Skip to main content
SpringerLink
Log in
Book cover

Advances in Data and Information Sciences pp 197–210Cite as

Latest Trends in Recommender Systems 2017

  • Conference paper
  • First Online:

  • 869 Accesses

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 39))

Abstract

Recommender systems are in trend from last two decades. Most of the early recommender systems were made using content-based and collaborative filtering methods. Computational intelligence and knowledge base were used in mid-90s. Later recommender systems used social networking, group recommendations, context-aware, and hybrid systems. Today in the era of big data and e-commerce, massive amount of data from web and organizations provide new opportunities for recommender systems. Increased information obtained from high volume of data can be used in recommender systems to provide users with personalized product or service recommendations. From 2013, most of the social networking site like Facebook and Instagram and online shopping giants like Amazon, Flipkart, etc., started providing personalized recommendations to engage and attract users. Most of the review papers are full of applications of recommender systems which do not give the clear idea about methods, techniques, and shortcomings of these systems. Hence, this paper presents an analysis of methods and techniques in current (majorly from 2013) recommender systems. Current challenges have been identified to carry out the work in future.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013) Recommender systems survey. Knowl-Based Syst 46:109–132

    Article  Google Scholar 

  2. Lu J, Dianshuang W, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32

    Article  Google Scholar 

  3. Drachsler H, Verbert K, Santos OC, Manouselis N (2015) Panorama of recommender systems to support learning. In: Recommender systems handbook. Springer, US, pp 421–451

    Chapter  Google Scholar 

  4. Verbert K, Manouselis N, Ochoa X, Wolpers M, Drachsler H, Bosnic I, Duval E (2012) Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans Learn Technol 5(4):318–335

    Article  Google Scholar 

  5. Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. In: Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 253–260

    Google Scholar 

  6. Pazzani MJ, Billsus D (2007) Content-based recommendation systems. In: The adaptive web. Springer, Berlin, Heidelberg, pp 325–341

    Google Scholar 

  7. He J, Chu WW (2010) A social network-based recommender system (SNRS). In: Data mining for social network data. Springer, US, pp 47–74

    Chapter  Google Scholar 

  8. Lops P, Gemmis MD, Semeraro G (2011) Content-based recommender systems: state of the art and trends. In: Recommender systems handbook. Springer, US, pp 73–105

    Google Scholar 

  9. 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(6):734–749

    Article  Google Scholar 

  10. 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. ACM, pp 285–295

    Google Scholar 

  11. Deshpande M, Karypis G (2004) Item-based top-n recommendation algorithms. ACM Trans Inf Syst (TOIS) 22(1):143–177

    Article  Google Scholar 

  12. Shambour Q, Jie L (2011) A hybrid trust-enhanced collaborative filtering recommendation approach for personalized government-to-business e-services. Int J Intel Syst 26(9):814–843

    Article  Google Scholar 

  13. Trewin S (2000) Knowledge-based recommender systems. Encycl Lib Inf Sci 69(Supplement 32):180

    Google Scholar 

  14. Smyth B (2007) Case-based recommendation. In: The adaptive web, pp 342–376

    Google Scholar 

  15. Middleton SE, Roure DD, Shadbolt R (2009) Ontology-based recommender systems. In: Handbook on ontologies. Springer, Berlin, Heidelberg, pp 779–796

    Chapter  Google Scholar 

  16. Cantador I, Bellogín A, Castells P (2008) A multilayer ontology-based hybrid recommendation model. Ai Commun 21(2–3):203–210

    MathSciNet  MATH  Google Scholar 

  17. Burke R (2007) The adaptive web. Chapter hybrid web recommender systems, p 377

    Google Scholar 

  18. Billsus D, Pazzani MJ (2000) User modeling for adaptive news access. User Model User-Adap Inter 10(2–3):147–180

    Article  Google Scholar 

  19. Mobasher B, Jin X, Zhou Y (2004) Semantically enhanced collaborative filtering on the web. In: Web mining: from web to semantic web. Springer, Berlin, Heidelberg, pp 57–76

    Chapter  Google Scholar 

  20. Smyth B, Cotter P (2000) A personalised TV listings service for the digital TV age. Knowl-Based Syst 13(2):53–59

    Article  Google Scholar 

  21. Wilson DC, Smyth B, Sullivan Derry O (2003) Sparsity reduction in collaborative recommendation: a case-based approach. Int J Pattern Recognit Artif Intell 17(05):863–884

    Article  Google Scholar 

  22. Sullivan DO, Smyth B, Wilson D (2004) Preserving recommender accuracy and diversity in sparse datasets. Int J Artif Intel Tools 13(01):219–235

    Article  Google Scholar 

  23. Bellogín A, Cantador I, Díez F, Castells P, Chavarriaga E (2013) An empirical comparison of social, collaborative filtering, and hybrid recommenders. ACM Trans Intel Syst Technol (TIST) 4(1):14

