Skip to main content
SpringerLink
Log in

Recommending packages with validity constraints to groups of users

  • Regular Paper
  • Published:
Knowledge and Information Systems Aims and scope Submit manuscript

Abstract

The success of recommender systems has made them the focus of a massive research effort in both industry and academia. Recent work has generalized recommendations to suggest packages of items to single users, or single items to groups of users. However, to the best of our knowledge, the interesting problem of recommending a package to a group of users (P2G) has not been studied to date. This is a problem with several practical applications, such as recommending vacation packages to tourist groups, entertainment packages to groups of friends or sets of courses to groups of students. In this paper, we formulate the P2G problem, and we propose probabilistic models that capture the preference of a group toward a package, incorporating factors such as user impact and package viability. We also investigate the issue of recommendation fairness. This is a novel consideration that arises in our setting, where we require that no user is consistently slighted by the item selection in the package. In addition, we study a special case of the P2G problem, where the recommended items are places and the recommendation should consider the current locations of the users in the group. We present aggregation algorithms for finding the best packages and compare our suggested models with baseline approaches stemming from previous work. The results show that our models find packages of high quality which consider all special requirements of P2G recommendation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Notes

  1. In general, for a set of n items, the viability can be defined by aggregating their pairwise relevance or by defining an n-ary function. In this paper, for simplicity and due to the application domain of our case studies in the experiments, we follow the first approach.

  2. http://www.yelp.com/dataset_challenge.

  3. http://grouplens.org/datasets/movielens/.

  4. http://mahout.apache.org.

  5. Since the user has visited all these restaurants, it is reasonable to assume that his usual location would be the one that minimizes the average distance to all of them.

References

  1. Amer-Yahia S, Bonchi F, Castillo C, Feuerstein E, Méndez-Díaz I, Zabala P (2014) Composite retrieval of diverse and complementary bundles. IEEE TKDE 26(11):2662–2675

    Google Scholar 

  2. Amer-Yahia S, Roy SB, Chawla A, Das G, Yu C (2009) Group recommendation: semantics and efficiency. PVLDB 2(1):754–765

    Google Scholar 

  3. Deng T, Fan W, Geerts F (2013) On the complexity of package recommendation problems. SIAM J Comput 42(5):1940–1986

    Article  MathSciNet  MATH  Google Scholar 

  4. Fagin R, Lotem A, Naor M (2003) Optimal aggregation algorithms for middleware. J Comput Syst Sci 66(4):614–656

    Article  MathSciNet  MATH  Google Scholar 

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

  6. Gorla J, Lathia N, Robertson S, Wang J (2013) Probabilistic group recommendation via information matching. WWW, pp 495–504

  7. Ilyas IF, Aref WG, Elmagarmid AK (2003) Supporting top-k join queries in relational databases. VLDB, pp 754–765

  8. Interdonato R, Romeo S, Tagarelli A, Karypis G (2013) A versatile graph-based approach to package recommendation. ICTAI, pp 857–864

  9. Jameson A, Smyth B (2007) Recommendation to groups. The adaptive web, methods and strategies of web personalization, pp 596–627

  10. Kay J, Lauder P (1988) A fair share scheduler. Commun ACM 31(1):44–55

    Article  Google Scholar 

  11. Kushner HJ, Whiting PA (2004) Convergence of proportional-fair sharing algorithms under general conditions. IEEE Trans Wirel Commun 3(4):1250–1259

    Article  Google Scholar 

  12. Li K, Lu W, Bhagat S, Lakshmanan LVS, Yu C (2014) On social event organization. KDD, pp 1206–1215

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

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

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

  16. Liu X, Tian Y, Ye M, Lee W-C (2012) Exploring personal impact for group recommendation. CIKM, pp 674–683

  17. Lu Z, Li H, Mamoulis N, Cheung DW (2017) Hbgg: a hierarchical Bayesian geographical model for group recommendation. In: Proceedings of the SIAM international conference on Data Mining (SDM), Houston, TX, April 2017

  18. O’Connor M, Cosley D, Konstan JA, Riedl J (2001) Polylens: a recommender system for groups of user. ECSCW, pp 199–218

  19. Parameswaran AG, Venetis P, Garcia-Molina H (2011) Recommendation systems with complex constraints: a course recommendation perspective. ACM TOIS 29(4):20

    Article  Google Scholar 

  20. Qi S, Mamoulis N, Pitoura E, Tsaparas P (2016) Recommending packages to groups. ICDM, pp 449–458

  21. Recio-García JA, Jiménez-Díaz G, Sánchez-Ruiz-Granados AA, Díaz-Agudo B (2009) Personality aware recommendations to groups. RecSys, pp 325–328

  22. Roy SB, Thirumuruganathan S, Amer-Yahia S, Das G, Yu C (2014) Exploiting group recommendation functions for flexible preferences. ICDE, pp 412–423

  23. Sarwar BM, Karypis G, Konstan JA, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. WWW, pp 285–295

  24. Serbos D, Qi S, Mamoulis N, Pitoura E, Tsaparas P (2017) Fairness in package-to-group recommendations. WWW, pp 371–379

  25. Steinhaus H (1948) The problem of fair division. Econometrica 16(1):101–104

    Google Scholar 

  26. Stettinger M (2014) Choicla: towards domain-independent decision support for groups of users. RecSys, pp 425–428

  27. Stettinger M, Felfernig A, Leitner G, Reiterer S (2015) Counteracting anchoring effects in group decision making. UMAP, pp 118–130

  28. Wang H, Terrovitis M, Mamoulis N (2013) 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, SIGSPATIAL GIS, pp 364–373

  29. Xie M, Lakshmanan LVS, Wood PT (2010) Breaking out of the box of recommendations: from items to packages. RecSys, pp 151–158

  30. Xie M, Lakshmanan LVS, Wood PT (2014) Generating top-k packages via preference elicitation. PVLDB 7(14):1941–1952

    Google Scholar 

  31. Ye M, Yin P, Lee W-C (2010) Location recommendation for location-based social networks. SIGSPATIAL GIS, pp 458–461

  32. Yuan Q, Cong G, Lin C-Y (2014) Com: a generative model for group recommendation. KDD, pp 163–172

  33. Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation. SIGIR, pp 363–372

  34. Zhang J-D, Chow C-Y (2013) iGSLR: personalized geo-social location recommendation—a kernel density estimation approach. SIGSPATIAL GIS, pp 324–333

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

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

  37. Zheng Y, Zhang L, Xie X, Ma W-Y (2009) Mining interesting locations and travel sequences from GPS trajectories. WWW, pp 791–800

  38. Zhu T, Harrington P, Li J, Tang L (2014) Bundle recommendation in ecommerce. SIGIR, pp 657–666

Download references

Acknowledgements

This work was supported by Grant 17205015 from Hong Kong RGC, by European Unions Horizon 2020 research and innovation programme under Grant agreement No. 657347, and by Marie Curie Reintegration Grant project titled JMUGCS which has received research funding from the European Union.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong SAR, China

    Shuyao Qi

  2. Department of Computer Science and Engineering, University of Ioannina, Ioannina, Greece

    Nikos Mamoulis, Evaggelia Pitoura & Panayiotis Tsaparas

Authors
  1. Shuyao Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nikos Mamoulis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Evaggelia Pitoura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Panayiotis Tsaparas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikos Mamoulis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qi, S., Mamoulis, N., Pitoura, E. et al. Recommending packages with validity constraints to groups of users. Knowl Inf Syst 54, 345–374 (2018). https://doi.org/10.1007/s10115-017-1082-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10115-017-1082-9

Keywords

Search

Navigation