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[17] Mao Q, Feng B, Pan S. A study of Top-N recommendation on user behavior data. in: IEEE International Conference on Computer Science and Automation Engineering; Zhangjiajie, China; 2012. pp. 582-586. doi: 10.1109/CSAE.2012.6272839
[18] Philip Z, Yi Z. Bayesian adaptive user pro ling with explicit & implicit feedback. In: ACM CIKM International Conference on Information and Knowledge Management, Arlington, Virginia, USA. 2006. pp. 397-404
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[23] Minakshi P, Anjana NG. Personalized recommender system using collaborative filtering technique and pyramid maintenance algorithm, International Journal of Computer Applications, 2016; 136(8): 25-31. doi: 10.5220/0006513202750282
[24] Tuan HD, Seung RJ, Hyunchul A. A novel recommendation model of location-based advertising: context-aware collaborative filtering using GA approach. Expert System with Applications, 2012; 39(3): 3731-3739. doi: 10.1016/j.eswa.2011.09.070
[25] Selcuk C, Abdulhamit S. Tourism demand modelling and forecasting using data mining techniques in multivariate time series: a case study in Turkey. Turkish Journal of Electrical Engineering & Computer Sciences, 2016; 24: 3388-3404. doi: 10.3906/elk-1311-134
[26] Francesco R, Lior R, Bracha S. Recommender Systems Handbook. New York, USA. Springer, 2011.
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[28] Jian W, Jianhua H, Kai C, Yi Zhou, Zuoyin T. Collaborative filtering and deep learning based recommendation system for cold start items. Expert System with Applications, 2017; 69: 29-39. doi: 10.1016/j.eswa.2016.09.040
[29] Dwi KSU. Item collaborative filtering untuk rekomendasi paket wisata pada franchise tour and travel (in Indonesian). Bachelor, Maulana Malik Ibrahim State Islam University, Yogyakarta, Indonesia, 2014.
[30] Gilda MD, Mehregan M. A new collaborative filtering algorithm using k-means clustering and neighbors ' voting. In: 11th International Conference on Hybrid Intelligent Systems (HIS); Melacca, Malaysia; 2011. pp 179-184.
[31] Georgios P, Xiangliang Z, Wei W. Clustering recommenders in collaborative. IFIP Internationl Federation for Information Processing, 2011; 358: 82-97. doi: 10.1007/978-0-387-85691-96
[32] Xiaonan G, Sen W. Hierarchical Clustering Algorithm for Binary Data Based on Cosine Similarity. In: 8th International Conference on Logistics, Informatics and Service Sciences (LISS); Toronto, Canada; 2018. pp. 1-6. Doi: 10.1109/LISS.2018.8593222
[33] Manzhao B,Shijian L,Ji H. A Fast Collaborative Filtering Algorithm for Implicit Binary Data. In: IEEE 10th International Conference on Computer-Aided Industrial Design & Conceptual Design; Wenzhou, China; 2009. pp. 973-976. Doi: 10.1109/CAIDCD.2009.5374935
[34] Widiarina. Klastering data menggunakan algoritma dynamic k-means (in Indonesian). Jurnal Teknik Komputer AMIK BSI, 2015; 1(2): 260-265. doi: 10.31294/jtk.v1i2.259
[35] Citradevi M, Geetharamani G. An analysis on the performance of kmeans clustering algorithm for cardiotogram data cluster. International Journal on Computational Sciences & Aplications, 2012; 5: 11-20. doi: 10.5121/ijcsa.2012.2502
[36] Zhengwu Y, Haiguang L. Location recommendation algorithm based on temporal and geographical similarity in location-based social networks. In: 12th World Congress on Intelligent Control and Automation (WCICA), Guilin, China, 2016. pp. 1697-1702
[37] Huayu L, Yong G, Richang H, Hengshu Z. Point of interest recommendations: learning potential check-ins from friends. In: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Fransisco, CA, USA, 2016. pp:975-984
[38] John SB, David H, Cael K. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedding of the 14th Annual Conference on Uncertainty in Artificial Intelligence, San Fransisco, USA, 1998. pp. 43-52
[39] Jia-Dong Z, Chi-Yin C. Point-of-interest recommendations in location-based social networks. SIGSPATIAL Special, 2013; 3: 26-33. doi: 10.1109/CC.2015.7385525
[40] Lei G, Haoran J, Xinhua W, Fangai L. Learning to recommend point-of-interest with the weighted bayesian personalized ranking method in LBSNs. Information, 2017; 8(20) : 1-19. doi: 10.3390/inf8010020
[2] Aysun B, Birgul K. Current State and Future Trends in Location Recommender Systems. I.J. Information Tech- 25 nology and Computer Science, 2017; 6: 1-8. doi: 10.5815/ijitcs.2017.06.01
[3] Sulis S, Teguh BA, Igi A. Context-based awareness in location recommendation system to enhance recommendation quality: a review. In: Intenational Conference on Information and Communications Technology; Yogyakarta, Indonesia; 2018. pp. 90-95.
