With the gigantic increment in portable applications use and the spread of positioning and location-aware technologies that we are seeing today, new procedures and methodologies for location-based strategies are required. Location recommendation is one of the highly demanded location-aware applications uniquely with the wide accessibility of social network applications that are location-aware including Facebook check-ins, Foursquare, and others. In this paper, we aim to present a new methodology for location recommendation. The proposed approach coordinates customary spatial traits alongside other essential components including shortest distance, and user interests. We also present another idea namely, "activity trajectory" that represents trajectory that fulfills the set of activities that the user is intrigued to do. The approach dispatched acquaints the related distance value to select trajectory(ies) with minimum cost value (distance) and spatial-area to prune unneeded directions. The proposed calculation utilizes the idea of movement direction to prescribe most comparable N-trajectory(ies) that matches the client's required action design with least voyaging separation. To upgrade the execution of the proposed approach, parallel handling is applied through the employment of a MapReduce based approach. Experiments taking into account genuine information sets were built up and tested for assessing the proposed approach. The exhibited tests indicate how the proposed approach beets different strategies giving better precision and run time.
[1] Lee, F. Gregory, MaoYe and Wang-Chien, "Location recommendation for out-of-town users in location-based social networks," in The 22nd ACM international Conference on information & knowledge management, San Francisco, 2013.
[2] N. Li and G. Chen, "Analysis of a location-based social network," in Computational Science and Engineering, 2009.
[3] G. Adomavicius, B. Mobasher, F. Ricci and A. Tuzhilin, "Context-aware recommender systems," in Recommender systems handbook, Springer US, 2011, pp. 217-253.
[4] M. Prem and V. Sindhwani, "Recommender systems 2011.," in Encyclopedia of machine learning., US, Springer, 2011, pp. 829-838.
[5] J. B. Schafer, D. Frankowski, J. Herlocker and S. Sen, "Collaborative filtering recommender systems," in Foundations and Trends in Human-Computer Interaction, ACM, 2011, pp. 81-173.
[6] H. Gao, J. Tang, X. Hu and H. Liu, "Content-aware point of interest recommendation on location-based social networks," in 15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
[7] C. Hongbo, C. Zhiming, M. Arefin Shamsul and Y. Morimoto, "Place recommendation from check-in spots on location-based online social networks," in Networking and Computing (ICNC), 2012 Third International Conference on, 2012.
[8] H. Yin, Y. Sun, B. Cui, Z. Hu and L. Chen, "LCARS: a location-content-aware recommender system," in Proceedings of the 19th ACM SIGKDD international conferenceon Knowledge discovery and data mining, 2013.
[9] H. Wang, M. Terrovitis and N. Mamoulis, "Location recommendation in location-based social networks using user check-in data," in Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems., 2013.
[10] Noulas, S. Scellato, N. Lathia and C. Mascolo, "A random walk around the city: New venue recommendation in location-based social networks."," in 2012 International Conference on and 2012 International Confernece on Social Computing, 2012.
[11] S. Shang, R. Ding, B. Yuan, K. Xie, K. Zheng and P. Kalnis, "User oriented trajectory search for trip recommendation," in Proceedings of the 15th International Conference on Extending Database Technology, 2012.
[12] M. Sarwat, "LARS*: An efficient and scalable location-aware recommender system," in Transactions on Knowledge and Data Engineering, vol. 26.6, IEEE, 2014, pp. 1384-1399.
[13] Z. Chen, H. T. Shen, X. Zhou, Y. Zheng and X. Xie, "Searching trajectories by locations: an efficiency study," in Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010.
[14] J. Bao, D. Wilkie, Y. Z. Zheng and M. Mokbel, "Recommendations in location-based social networks: a survey," GEOINFORMATICA, pp. 525-565., 2015.
[15] (Online). Available: https://foursquare.com.
[16] (Online). Available: www.facebook.com.
[17] M. Sarwat, J. Bao, A. Eldawy, J. J. Levandoski, A. Magdy and M. F. Mokbel, "Sindbad: A Location-based Social Networking System," in Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, 2012.
[18] K. Zheng. S. Shang, N. Jing and Y. Y, "Towards efficient search for activity trajectories," in 29th International Conference In Data Engineering (ICDE), 2013.
[19] R. M. Feitosa, S. Labidi, L. S. A. dos Santos and N. Santos, "Social Recommendation in Location-Based Social Network using Text Mining," in 4th International Conference on Intelligent Systems, Modelling and Simulation, 2013.
[20] Ma, H. Lu, L. Shou and G. Chen, "KSQ: Top-k similarity query on uncertain trajectories" in IEEE Transactions on Knowledge and Data Engineering, 2013.
[21] L.-A. Tang, Y. Zheng, X. Xie, J. Yuan, X. Yu and J. Han, "Retrieving k-nearest neighboring trajectories by a set of point locations," International Symposium on Spatial and Temporal Databases, 2011.
[22] e. a. Monreale, "Where will I go next?: Predicting Future Categorical Check-ins in location-based Social Networks," in In Collaborative Computing: Networking, Applications and Worksharing , 2012.
