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Efficient User Profiling Based Intelligent Travel Recommender System for Individual and Group of Users

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Abstract

In recent times, Recommender Systems (RSs) are gaining immense popularity with the wider adaptation to deal information overload problem in various application domains such as e-commerce, entertainment, e-tourism, etc. RSs are developed as information filtering systems to make personalized predictions based on the priorities and preferences for the suggestion of relevant items to users. Travel Recommender Systems (TRSs) generates a list of best matching locations or Point of Interests (POIs) to the users based their preferences. Predicting interesting locations for the generation recommendations from Location Based Social Network (LBSN) is crucial due to variety, size, and dimensions of data. The growing demand for effective TRS extends the scope for the development of user behavior based recommendation approach. In the literature, several research works are conducted to generate location recommendations by focusing on location attributes and failed to incorporate user behavior. As a significant solution to the existing limitations of TRSs, we propose Activity and Behavior induced Personalized Recommender System (ABiPRS) as a hybrid approach to predict persuasive POI recommendations. The proposed ABiPRS is designed to support travelling user by providing effective list of POIs as recommendations. As an extension, we have designed a new group recommendation model to meet the requirements of the group of users by exploiting relationships between them. Further, we have developed a novel hybridization approach for aggregating recommendations from multiple RSs to improve the effectiveness of recommendations. The proposed approaches are evaluated on the real-time large-scale datasets of Yelp and TripAdvisor. The experimental results depict the improved performance of the proposed hybrid recommendation approach over standalone and baseline hybrid approaches.

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Abbreviations

ABiPRS:

Activity and Behavior induced Personalized Recommender System

AKNN:

Adaptive K-Nearest Neighbors

CFRS:

Collaborative Filtering Recommender System

DCR:

Differential Context Relaxation

DCW:

Differential Context Weighting

DPSOHiK:

Dynamic Particle Swarm Optimization and Hierarchy induced K-Means

EIUCF:

Emotion Induced User-based Collaborative Filtering

FCM:

Fuzzy C-Means

GeoTeCS:

Geographical Temporal Categorical and Social

GPS:

Global Positioning System

HSS:

Hybrid Selection Score

HSSRS:

Hybrid Selection Score based Recommender System

ICF:

Item-based Collaborative Filtering

ICGS:

Item Clustering and Global Similarity

LBSN:

Location Based Social Network

MAE:

Mean Absolute Error

POI:

Point of Interest

PSO:

Particle Swarm Optimization

QICE:

Quantum Induced Clustering Ensemble

RS:

Recommender System

SPTW:

Social Pertinent Trust Walker

TBCF:

Time-context-Based Collaborative Filtering

TRS:

Travel Recommender System

UCF:

User-based Collaborative Filtering

USRiGRM:

User Social Relationship induced Group Recommendation Model

UTA:

Utilités Additives

UUPM:

Utility based User Preference Mining

References

  1. Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Knowl Data Eng IEEE Trans 17:734–749

    Article  Google Scholar 

  2. Amoretti M, Belli L, Zanichelli F (2017) UTravel: smart mobility with a novel user profiling and recommendation approach. Pervasive Mobil Comput 38:474–489

    Article  Google Scholar 

  3. Baral R, Wang D, Li T, Chen SC (2016) GeoTeCS: exploiting geographical, temporal, categorical and social aspects for personalized poi recommendation. In information reuse and integration (IRI), 2016 IEEE 17th international conference on (pp. 94-101). IEEE

  4. Baraldi A, Blonda P (1999) A survey of fuzzy clustering algorithms for pattern recognition. I. IEEE Transactions on Systems. Man, Cybernetics, Part B (Cybernetics) 29(6):778–785

    Article  Google Scholar 

  5. Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203

    Article  Google Scholar 

  6. Boratto L, Carta S (2010) State-of-the-art in group recommendation and new approaches for automatic identification of groups. In: Soro A, Vargiu E, Ar- mano G, Paddeu G (eds) Information retrieval and mining in distributed environments, studies in computational intelligence, vol 324. Springer, Berlin, pp 1–20

    Google Scholar 

  7. Burke R (2007) Hybrid web recommender systems. In: Brusilovsky P, Kobsa A, Nejdl W (eds) The adaptive web, Vol. 4321 of lecture notes in computer science. Springer, Berlin, pp 77–408