    Article  Google Scholar 

  24. Abbas A, Zhang L, Khan SU (2015) A survey on context-aware recommender systems based on computational intelligence techniques. Computing 97(7):667–690

    Article  MathSciNet  Google Scholar 

  25. Amatriain X, Pujol JM (2015) Data mining methods for recommender systems. In: Recommender systems handbook. Springer, US, pp 227–262

    Chapter  Google Scholar 

  26. Ghazanfar MA, Prügel-Bennett A (2014) Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Syst Appl 41(7):3261–3275

    Article  Google Scholar 

  27. Bobadilla J, Ortega F, Hernando A, Alcalá J (2011) Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl-Based Syst 24(8):1310–1316

    Article  Google Scholar 

  28. Hwang S-Y, Wei C-P, Liao Y-F (2010) Coauthorship networks and academic literature recommendation. Electron Commer Res Appl 9(4):323–334

    Article  Google Scholar 

  29. Dey AK, Abowd GD, Salber D (2001) A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human Comput Interact 16(2):97–166

    Article  Google Scholar 

  30. Wang W, Zhang G, Jie L (2016) Member contribution-based group recommender system. Decis Support Syst 87:80–93

    Article  Google Scholar 

  31. Majid A, Chen L, Chen G, Mirza HT, Hussain I, Woodward J (2013) A context-aware personalized travel recommendation system based on geotagged social media data mining. Int J Geogr Inf Sci 27(4):662–684

    Article  Google Scholar 

  32. O’connor M, Cosley D, Konstan JA, Riedl J (2001) PolyLens: a recommender system for groups of users. In: ECSCW 2001. Springer, Netherlands, pp 199–218

    Google Scholar 

  33. Jameson A, Smyth B (2007) Recommendation to groups. In: The adaptive web, pp 596–627

    Google Scholar 

  34. Chowdhury N, Cai X (2016) Nonparametric Bayesian probabilistic latent factor model for group recommender systems. In: International conference on web information systems engineering. Springer International Publishing, pp 61–76

    Chapter  Google Scholar 

  35. Quijano-Sanchez L, Recio-Garcia JA, Diaz-Agudo B, Jimenez-Diaz G (2013) Social factors in group recommender systems. ACM Trans Intel Syst Technol (TIST) 4(1):8

    Article  Google Scholar 

  36. Popescu G (2013) Group recommender systems as a voting problem. In: International conference on online communities and social computing. Springer, Berlin, Heidelberg, pp 412–421

    Chapter  Google Scholar 

  37. Cho YS, Moon SC, Jeong S-P, Oh I-B, Ryu KH (2013) Clustering method using item preference based on RFM for recommendation system in u-commerce. In: Ubiquitous information technologies and applications. Springer, Dordrecht, pp 353–362

    Google Scholar 

  38. Yang D, Zhang D, Yu Z, Wang Z (2013) A sentiment-enhanced personalized location recommendation system. In: Proceedings of the 24th ACM conference on hypertext and social media.ACM, pp 119–128

    Google Scholar 

  39. Vesin B, Klašnja-Milićević A, Ivanović M, Budimac Z (2013) Applying recommender systems and adaptive hypermedia for e-learning personalizatio. Comput Inform 32(3):629–659

    MATH  Google Scholar 

  40. Hornung T, Ziegler C-N, Franz S, Przyjaciel-Zablocki M, Schätzle A, Lausen G (2013) Evaluating hybrid music recommender systems. In: Proceedings of the 2013 IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT), vol 01. IEEE Computer Society, pp 57–64

    Google Scholar 

  41. Razak TR, Hashim MA, Noor NM, Halim IHA, Shamsul NFF (2014) Career path recommendation system for UiTM Perlis students using fuzzy logic. In: 2014 5th international conference on intelligent and advanced systems (ICIAS). IEEE, pp 1–5

    Google Scholar 

  42. Heap B, Krzywicki A, Wobcke W, Bain M, Compton P (2014) Combining career progression and profile matching in a job recommender system. In: Pacific Rim international conference on artificial intelligence. Springer, Cham, pp 396–408

    Google Scholar 

  43. Yu H-F, Hsieh C-J, Si S, Dhillon IS (2014) Parallel matrix factorization for recommender systems. Knowl Inf Syst 41(3):793–819

    Article  Google Scholar 

  44. Sarwat M, Levandoski JJ, Eldawy A, Mokbel MF (2014) LARS*: an efficient and scalable location-aware recommender system. IEEE Trans Knowl Data Eng 26(6):1384–1399

    Article  Google Scholar 

  45. Khribi MK, Jemni M, Nasraoui O (2015) Recommendation systems for personalized technology-enhanced learning. In: Ubiquitous learning environments and technologies. Springer, Berlin, Heidelberg, pp 159–180

    Google Scholar 

  46. Parveen H, Ashraf M, Parveen R (2015) Improving the performance of multi-criteria recommendation system using fuzzy integrated meta heuristic. In: 2015 international conference on computing, communication and automation (ICCCA). IEEE, pp 304–308

