data mining to knowledge discovery. American Association for Artificial Intelligence. 1996; 17(3):1–18.
  • Roddick JF, Spiliopoulou M. A bibliography of temporal, spatial and spatio-temporal data mining research. ACM SIGKDD Explorations Newsletter. 1999 Jun; 1(1):34–8.
  • Wang H, Xing C. An approach to online recommendation of products with high price-performance ratios based on a customized price-dominance relationship. J Softw. 2013 Dec; 8(12):1–6.
  • Borzsony S, Kossmann D, Stocker K. The Skyline operator. Proceedings 17th Int Conf Data Eng. 2001. p. 421–30.
  • Chomicki J, Godfrey P, Gryz J, Liang D. Skyline with presorting. Proceedings - Int Conf Data Eng. 2003 Mar. p. 717–9.
  • El Maarry K, Lo C, Balke W. Crowdsourcing for Query Processing on Web Data : A Case Study on the Skyline Operator. Journal of Computing and Information Technology. 2015 Mar; 23(1):43–60.
  • Kodama K. Skyline Queries based on user Location and Preferences for Making Location-based Recommendations Preference-based Query Processing Multi-Level Skyline Query, DB Group Nagoya University, 2009 Nov.
  • Goncalves M, Torres D, Perera G. Making recommendations using location-based skyline queries. Proceedings Int Work Database Expert Syst Appl DEXA. 2012; 111–5.
  • Li Y, Qu W, Li Z, Xu Y, Ji C, Wu J. Skyline query based on user preference with mapreduce. 2014 IEEE 12th Int Conf Dependable, Auton Secur Comput. 2014 Aug. p. 153–8.
  • Ma Z, Xu Y, Sheng L, Li L. QBHSQ: A quad-tree based algorithm for high-dimension skyline query. 3rd Int Symp Intell Inf Technol Appl IITA. 2009 Nov; 1:593–6.
  • Lin Y, Wang ET, Chen ALP. Finding Targets with the Nearest Favor Neighbor and Farthest Disfavor Neighbor by a Skyline Query. SAC ‘14 Proceedings of the 29th Annual ACM Symposium on Applied Computing. 2014; 821–6.
  • Zhao L, Yang Y-Y, Zhou X. Continuous probabilistic subspace skyline query processing using grid projections. J Comput Sci Technol. 2014 Mar; 29(2):332–44.
  • Lim J, Lee Y, Bok K, Yoo J. A continuous reverse skyline query processing for moving objects. 2014 Int Conf Big Data Smart Comput BIGCOMP. 2014 Jan. p. 66–71 14. Papadias D, Fu G, Seeger B. An Optimal and Progressive Algorithm for Skyline Queries, SIGMOD ‘03 Proceedings of the 2003 ACM SIGMOD International Conference on Management of data. 2003. p. 467–78.
  • Guttman A. R-Trees: A Dynamic Index Structure For Spatial Searching. 84 Proc 1984 ACM SIGMOD Int Conf Manag data. 1984 Jun. p. 47–57.
  • Hou C-C, Chang C-K, Chen Y-C, Su H-Y, Hsu Y-L. Finding similar users in social networks by using the neural-based skyline region. J Comput. 2015; 10(5):292–9.
  • Sharifzadeh M, Shahabi C. The Spatial S kyline Queries. VLDB ‘06 Proceedings of the 32nd International Conference on Very Large Data Bases. 2006. p. 751–62.
  • Bilge A, Kaleli C. A multi-criteria item-based collaborative filtering framework. 2014 11th Int Jt Conf Comput Sci Softw Eng Human Factors Comput Sci Softw Eng - e-Science High Perform Comput eHPC, JCSSE. 2014 May. p. 18–22.
  • Huang YHY. An item based collaborative filtering using item clustering prediction. 2009 ISECS Int Colloq Comput Commun Control Manag. 2009 Aug; 4:54–6.
  • Ying JCB, Shi N, Tseng VS, Tsai HW, Cheng KH, Lin SC. Preference-aware community detection for item recommendation. Proceedings - 2013 Conf Technol Appl Artif Intell TAAI 2013. 2013 Dec. p. 49–54.
  • Kumar T. Solution of Linear and Non Linear Regression Problem by K Nearest Neighbour Approach: By using Three Sigma Rule. 2015 IEEE Int Conf Comput Intell Commun Technol. 2015 Feb. p. 197–201.
  • Yi X, Paulet R, Bertino E, Varadharajan V. Practical k nearest neighbor queries with location privacy. Icde’14. 2014 Mar -Apr; 640–51.
  • Xu W, Miranker DP, Mao R, Ramakrishnan S. Anytime K-nearest neighbor search for database applications. Proceedings - First Int Work Similarity Search Appl. SISAP 2008. 2008; 139–48.


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