    Google Scholar 

  8. Capdevila J, Arias M, Arratia A (2016) GeoSRS: a hybrid social recommender system for geolocated data. Inf Syst 57:111–128

    Article  Google Scholar 

  9. Christensen I, Schiaffino S, Armentano M (2016) Social group recommendation in the tourism domain. J Intell Inf Syst 47(2):209–231

    Article  Google Scholar 

  10. De Amo S, Diallo MS, Diop CT, Giacometti A, Li D, Soulet A (2015) Contextual preference mining for user profile construction. Inf Syst 49:182–199

    Article  Google Scholar 

  11. Gao M, Liu K, Wu Z (2010) Personalisation in web computing and informatics: theories, techniques, applications, and future research. Inf Syst Front 12(5):607–629

    Article  Google Scholar 

  12. Gavalas D, Kasapakis V, Konstantopoulos C, Pantziou G, Vathis N, Zaroliagis C (2015) The eCOMPASS multimodal tourist tour planner. Expert Syst Appl 42(21):7303–7316

    Article  Google Scholar 

  13. Havens TC, Bezdek JC, Leckie C, Hall LO, Palaniswami M (2012) Fuzzy c-means algorithms for very large data. IEEE Trans Fuzzy Syst 20(6):1130–1146

    Article  Google Scholar 

  14. He L, Wu F (2009). A time-context-based collaborative filtering algorithm. In Granular Computing, 2009, GRC'09. IEEE International Conference on (pp. 209–213). IEEE

  15. Hussein T, Linder T, Gaulke W, Ziegler J (2014) Hybreed: a software framework for developing context-aware hybrid recommender systems. User Model User-Adap Inter 24(1–2):121–174

    Article  Google Scholar 

  16. Kaššák O, Kompan M, Bieliková M (2016) Personalized hybrid recommendation for group of users: top-N multimedia recommender. Inf Process Manag 52(3):459–477

    Article  Google Scholar 

  17. Kim H, Yang G, Jung H, Lee SH, Ahn JJ (2018) An intelligent product recommendation model to reflect the recent purchasing patterns of customers. Mobil Netw Appl 1–8

  18. Kompan M, Bielikova M (2014) Group recommendations: survey and perspectives. Comput Info 33(2):446–476

    Google Scholar 

  19. Lakiotaki K, Matsatsinis NF, Tsoukias A (2011) Multicriteria user modeling in recommender systems. IEEE Intell Syst 26(2):64–76

    Article  Google Scholar 

  20. Li X, Xu G, Chen E, Zong Y (2015) Learning recency based comparative choice towards point-of-interest recommendation. Expert Syst Appl 42(9):4274–4283

    Article  Google Scholar 

  21. Logesh R, Subramaniyaswamy V (2017a) A reliable point of interest recommendation based on trust relevancy between users. Wirel Pers Commun 1–30

  22. Logesh R, Subramaniyaswamy V (2017b) Learning Recency and inferring associations in location based social network for emotion induced point-of-interest recommendation. J Inf Sci Eng

  23. Logesh R, Subramaniyaswamy V, Malathi D, Senthilselvan N, Sasikumar A, Saravanan P, Manikandan G (2017a) Dynamic particle swarm optimization for personalized recommender system based on electroencephalography feedback. Biomed Res 28(13)

  24. Logesh R, Subramaniyaswamy V, Vijayakumar V, Gao XZ, Indragandhi V (2017b) A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Futur Gener Comput Syst

  25. Logesh R, Subramaniyaswamy V, Vijayakumar V (2018) A personalized travel recommender system utilizing social network profile and accurate GPS data. Electron Gov Int J

  26. Masthoff J (2011) Group recommender systems: combining individual models. Recommender systems handbook (pp. 677–702). Springer US

  27. Ntoutsi I, Stefanidis K (2012) gRecs: a group recommendation system based on user clustering. Database Systems for Advanced Applications (pp. 299–303). Springer, Berlin

    Google Scholar 

  28. Quijano-Sanchez L, Recio-Garcia J, Diaz-Agudo B, Jimenez-Diaz G (2013) Social factors in group recommender systems. ACM Trans Intell Syst Technol 4(1):1–30 ACM