[23] H. Su, K. Zheng and K. Zeng, "STMaker–A System to Make Sense of Trajectory Data," in Proceedings of the VLDB Endowment, Chicago, 2014.
[24] K. Zheng, Y. Zheng, J. Yuan and S. Shang, "On discovery of gathering patterns from trajectories.," in 2013 IEEE 29th International Conference in Data Engineering (ICDE), 2013.
[25] Y. Zheng, Z. Chen and X. Xie, "Searching Similar Trajectories by Locations". US Patent 12/794,538, 8 December 2011.
[26] Y. Gui-juna and Z. Ji-xianb, "A Dynamic Index Structure for Spatial Database Querying Based on R-Trees," in Proceedings of International Symposium on Spatio-temporal Modeling, Spatial Reasoning, Analysis, Data Mining and Data Fusion, 2005.
[27] J. Bao, Y. Zheng, D. Wilkie and M. F. Mokbel, "A Survey on Recommendations in Location-based Social Networks," ACM Transaction on Intelligent Systems and Technology., 2013.
[28] M. Sridevi, R. R. Rao and M. V. Rao, "A Survey on Recommender System," International Journal of Computer Science and Information Security, vol. 5, no. 14, p. 265, 2016.
[29] Hongbo, C. Zhiming and M. S. Arefin, "Place Recommendation from Check-in Spots on," in Third International Conference on Networking and Computing, 2012.
[30] R. Yerva, F. Grosan, A. Tandrau and K. Aberer, "TripEneer: User-based Travel Plan Recommendation Application," in 7th International AAAI Conference on Weblogs and Social Media., 2013.
[31] ORACLE, "Oracle VM VirtualBox," Oracle, (Online). Available: https://www.virtualbox.org/wiki/Downloads.
[32] K.-H. Lee, Y.-J. Lee, H. Choi, Y. D. Chung and B. Moon, "Parallel data processing with MapReduce: a survey.," ACM SIGMOD, pp. 11-20, 2012.
[33] Stanford, "Stanford Network Analysis Project," Stanford University, (Online). Available: https://snap.stanford.edu/index.html.
[34] Cho, S. A. Myers and J. Leskovec, "Friendship and Mobility: User Movement in Location-Based Social Networks," in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2011.
[35] AMAZON, "Amazon Web Services," (Online). Available: https://aws.amazon.com/.
[1] Lee, F. Gregory, MaoYe and Wang-Chien, "Location recommendation for out-of-town users in location-based social networks," in The 22nd ACM international Conference on information & knowledge management, San Francisco, 2013.
[2] N. Li and G. Chen, "Analysis of a location-based social network," in Computational Science and Engineering, 2009.
[3] G. Adomavicius, B. Mobasher, F. Ricci and A. Tuzhilin, "Context-aware recommender systems," in Recommender systems handbook, Springer US, 2011, pp. 217-253.
[4] M. Prem and V. Sindhwani, "Recommender systems 2011.," in Encyclopedia of machine learning., US, Springer, 2011, pp. 829-838.
[5] J. B. Schafer, D. Frankowski, J. Herlocker and S. Sen, "Collaborative filtering recommender systems," in Foundations and Trends in Human-Computer Interaction, ACM, 2011, pp. 81-173.
[6] H. Gao, J. Tang, X. Hu and H. Liu, "Content-aware point of interest recommendation on location-based social networks," in 15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
[7] C. Hongbo, C. Zhiming, M. Arefin Shamsul and Y. Morimoto, "Place recommendation from check-in spots on location-based online social networks," in Networking and Computing (ICNC), 2012 Third International Conference on, 2012.
[8] H. Yin, Y. Sun, B. Cui, Z. Hu and L. Chen, "LCARS: a location-content-aware recommender system," in Proceedings of the 19th ACM SIGKDD international conferenceon Knowledge discovery and data mining, 2013.
[9] H. Wang, M. Terrovitis and N. Mamoulis, "Location recommendation in location-based social networks using user check-in data," in Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems., 2013.
[10] Noulas, S. Scellato, N. Lathia and C. Mascolo, "A random walk around the city: New venue recommendation in location-based social networks."," in 2012 International Conference on and 2012 International Confernece on Social Computing, 2012.
[11] S. Shang, R. Ding, B. Yuan, K. Xie, K. Zheng and P. Kalnis, "User oriented trajectory search for trip recommendation," in Proceedings of the 15th International Conference on Extending Database Technology, 2012.
[12] M. Sarwat, "LARS*: An efficient and scalable location-aware recommender system," in Transactions on Knowledge and Data Engineering, vol. 26.6, IEEE, 2014, pp. 1384-1399.
[13] Z. Chen, H. T. Shen, X. Zhou, Y. Zheng and X. Xie, "Searching trajectories by locations: an efficiency study," in Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010.
[14] J. Bao, D. Wilkie, Y. Z. Zheng and M. Mokbel, "Recommendations in location-based social networks: a survey," GEOINFORMATICA, pp. 525-565., 2015.
[15] (Online). Available: https://foursquare.com.