    Google Scholar 

  47. Zhang X, Cheng J, Qiu S, Zhu G, Hanqing L (2015) Dualds: a dual discriminative rating elicitation framework for cold start recommendation. Knowl-Based Syst 73:161–172

    Article  Google Scholar 

  48. Pirasteh P, Hwang D, Jung JJ (2015) Exploiting matrix factorization to asymmetric user similarities in recommendation systems. Knowl-Based Syst 83:51–57

    Article  Google Scholar 

  49. Adomavicius G, Zhang J (2015) Improving stability of recommender systems: a meta-algorithmic approach. IEEE Trans Knowl Data Eng 27(6):1573–1587

    Article  Google Scholar 

  50. Chen G, Chen L (2015) Augmenting service recommender systems by incorporating contextual opinions from user reviews. User Model User-Adap Inter 25(3):295–329

    Article  Google Scholar 

  51. Das J, Majumder S, Dutta D, Gupta P (2015) Iterative use of weighted voronoi diagrams to improve scalability in recommender systems. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Cham, pp 605–617

    Google Scholar 

  52. Cho YS, Moon SC (2015) Recommender system using periodicity analysis via mining sequential patterns with time-series and FRAT analysis. JoC 6(1):9–17

    Google Scholar 

  53. Martinez-Cruz C, Porcel C, Bernabé-Moreno J, Herrera-Viedma E (2015) A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Inf Sci 311:102–118

    Article  Google Scholar 

  54. Alqadah F, Reddy CK, Junling H, Alqadah HF (2015) Biclustering neighborhood-based collaborative filtering method for top-n recommender systems. Knowl Inf Syst 44(2):475–491

    Article  Google Scholar 

  55. Codina V, Ricci F, Ceccaroni L (2016) Distributional semantic pre-filtering in context-aware recommender systems. User Model User-Adap Inter 26(1):1–32

    Article  Google Scholar 

  56. Moreno MN, Saddys S, Vivian FL, María DM, Sánchez AL (2016) Web mining based framework for solving usual problems in recommender systems. A case study for movies recommendation. Neurocomputing 176:72–80

    Article  Google Scholar 

  57. Pinto FM, Estefania M, Cerón N, Andrade R, Campaña M (2016) iRecomendYou: a design proposal for the development of a pervasive recommendation system based on student’s profile for ecuador’s students’ candidature to a scholarship. In: New advances in information systems and technologies. Springer, Cham, pp 537–546

    Chapter  Google Scholar 

  58. Luo X, Zhou MC, Li S, You Z, Xia Y, Zhu Q (2016) A nonnegative latent factor model for large-scale sparse matrices in recommender systems via alternating direction method. IEEE Trans Neural Netw Learn Syst 27(3):579–592

    Article  MathSciNet  Google Scholar 

  59. Guan X, Li C-T, Guan Y (2016) Enhanced SVD for collaborative filtering. In: Pacific-Asia conference on knowledge discovery and data mining. Springer International Publishing, pp 503–514

    Chapter  Google Scholar 

  60. Ying Y, Chen L, Chen G (2017) A temporal-aware POI recommendation system using context-aware tensor decomposition and weighted HITS. Neurocomputing 242:195–205

    Article  Google Scholar 

  61. Rawat YS, Kankanhalli MS (2017) ClickSmart: a context-aware viewpoint recommendation system for mobile photography. IEEE Trans Circuits Syst Video Technol 27(1):149–158

    Article  Google Scholar 

  62. Wei J, He J, Chen K, Zhou Y, Tang Z (2017) Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst Appl 69:29–39

    Article  Google Scholar 

  63. Colomo-Palacios R, García-Peñalvo FJ, Stantchev V, Misra S (2017) Towards a social and context-aware mobile recommendation system for tourism. Pervasive Mob Comput 38:505–515

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, India

    Poonam Singh, Sachin Ahuja & Shaily Jain

Authors
  1. Poonam Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sachin Ahuja
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaily Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Poonam Singh or Shaily Jain .

Editor information

Editors and Affiliations

  1. Smart Grid and Renewable Energy, University of Agder, Kristiansand, Norway

    Mohan L. Kolhe

  2. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Munesh C. Trivedi

  3. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Shailesh Tiwari

  4. Department of Computer Science, Indira Gandhi National Tribal University, Amarkantak, Madhya Pradesh, India

    Vikash Kumar Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singh, P., Ahuja, S., Jain, S. (2019). Latest Trends in Recommender Systems 2017. In: Kolhe, M., Trivedi, M., Tiwari, S., Singh, V. (eds) Advances in Data and Information Sciences . Lecture Notes in Networks and Systems, vol 39. Springer, Singapore. https://doi.org/10.1007/978-981-13-0277-0_17

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-0277-0_17

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-0276-3

  • Online ISBN: 978-981-13-0277-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation