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International Conference on Database Systems for Advanced Applications

DASFAA 2018: Database Systems for Advanced Applications pp 67–82Cite as

Geographical Relevance Model for Long Tail Point-of-Interest Recommendation

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10827))

Abstract

Point-of-Interest (POI) recommendation plays a key role in people’s daily life, and has been widely studied in recent years, due to its increasingly applications (e.g., recommending new restaurants for users). One of important phenomena in the POI recommendation community is the data sparsity, which makes deep impact on the quality of recommendation. Existing works have proposed various models to alleviate the bottleneck of the data sparsity, and most of these works addressed this issue from the user perspective. To the best of our knowledge, few attention has been made to address this issue from the POI perspective. In this paper, we observe that the “long tail” POIs, which have few check-ins and have less opportunity to be exposed, take up a great proportion among all the POIs. It is interesting and meaningful to investigate the long tail POI recommendation from the POI perspective. To this end, this paper proposes a new model, named GRM (geographical relevance model), that expands POI profiles via relevant POIs and employs the geographical information, addressing the limitations of existing models. Experimental results based on two public datasets demonstrate that our model is effective and competitive. It outperforms state-of-the-art models for the long tail POI recommendation problem.

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Notes

  1. 1.

    Foursquare is available at https://pan.baidu.com/s/1hrYNwJM.

  2. 2.

    Gowalla is available at https://pan.baidu.com/s/1i4DgFmX.

References

  1. Li, X., Cong, G., Li, X., Pham, T., Krishnaswamy, S.: Rank-GeoFM: a ranking based geographical factorization method for point of interest recommendation. In: SIGIR, pp. 433–442 (2015)

    Google Scholar 

  2. Zhang, J., Chow, C.: GeoSoCa: exploiting geographical, social and categorical correlations for point-of-interest recommendations. In: SIGIR, pp. 443–452 (2015)

    Google Scholar 

  3. Hosseini, S., Yin, H., Zhang, M., Zhou, X., Sadiq, S.: Jointly modeling heterogeneous temporal properties in location recommendation. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10177, pp. 490–506. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55753-3_31

    Chapter  Google Scholar 

  4. Shang, S., Chen, L., Jensen, C., Wen, J., Kalnis, P.: Searching trajectories by regions of interest. TKDE 29(7), 1549–1562 (2017)

    Google Scholar 

  5. Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Nguyen, Q.: Adapting to user interest drift for POI recommendation. TKDE 28(10), 2566–2581 (2016)

    Google Scholar 

  6. Shang, S., Chen, L., Wei, Z., Jensen, C., Wen, J., Kalnis, P.: Collective travel planning in spatial networks. TKDE 28(5), 1132–1146 (2016)

    Google Scholar 

  7. Cheng, C., Yang, H., King, I., Lyu, M.: Fused matrix factorization with geographical and social influence in location-based social networks. AAAI 12, 17–23 (2012)

    Google Scholar 

  8. Zhang, J., Chow, C.: iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. In: SIGSPATIAL, pp. 334–343 (2013)

    Google Scholar 

  9. Shang, S., Lu, H., Pedersen, T., Xie, X.: Modeling of traffic-aware travel time in spatial networks. In: MDM, pp. 247–250 (2013)

    Google Scholar 

  10. Wang, W., Yin, H., Chen, L., Sun, Y., Sadiq, S., Zhou, X.: Geo-SAGE: a geographical sparse additive generative model for spatial item recommendation. In: SIGKDD, pp. 1255–1264 (2015)

    Google Scholar 

  11. He, J., Li, X., Liao, L.: Category-aware next point-of-interest recommendation via listwise Bayesian personalized ranking. In: IJCAI, pp. 1837–1843 (2017)

    Google Scholar 

  12. Chen, X., Zeng, Y., Cong, G., Qin, S., Xiang, Y., Dai, Y.: On information coverage for location category based point-of-interest recommendation. In: AAAI, pp. 37–43 (2015)

    Google Scholar 

  13. Shang, S., Zheng, K., Jensen, C., Yang, B., Kalnis, B., Li, G., Wen, J.: Discovery of path nearby clusters in spatial networks. TKDE 27(6), 1505–1518 (2015)

    Google Scholar 

  14. Noulas, A., Scellato, S., Lathia, N., Mascolo, C.: A random walk around the city: new venue recommendation in location-based social networks. In: SocialCom/PASSAT, pp. 144–153 (2012)

