Efficiency Evaluation of Recommender Systems: Study of Existing Problems and Possible Extensions

Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 9)


Recommender systems can help people to choose the desired product from a choice of various products. But there are various issues with the existing recommender systems. Thus, new systems which can efficiently recommend the most appropriate item to users based on their preferences are in demand. As a step towards providing the users with such a system, we present a general overview of the recommender systems in this paper. This paper also proposes potential solutions to the different problems which are found in current recommendation methods. These extensions to the current recommendation systems can improve their capabilities and make them appropriate for a broader area of applications. These potential extensions include integration of contextual information into the recommendation method, an improvement in understanding of items and users, developing less intrusive recommendation approaches, utilization of multi-criteria ratings, and providing more flexible types of recommendations.


Contextual information Extensions to recommender systems Multi-criteria ratings Multidimensionality Recommender systems 


  1. 1.
    Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. Recommender Systems Handbook, Springer, pp 1–35CrossRefMATHGoogle Scholar
  2. 2.
    Kim HW, Han K, YI MY, Cho J, Hong J (2012) Moviemine: personalized movie content search by utilizing user comments. J IEEE Trans Consum Electron 58Google Scholar
  3. 3.
    Huete JF, Fernandez JM, Campos LM, Rueda-Morales MA (2012) Using past-prediction accuracy in recommender systems. J Inf Sci 199(12):78–92CrossRefGoogle Scholar
  4. 4.
    Chen T, He L (2009) Collaborative filtering based on demographic attribute vector. In: Proceedings of international conference on future computer and communication. IEEEGoogle Scholar
  5. 5.
    Burke R (2000) Knowledge-based recommender systems. Encyclop Lib Inf Syst 69(supplement 32):175–186Google Scholar
  6. 6.
    Liu DR, Lai CH, Lee WJ (2009) A hybrid of sequential rules and collaborative filtering for product recommendation. J Inf Sci 179:3505–3519CrossRefGoogle Scholar
  7. 7.
    Nilashi M, Ibrahim OB, Ithnin N (2014) Hybrid recommendation approaches for multi-criteria collaborative filtering. J Expert Syst Appl 41:3879–3900CrossRefGoogle Scholar
  8. 8.
    Liu Q, Chen E, Xiong H, Ge Y, Li Z, Wu X (2014) A cocktail approach for travel package recommendation. J IEEE Trans Knowl Data Eng 26Google Scholar
  9. 9.
    Gavalas D, Konstantopoulos C, Mastakas K, Pantziou G (2014) Mobile recommender systems in tourism. J Netw Comput Appl 39:319–333CrossRefGoogle Scholar
  10. 10.
    Hyung Z, Lee K, Lee K (2014) Music recommendation using text analysis on song requests to radio stations. J Expert Syst Appl 41:2608–2618CrossRefGoogle Scholar
  11. 11.
    Lin C, Xie R, Guan X, Li L, Li T (2014) Personalized news recommendation via implicit social experts. J Inf Sci 254:1–18CrossRefGoogle Scholar
  12. 12.
    Agarwal V, Bharadwaj KK (2012) A collaborative filtering framework for fiends recommendation in social networks based on interaction intensity and adaptive user similarity. SpringerGoogle Scholar
  13. 13.
    Esslimani I, Brun A, Boyer A (2010) Densifying a behavioural recommender system by social networks link prediction methods. SpringerGoogle Scholar
  14. 14.
    Arias JP, Vilas AF, Redondo RP (2012) Recommender systems for social web. Springer (2012)Google Scholar
  15. 15.
    Balabanovic M, Shoham Y (2007) Fab: content-based, collaborative recommendation. ACM 40:66–72Google Scholar
  16. 16.
    Shardanand U, Maes P (2005) Social information filtering: algorithms for automating ‘word of mouth’. In: Conference of human factors in computing systemsGoogle Scholar
  17. 17.
    Linden G, Smith B, York J (2003) recommendations: item-to-item collaborative filtering. IEEE Int ComputGoogle Scholar
  18. 18.
    Miller BN, Albert I, Lam SK, Konstan JA, Riedl J (2003) Movielens unplugged: experiences with an occasionally connected recommender system. In: International conference of intelligent user interfacesGoogle Scholar
  19. 19.
    Billsus D, Brunk CA, Evans C, Gladish B, Pazzani M (2002) Adaptive interfaces for ubiquitous web access. ACM 45:34–38CrossRefGoogle Scholar
  20. 20.
    Melville P, Mooney RJ, Nagarajan R (2002) Content-boosted collaborative filtering for improved recommendations. In: 18th national conference of artificial intelligenceGoogle Scholar
  21. 21.
    Sheth B, Maes P (2003) Evolving agents for personalized information filtering. In: Ninth international conference of artificial intelligence for applications. IEEE (2003)Google Scholar
  22. 22.
    Zhang Y, Callan J, Minka T (2002) Novelty and redundancy detection in adaptive filtering. In: 25th annual international ACM SIGIR conference, pp 81–88Google Scholar
  23. 23.
    Rashid AM, Albert I, Cosley D, Lam SK, McNee SM, Konstan JA, Riedl J (2002) Getting to know you: learning new user preferences in recommender systems. In: International conference of intelligent user interfacesGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Amity UniversityNoidaIndia

Personalised recommendations