Abstract
The technological evolutions have promoted mobile devices from rudimentary communication facilities to advanced personal assistants. According to the huge amount of accessible data, developing a time-saving and cost-effective method for location-based recommendations in mobile devices has been considered a challenging issue. This paper contributes a state-of-the-art solution for a personalized recommender assistant which suggests both accurate and unexpected point of interests (POIs) to users in each part of the day of the week based on their previously monitored, daily behavioral patterns. The presented approach consists of two steps of extracting the behavioral patterns from users’ trajectories and location-based recommendation based on the discovered patterns and user’s ratings. The behavioral pattern of the user includes their activity types in different parts of the day of the week, which is monitored via a combination of a stay point detection algorithm and an association rule mining (ARM) method. Having the behavioral patterns, the system exploits two recommendation procedures based on conventional collaborative filtering and K-furthest neighborhood model to recommend typical and serendipitous POIs to the users. The suggested POI list contains not only relevant and precise POIs but also unpredictable and surprising items to the users. To evaluate the system, the values of RMSE of each procedure were computed and compared. Conducted experiments proved the feasibility of the proposed solution.
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References
Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17:734–749
Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: ACM sigmod record, Washington, DC, USA. ACM, pp 207–216
Cao H, Mamoulis N, Cheung DW (2007) Discovery of periodic patterns in spatiotemporal sequences. IEEE Trans Knowl Data Eng 19:453–467
Celdrán AH, Pérez MG, Clemente FJG, Pérez, GM (2016) Design of a recommender system based on users’ behavior and collaborative location and tracking. J Comput Sci 12
Choong MY, Chin RKY, Yeo KB, Tze Kin Teo K (2016) Trajectory pattern mining via clustering based on similarity function for transportation surveillance. Int J Simul Syst Sci Technol 17
de Gemmis M, Lops P, Semeraro G, Musto C (2015) An investigation on the serendipity problem in recommender systems. Inf Process Manage 51:695–717
Dodge S, Weibel R, Lautenschütz A-K (2008) Towards a taxonomy of movement patterns. Inf Visual 7:240–252
Etienne L, Devogele T, Bouju A (2012) Spatio-temporal trajectory analysis of mobile objects following the same itinerary. Adv Geo-Spatial Inf Sci 10:47–57
Fu Z, Hu W, Tan T (2005) Similarity based vehicle trajectory clustering and anomaly detection. In: IEEE international conference on image processing ICIP 2005. IEEE, pp II-602
Gao H, Tang J, Hu X, Liu H (2015) Content-aware point of interest recommendation on location-based social networks. In: AAAI, pp 1721–1727
Ge M, Delgado-Battenfeld C, Jannach D (2010) Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the fourth ACM conference on recommender systems, 2010 Barcelona, Spain. ACM, pp 257–260
Gudmundsson J, Laube P, Wolle T (2011) Computational movement analysis. In: Springer handbook of geographic information. Springer, Berlin
Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst (TOIS) 22:5–53
Iaquinta L, De Gemmis M, Lops P, Semeraro G, Filannino M, Molino P (2008) Introducing serendipity in a content-based recommender system. In: 2008 Eighth international conference on hybrid intelligent systems HIS’08, Barcelona, Spain. IEEE, pp 168–173
Li Q, Zheng Y, Xie X, Chen Y, Liu W, Ma W-Y (2008) Mining user similarity based on location history. In: 2008 Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems, Irvine, California. ACM, p 34
Li Z, Ding B, Han J, Kays R, Nye P (2010) Mining periodic behaviors for moving objects. In: 2010 Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC, USA. ACM, pp 1099–1108
Liu B, Fu Y, Yao Z, Xiong H (2013) Learning geographical preferences for point-of-interest recommendation. In: 2013 Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, Chicago, Illinois, USA. ACM, pp 1043–1051
Liu B, Xiong H (2013) Point-of-interest recommendation in location based social networks with topic and location awareness. In: Proceedings of the 2013 SIAM international conference on data mining, Austin, Texas, USA. SIAM, pp 396–404
Mamoulis N, Cao H, Kollios G, Hadjieleftheriou M, Tao Y, Cheung DW (2004) Mining, indexing, and querying historical spatiotemporal data. In: 2004 Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, WA, USA. ACM, pp 236–245
Mcnee SM, Riedl J, Konstan JA (2006) Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: 2006 CHI’06 extended abstracts on human factors in computing systems, Montréal, Québec, Canada. ACM, pp 1097–1101
Menk A, Sebastia L, Ferreira R (2017) Curumim: a serendipitous recommender system based on human curiosity. Procedia Comput Sci 112:484–493
Murakami T, Mori K, Orihara R (2007) Metrics for evaluating the serendipity of recommendation lists. In: Annual conference of the Japanese society for artificial intelligence, 2007. Springer, Berlin, pp 40–46
Palma AT, Bogorny V, Kuijpers B, Alvares LO (2008) A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM symposium on applied computing, Fortaleza, Ceara, Brazil. ACM, pp 863–868
Ricci F, Rokach L, Shapira B, Kantor PB (2015) Recommender systems handbook. Springer
Rocha JAM, Times VC, Oliveira G, Alvares LO, Bogorny V (2015) DB-SMoT: a direction-based spatio-temporal clustering method. In: 2010 5th IEEE international conference intelligent systems (IS), London, UK. IEEE, pp 114–119
Said A, Fields B, Jain BJ, Albayrak S (2013) User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm. In: Proceedings of the 2013 conference on computer supported cooperative work, San Antonio, Texas, USA. ACM, pp 1399–1408
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: 2001 Proceedings of the 10th international conference on World Wide Web, Hong Kong, Hong Kong. ACM, pp 285–295
Schreck T, Bernard J, von Landesberger T, Kohlhammer J (2009) Visual cluster analysis of trajectory data with interactive Kohonen maps. Inf Visual 8:14–29
Shani G, Gunawardana A (2011) Evaluating recommendation systems. In: Recommender systems handbook, pp 257–297
Wang J, De Vries AP, Reinders MJ (2006) Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: 2006 Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, Seattle, Washington, USA. ACM, pp 501–508
Yamaba H, Tanoue M, Takatsuka K, Okazaki N, Tomita S (2013) On a serendipity-oriented recommender system based on Folksonomy and its evaluation. Procedia Comput Sci 22:276–284
Yang D, Zhang D, Zheng VW, Yu Z (2015) Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans Syst Man Cybern Syst 45:129–142
Ye M, Yin P, Lee W-C, Lee D-L (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: 2011 Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval, Beijing, China. ACM, pp 325–334
Ying J-C, Chen H-S, Lin KW, Lu EH-C, Tseng VS, Tsai H-W, Cheng KH, Lin S-C (2014) Semantic trajectory-based high utility item recommendation system. Expert Syst Appl 41:4762–4776
Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation. In: 2013 Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval, Dublin, Ireland. ACM, pp 363–372
Zheng Y, Zhou X (2011) Computing with spatial trajectories. Springer Science & Business Media
Ziegler C-N, Mcnee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversification. In: 2005 Proceedings of the 14th international conference on World Wide Web, Chiba, Japan. ACM, pp 22–32
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Khoshahval, S., Farnaghi, M., Taleai, M., Mansourian, A. (2018). A Personalized Location-Based and Serendipity-Oriented Point of Interest Recommender Assistant Based on Behavioral Patterns. In: Mansourian, A., Pilesjö, P., Harrie, L., van Lammeren, R. (eds) Geospatial Technologies for All. AGILE 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-78208-9_14
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