    Google Scholar 

  15. Lichman, M., Smyth, P.: Modeling human location data with mixtures of kernel densities. In: SIGKDD, pp. 35–44 (2014)

    Google Scholar 

  16. Shang, S., Ding, R., Zheng, K., Jensen, C., Kalnis, P., Zhou, X.: Personalized trajectory matching in spatial networks. VLDB J. 23(3), 449–468 (2014)

    Article  Google Scholar 

  17. Bao, J., Zheng, Y., Wilkie, D., Mokbel, M.: Recommendations in location-based social networks: a survey. Geoinformatica 19(3), 525–565 (2015)

    Article  Google Scholar 

  18. Shang, S., Liu, J., Zheng, K., Lu, H., Pedersen, T., Wen, J.: Planning unobstructed paths in traffic-aware spatial networks. GeoInformatica 19(4), 723–746 (2015)

    Article  Google Scholar 

  19. Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: SIGKDD, pp. 831–840 (2014)

    Google Scholar 

  20. Ye, M., Yin, P., Lee, W., Lee, D.: Exploiting geographical influence for collaborative point-of-interest recommendation. In: SIGIR, pp. 325–334 (2011)

    Google Scholar 

  21. Levandoski, J., Sarwat, M., Eldawy, A., Mokbel, M.: Lars: a location-aware recommender system. In: ICDE, pp. 450–461 (2012)

    Google Scholar 

  22. Shang, S., Yuan, B., Deng, K., Xie, K., Zheng, K., Zhou, X.: PNN query processing on compressed trajectories. GeoInformatica 16(3), 467–496 (2012)

    Article  Google Scholar 

  23. Zheng, V., Zheng, Y., Xie, X., Yang, Q.: Collaborative location and activity recommendations with GPS history data. In: WWW, pp. 1029–1038 (2010)

    Google Scholar 

  24. Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y., Yuan, Q.: Personalized ranking metric embedding for next new POI recommendation. In: IJCAI, pp. 2069–2075 (2015)

    Google Scholar 

  25. Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: AAAI, pp. 194–200 (2016)

    Google Scholar 

  26. Yuan, Q., Cong, G., Sun, A.: Graph-based point-of-interest recommendation with geographical and temporal influences. In: CIKM, pp. 659–668 (2014)

    Google Scholar 

  27. Liu, B., Fu, Y., Yao, Z., Xiong, H.: Learning geographical preferences for point-of-interest recommendation. In: SIGKDD, pp. 1043–1051 (2013)

    Google Scholar 

  28. Liu, B., Xiong, H., Papadimitriou, S., Fu, Y., Yao, Z.: A general geographical probabilistic factor model for point of interest recommendation. TKDE 27(5), 1167–1179 (2015)

    Google Scholar 

  29. Zhang, J., Chow, C., Zheng, Y.: ORec: an opinion-based point-of-interest recommendation framework. In: CIKM, pp. 1641–1650 (2015)

    Google Scholar 

  30. Gao, H., Tang, T., Hu, X., Liu, H.: Content-aware point of interest recommendation on location-based social networks. In: AAAI, pp. 1721–1727 (2015)

    Google Scholar 

  31. Gao, H., Tang, J., Hu, X., Liu, H.: Exploring temporal effects for location recommendation on location-based social networks. In: RecSys, pp. 93–100 (2013)

    Google Scholar 

  32. Liu, Y., Liu, C., Liu, B., Qu, M., Xiong, H.: Unified point-of-interest recommendation with temporal interval assessment. In: SIGKDD, pp. 1015–1024 (2016)

    Google Scholar 

  33. Shang, S., Chen, L., Wei, Z., Jensen, C., Zheng, K., Kalnis, P.: Trajectory similarity join in spatial networks. PVLDB 10(11), 1178–1189 (2017)

    Google Scholar 

  34. Wang, Y., Yuan, N., Lian, D., Lin, L., Xie, X., Chen, E., Rui, Y.: Regularity and conformity: location prediction using heterogeneous mobility data. In: SIGKDD, pp. 1275–1284 (2015)

    Google Scholar 

  35. Yin, H., Cui, B., Li, J., Yao, J., Chen, C.: Challenging the long tail recommendation. PVLDB 5(9), 896–907 (2012)

    Google Scholar 

  36. Shang, S., Ding, R., Yuan, B., Xie, K., Zheng, K., Kalnis, P.: User oriented trajectory search for trip recommendation. In: EDBT, pp. 156–167 (2012)