[16] (Online). Available: www.facebook.com.
[17] M. Sarwat, J. Bao, A. Eldawy, J. J. Levandoski, A. Magdy and M. F. Mokbel, "Sindbad: A Location-based Social Networking System," in Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, 2012.
[18] K. Zheng. S. Shang, N. Jing and Y. Y, "Towards efficient search for activity trajectories," in 29th International Conference In Data Engineering (ICDE), 2013.
[19] R. M. Feitosa, S. Labidi, L. S. A. dos Santos and N. Santos, "Social Recommendation in Location-Based Social Network using Text Mining," in 4th International Conference on Intelligent Systems, Modelling and Simulation, 2013.
[20] Ma, H. Lu, L. Shou and G. Chen, "KSQ: Top-k similarity query on uncertain trajectories" in IEEE Transactions on Knowledge and Data Engineering, 2013.
[21] L.-A. Tang, Y. Zheng, X. Xie, J. Yuan, X. Yu and J. Han, "Retrieving k-nearest neighboring trajectories by a set of point locations," International Symposium on Spatial and Temporal Databases, 2011.
[22] e. a. Monreale, "Where will I go next?: Predicting Future Categorical Check-ins in location-based Social Networks," in In Collaborative Computing: Networking, Applications and Worksharing , 2012.
[23] H. Su, K. Zheng and K. Zeng, "STMaker–A System to Make Sense of Trajectory Data," in Proceedings of the VLDB Endowment, Chicago, 2014.
[24] K. Zheng, Y. Zheng, J. Yuan and S. Shang, "On discovery of gathering patterns from trajectories.," in 2013 IEEE 29th International Conference in Data Engineering (ICDE), 2013.
[25] Y. Zheng, Z. Chen and X. Xie, "Searching Similar Trajectories by Locations". US Patent 12/794,538, 8 December 2011.
[26] Y. Gui-juna and Z. Ji-xianb, "A Dynamic Index Structure for Spatial Database Querying Based on R-Trees," in Proceedings of International Symposium on Spatio-temporal Modeling, Spatial Reasoning, Analysis, Data Mining and Data Fusion, 2005.
[27] J. Bao, Y. Zheng, D. Wilkie and M. F. Mokbel, "A Survey on Recommendations in Location-based Social Networks," ACM Transaction on Intelligent Systems and Technology., 2013.
[28] M. Sridevi, R. R. Rao and M. V. Rao, "A Survey on Recommender System," International Journal of Computer Science and Information Security, vol. 5, no. 14, p. 265, 2016.
[29] Hongbo, C. Zhiming and M. S. Arefin, "Place Recommendation from Check-in Spots on," in Third International Conference on Networking and Computing, 2012.
[30] R. Yerva, F. Grosan, A. Tandrau and K. Aberer, "TripEneer: User-based Travel Plan Recommendation Application," in 7th International AAAI Conference on Weblogs and Social Media., 2013.
[31] ORACLE, "Oracle VM VirtualBox," Oracle, (Online). Available: https://www.virtualbox.org/wiki/Downloads.
[32] K.-H. Lee, Y.-J. Lee, H. Choi, Y. D. Chung and B. Moon, "Parallel data processing with MapReduce: a survey.," ACM SIGMOD, pp. 11-20, 2012.
[33] Stanford, "Stanford Network Analysis Project," Stanford University, (Online). Available: https://snap.stanford.edu/index.html.
[34] Cho, S. A. Myers and J. Leskovec, "Friendship and Mobility: User Movement in Location-Based Social Networks," in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2011.
[35] AMAZON, "Amazon Web Services," (Online). Available: https://aws.amazon.com/.
@article{"International Journal of Information, Control and Computer Sciences:74347", author = "Mohamed Mahmoud Hasan and Hoda M. O. Mokhtar", title = "An Activity Based Trajectory Search Approach", abstract = "With the gigantic increment in portable applications use and the spread of positioning and location-aware technologies that we are seeing today, new procedures and methodologies for location-based strategies are required. Location recommendation is one of the highly demanded location-aware applications uniquely with the wide accessibility of social network applications that are location-aware including Facebook check-ins, Foursquare, and others. In this paper, we aim to present a new methodology for location recommendation. The proposed approach coordinates customary spatial traits alongside other essential components including shortest distance, and user interests. We also present another idea namely, "activity trajectory" that represents trajectory that fulfills the set of activities that the user is intrigued to do. The approach dispatched acquaints the related distance value to select trajectory(ies) with minimum cost value (distance) and spatial-area to prune unneeded directions. The proposed calculation utilizes the idea of movement direction to prescribe most comparable N-trajectory(ies) that matches the client's required action design with least voyaging separation. To upgrade the execution of the proposed approach, parallel handling is applied through the employment of a MapReduce based approach. Experiments taking into account genuine information sets were built up and tested for assessing the proposed approach. The exhibited tests indicate how the proposed approach beets different strategies giving better precision and run time.
", keywords = "Location-based recommendation, map-reduce, recommendation system, trajectory search. ", volume = "10", number = "12", pages = "2032-6", }