[4] Fernando O, Antonio H, Jesus B, Jeon HK. Recommending items to group of users using matrix factorization based collaborative ltering. Information System. 2016; 345: 313-324. doi: 10.1016/j.ins.2016.01.083
[5] Lakshmi TP, Sreenivasa DP, Siva NN, Srikanth Y. Movie Recommender System Using Item Based Collaborative Filtering Technique. In: International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS); Pudukkottai, India; 2016. pp. 1-5
[6] Huayu L, Yong G, Defu L, Hao L. Learning user ' s intrinsic and extrinsic interests for point-of-interest recommendation: a unified approach. In: The Twenty-Sixth International Conference on Arti cial Intelligence (IJCAI-17); Melbourne, Australia; 2017. pp. 2117{2123.
[7] Yiding L, Tuan-Anh NP, Gao C, Quan Y. An experimental evaluation of point-of-interest recommendation in location-based social networks. In: Proceedings of the VLDB Endowment; 2017. pp. 1021-1021.
[8] Shanshan F, Xutao L, Yifeng Z, Gao C, Yeow MC et al. Personalized ranking metric embedding for next new poi recommendation. In: The Twenty-Fourth International Conference on Artificial Intelligence (IJCAI 2015); Buenos Aires, Argentina; 2015. pp. 2069-2075.
[9] Qilong B, Xiaoyong L, Zhongying B. Clustering collaborative ltering recommendation system based on svd algorithm. In: 4th IEEE International Conference on Software Engineering and Service Science (ICSESS); China; 2013. pp. 963-967.
[10] Fidan k, Gurel Y, Adnan K. A mobile and web application-based recommendation system using color quantization and collaborative ltering. Turkish Journal of Electrical Engineering & Computer Sciences, 2015; 23: 900-912. doi: 10.3906/elk-1212-145
[11] HaiHong E, JianFeng W, MeiNa S, Qiang B, YingYi L. Incremental weighted bipartite algorithm for large-scale recommendation systems. Turkish Journal of Electrical Engineering & Computer Sciences, 2016; 24: 448-463. doi: 10.2906/elk-1307-91
[12] Gabor T, Istvan P, Bottyan N, Domonkos T. Scalable collaborative filtering approaches for large recommender systems. Journal of Machine Learning Research, 2009; 10: 623-656. doi: 10.1016/j.eswa.2016.09.040
[13] Fidel C, Victor C, Diego F, Vreixo F. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable , high-performance recommender systems. ACM Transactions on the Web, 2011; 5: pp. 1-19 doi: 10.1145/1921591.1921593
[14] Shadi IA, Mohammad M. K-means algorithm with a novel distance measure. Turkish Journal of Electrical Engineering & Computer Sciences, 2013; 21: 1665-1684. doi: 10.3906/elk-1010-869
[15] Maria H, Yannis B, MichaLIS V. Clustering validity checking methods: part II. ACM SIGMOD Record, 2002; 19 31(3); 19-27. doi: 10.1145/601858.6011862
[16] Rui C,Qingyi H, Yan-Shuo C, Bo W, Lei Z, Xiangjie K. A Survey of Collaborative Filtering-Based Recommender Systems: From Traditional Methods to Hybrid Methods Based on Social Networks, IEEE Access, 2018; 6: 64301- 22 64320. doi:10.1109/ACCESS.2018.2877208
[17] Mao Q, Feng B, Pan S. A study of Top-N recommendation on user behavior data. in: IEEE International Conference on Computer Science and Automation Engineering; Zhangjiajie, China; 2012. pp. 582-586. doi: 10.1109/CSAE.2012.6272839
[18] Philip Z, Yi Z. Bayesian adaptive user pro ling with explicit & implicit feedback. In: ACM CIKM International Conference on Information and Knowledge Management, Arlington, Virginia, USA. 2006. pp. 397-404
[19] Yifan H, Yehuda K, Chris V. Collaborative filtering for implicit feedback datasets. In: Eighth IEEE International Conference on Data Mining; Pisa, Italy. 2008. pp. 263-272
[20] George K. Evaluation of item-based top-n recommendation algorithms. In: the tenth international conference on Information and knowledge management; Atlanta, Georgia, USA. 2001. pp. 247-254
[21] Thorsten J, Laura G, Bing P, Helene H, Geri G. Accurately interpreting clickthrough data as implicit feedback. In: SIGIR '05, ser. SIGIR '05. New York, NY, USA: ACM, 2005, pp. 154-161.