    Google Scholar 

  37. Valcarce, D., Parapar, J., Barreiro, Á.: Item-based relevance modelling of recommendations for getting rid of long tail products. KBS 103(C), 41–51 (2016)

    Google Scholar 

  38. Lavrenko, V., Croft, W.B.: Relevance based language models. In: SIGIR, pp. 120–127 (2001)

    Google Scholar 

  39. Parapar, J., Bellogín, A., Castells, P., Barreiro, Á.: Relevance-based language modelling for recommender systems. IPM 49(4), 966–980 (2013)

    Google Scholar 

  40. Shang, S., Lu, H., Pedersen, T.B., Xie, X.: Finding traffic-aware fastest paths in spatial networks. In: Nascimento, M.A., Sellis, T., Cheng, R., Sander, J., Zheng, Y., Kriegel, H.-P., Renz, M., Sengstock, C. (eds.) SSTD 2013. LNCS, vol. 8098, pp. 128–145. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40235-7_8

    Chapter  Google Scholar 

  41. Valcarce, D., Parapar, J., Barreiro, Á.: A study of priors for relevance-based language modelling of recommender systems. In: RecSys, pp. 237–240 (2015)

    Google Scholar 

  42. Zhai, C., Lafferty, J.: A study of smoothing methods for language models applied to information retrieval. TOIS 22(2), 179–214 (2004)

    Article  Google Scholar 

  43. Desrosiers, C., Karypis, G.: A comprehensive survey of neighborhood-based recommendation methods. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 107–144. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_4

    Chapter  Google Scholar 

  44. Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys, pp. 39–46 (2010)

    Google Scholar 

  45. Wang, Z., Wang, D., Yao, B., Guo, M.: Probabilistic range query over uncertain moving objects in constrained two-dimensional space. TKDE 27(3), 866–879 (2015)

    Google Scholar 

  46. Xie, K., Deng, K., Shang, S., Zhou, X., Zheng, K.: Finding alternative shortest paths in spatial networks. TODS 37(4), 29:1–29:31 (2012)

    Article  Google Scholar 

  47. Wang, Z., Yao, B., Cheng, R., Gao, X., Zou, L., Guan, H., Guo, M.: SMe: explicit & implicit constrained-space probabilistic threshold range queries for moving objects. GeoInformatica 20(1), 19–58 (2016)

    Article  Google Scholar 

  48. Feng, S., Cong, G., An, B., Chee, Y.: POI2Vec: geographical latent representation for predicting future visitors. In: AAAI, pp. 102–108 (2017)

    Google Scholar 

Download references

Acknowledgment

This work was supported by the NSFC (61472453,61672351, 61729202, 91438121, U1401256, U1501252, U1611264, U1711261, U1636210 and U1711262), the Opening Projects of Guangdong Key Laboratory of Big Data Analysis and Processing (201808), Guangdong Province Key Laboratory of Popular High Performance Computers of Shenzhen University (SZU-GDPHPCL2017), State Key Laboratory of Mathematical Engineering and Advanced Computing (2017A01), the 2016 Characteristic Innovation Project (Natural Science) of Education Department of Guangdong Province of China (2016KTSCX162), Foshan Science and Technology Bureau Project (2016AG100382), and Guangdong Pre-national project (2014GKXM054).

Author information

Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China

    Wei Liu, Zhi-Jie Wang, Mengdie Nie & Jian Yin

  2. Shanghai Jiao Tong University, Shanghai, China

    Bin Yao

  3. Neusoft Institute Guangdong, Foshan, China

    Jing Wang

  4. Shenzhen University, Shenzhen, China

    Rui Mao

  5. Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China

    Wei Liu, Zhi-Jie Wang, Bin Yao, Mengdie Nie & Jian Yin

  6. Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen, China

    Bin Yao & Rui Mao

Authors
  1. Wei Liu
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  2. Zhi-Jie Wang
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  7. Jian Yin
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Corresponding authors

Correspondence to Zhi-Jie Wang or Jian Yin .

Editor information

Editors and Affiliations

  1. Simon Fraser University, Burnaby, BC, Canada

    Jian Pei

  2. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos

  3. University of Queensland, Brisbane, QLD, Australia

    Shazia Sadiq

  4. University of Western Australia, Crawley, WA, Australia

    Jianxin Li

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Liu, W. et al. (2018). Geographical Relevance Model for Long Tail Point-of-Interest Recommendation. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-91452-7_5

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