    Article  Google Scholar 

  29. Ravi L, Vairavasundaram S (2016) A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. Comput Int Neurosci 2016:1–28

    Article  Google Scholar 

  30. Ren X, Song M, Haihong E, Song J (2017) Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation. Neurocomputing 241:38–55

    Article  Google Scholar 

  31. Resnick, P., Iacovou, N., Suchak, M., Bergstrom P, Riedl J (1994) GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work (pp. 175–186). ACM

  32. Sanchez F, Barrilero M, Uribe S, Alvarez F, Tena A, Menendez JM (2012) Social and content hybrid image recommender system for mobile social networks. Mobil Netw Appl 17(6):782–795

    Article  Google Scholar 

  33. Saranya KG, Sadasivam GS (2017) Personalized news article recommendation with novelty using collaborative filtering based rough set theory. Mobil Netw Appl 1–11

  34. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp. 285–295). ACM

  35. Senot C, Kostadinov D, Bouzid M (2010) Analysis of strategies for building group profiles. User modeling, adaptation, and personalization. Springer, Berlin, pp 40–51

    Book  Google Scholar 

  36. Subramaniyaswamy V, Logesh R (2017) Adaptive KNN based recommender system through Mining of User Preferences. Wirel Pers Commun, 1–19

  37. Subramaniyaswamy V, Vijayakumar V, Logesh R, Indragandhi V (2015) Intelligent travel recommendation system by mining attributes from community contributed photos. Procedia Comput Sci 50:447–455

    Article  Google Scholar 

  38. Wei, S., Ye, N., Zhang, S., Huang, X., & Zhu, J. (2012). Collaborative filtering recommendation algorithm based on item clustering and global similarity. In Business Intelligence and Financial Engineering (BIFE), 2012 Fifth International Conference on (pp. 69–72). IEEE

  39. Yang S, Zhou P, Duan K, Hossain MS, Alhamid MF (2017) emHealth: towards emotion health through depression prediction and intelligent health recommender system. Mobil Netw Appl 1–11

  40. Yilmaz Ö, Erdur RC (2012) iConAwa–an intelligent context-aware system. Expert Syst Appl 39(3):2907–2918

    Article  Google Scholar 

  41. Zhang Y, Tu Z, Wang Q (2017) TempoRec: temporal-topic based recommender for social network services. Mobil Netw Appl 22(6):1182–1191

    Article  Google Scholar 

  42. Zheng Y (2011) Location-based social networks: Users. Computing with Spatial Trajectories, Yu Zheng and Xiaofang Zhou, Eds. Springer, 243–276

  43. Zheng Y, Burke R, Mobasher B (2012) Differential context relaxation for context-aware travel recommendation. E-Comm Web Technol 88–99

  44. Zheng Y, Burke R, Mobasher B (2013) Recommendation with differential context weighting. In International Conference on User Modeling, Adaptation, and Personalization. Springer, Berlin, pp 152–164 Recommendation with Differential Context Weighting

    Book  Google Scholar 

  45. Zhou F, Jiao JR, Yang XJ, Lei B (2017) Augmenting feature model through customer preference mining by hybrid sentiment analysis. Expert Syst Appl 89:306–317

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to Science and Engineering Research Board (SERB), Department of Science & Technology, New Delhi, for the financial support (No. YSS/2014/000718/ES). Authors express their gratitude to SASTRA Deemed University, Thanjavur, for providing the infrastructural facilities to carry out this research work.

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Authors and Affiliations

  1. School of Computing, SASTRA Deemed University, Thanjavur, Tamilnadu, India

    R. Logesh & V. Subramaniyaswamy

  2. School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, Tamilnadu, India

    V. Vijayakumar

  3. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China

    Xiong Li

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  1. R. Logesh
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  2. V. Subramaniyaswamy
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  3. V. Vijayakumar
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  4. Xiong Li
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Correspondence to R. Logesh.

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Logesh, R., Subramaniyaswamy, V., Vijayakumar, V. et al. Efficient User Profiling Based Intelligent Travel Recommender System for Individual and Group of Users. Mobile Netw Appl 24, 1018–1033 (2019). https://doi.org/10.1007/s11036-018-1059-2

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