[22] Maria H, Yannis B, MichaLIS V. Clustering validity checking methods: part II. ACM SIGMOD Record, 2002; 31(3); 19-27. doi: 10.1145/601858.6011862
[23] Minakshi P, Anjana NG. Personalized recommender system using collaborative filtering technique and pyramid maintenance algorithm, International Journal of Computer Applications, 2016; 136(8): 25-31. doi: 10.5220/0006513202750282
[24] Tuan HD, Seung RJ, Hyunchul A. A novel recommendation model of location-based advertising: context-aware collaborative filtering using GA approach. Expert System with Applications, 2012; 39(3): 3731-3739. doi: 10.1016/j.eswa.2011.09.070
[25] Selcuk C, Abdulhamit S. Tourism demand modelling and forecasting using data mining techniques in multivariate time series: a case study in Turkey. Turkish Journal of Electrical Engineering & Computer Sciences, 2016; 24: 3388-3404. doi: 10.3906/elk-1311-134
[26] Francesco R, Lior R, Bracha S. Recommender Systems Handbook. New York, USA. Springer, 2011.
[27] Chiu-Chang T, Chi-Fu H, Zong-Han W. Collaborative location recommendations with dynamic time periods. Pervasive and Mobile Computing, 2017; 35(1): 1-14. doi: 10.1016/j.pmcj.2016.07.008
[28] Jian W, Jianhua H, Kai C, Yi Zhou, Zuoyin T. Collaborative filtering and deep learning based recommendation system for cold start items. Expert System with Applications, 2017; 69: 29-39. doi: 10.1016/j.eswa.2016.09.040
[29] Dwi KSU. Item collaborative filtering untuk rekomendasi paket wisata pada franchise tour and travel (in Indonesian). Bachelor, Maulana Malik Ibrahim State Islam University, Yogyakarta, Indonesia, 2014.
[30] Gilda MD, Mehregan M. A new collaborative filtering algorithm using k-means clustering and neighbors ' voting. In: 11th International Conference on Hybrid Intelligent Systems (HIS); Melacca, Malaysia; 2011. pp 179-184.
[31] Georgios P, Xiangliang Z, Wei W. Clustering recommenders in collaborative. IFIP Internationl Federation for Information Processing, 2011; 358: 82-97. doi: 10.1007/978-0-387-85691-96
[32] Xiaonan G, Sen W. Hierarchical Clustering Algorithm for Binary Data Based on Cosine Similarity. In: 8th International Conference on Logistics, Informatics and Service Sciences (LISS); Toronto, Canada; 2018. pp. 1-6. Doi: 10.1109/LISS.2018.8593222
[33] Manzhao B,Shijian L,Ji H. A Fast Collaborative Filtering Algorithm for Implicit Binary Data. In: IEEE 10th International Conference on Computer-Aided Industrial Design & Conceptual Design; Wenzhou, China; 2009. pp. 973-976. Doi: 10.1109/CAIDCD.2009.5374935
[34] Widiarina. Klastering data menggunakan algoritma dynamic k-means (in Indonesian). Jurnal Teknik Komputer AMIK BSI, 2015; 1(2): 260-265. doi: 10.31294/jtk.v1i2.259
[35] Citradevi M, Geetharamani G. An analysis on the performance of kmeans clustering algorithm for cardiotogram data cluster. International Journal on Computational Sciences & Aplications, 2012; 5: 11-20. doi: 10.5121/ijcsa.2012.2502
[36] Zhengwu Y, Haiguang L. Location recommendation algorithm based on temporal and geographical similarity in location-based social networks. In: 12th World Congress on Intelligent Control and Automation (WCICA), Guilin, China, 2016. pp. 1697-1702
[37] Huayu L, Yong G, Richang H, Hengshu Z. Point of interest recommendations: learning potential check-ins from friends. In: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Fransisco, CA, USA, 2016. pp:975-984
[38] John SB, David H, Cael K. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedding of the 14th Annual Conference on Uncertainty in Artificial Intelligence, San Fransisco, USA, 1998. pp. 43-52
[39] Jia-Dong Z, Chi-Yin C. Point-of-interest recommendations in location-based social networks. SIGSPATIAL Special, 2013; 3: 26-33. doi: 10.1109/CC.2015.7385525
[40] Lei G, Haoran J, Xinhua W, Fangai L. Learning to recommend point-of-interest with the weighted bayesian personalized ranking method in LBSNs. Information, 2017; 8(20) : 1-19. doi: 10.3390/inf8010020
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Affiliations
Sulis Setiowati
Politeknik Negeri Jakarta
Teguh Bharata Adji
Affiliation not stated
Igi Ardiyanto
Affiliation not stated
Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering
Abstract
Recommendation system always involves huge volumes of data, therefore it causes the scalability issues that do not only increase the processing time but also reduce the accuracy. In addition, the type of data used also greatly affects the result of the recommendations. In the recommendation system, there are two common types of data namely implicit (binary) rating and explicit (scalar) rating. Binary rating produces lower accuracy when it is not handled with the properly. Thus, optimized K-Means+ clustering and user-based collaborative filtering are proposed in this research. The K-Means clustering is optimized by selecting the K value using the Davies-Bouldin Index (DBI) method. The experimental result shows that the optimization of the K values produces better clustering than Elbow Method. The K-Means+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6% and f-measure of 7.2%, respectively. The proposed method was compared to DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in precision value. This result proves that K-Means+ with UBCF can handle implicit feedback datasets and improve precision.