Skip to main content
SpringerLink
Log in

A scientometric review of emerging trends and new developments in recommendation systems

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Recommendation systems have drawn an increasingly broad range of interest since early 1990s. Recently, a search with the query of “recommendation systems” on Google Scholar found over 32,000 documents. As the volume of the literature grows rapidly, thus, a systematic review of the diverse research field and its current challenges becomes essential. This study surveys the literature of recommendation systems between 1992 and 2014. The overall structure of its intellectual landscape is illustrated in terms of thematic concentrations of co-cited references and emerging trends of bursting keywords and citations to references. Our review is based on two sets of bibliographic records retrieved from the Web of Science. The core dataset, obtained through a topic search, contains 2573 original research and review articles. The expanded dataset, consisting of 12,916 articles and reviews, was collected by citation expansion. We identified intellectual landscapes, landmark articles and bursting keywords of the domain in core and broader perspectives. We found that a number of landmark studies in 1980s and 1990s and techniques such as LDA, pLSI, and matrix factorization have tremendously influenced the development of the recommendation systems research. Furthermore, our study reveals that the field of recommendation systems is still evolving and developing. Thematic trends in recommendation systems research reflect the development of a wide variety of information systems such as the World Wide Web and social media. Finally, collaborative filtering has been a dominant research concept of the field. Recent emerging topics focus on enhancing the effectiveness of recommendation systems by addressing diverse challenges.

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

Similar content being viewed by others

References

  • Adomavicius, G. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749.

    Article  Google Scholar 

  • Agrawal, R., Imieliński, T., & Swam, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207–216.

    Article  Google Scholar 

  • Aharon, M., Elad, M., & Bruckstein, A. (2006). K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11), 4311–4322.

    Article  Google Scholar 

  • Argyriou, A., Evgeniou, T., & Pontil, M. (2008). Convex multi-task feature learning. Machine Learning, 73(3), 243–272.

    Article  Google Scholar 

  • Baeza-Yates, R. A., & Ribeiro-Neto, B. (1999). Modern information retrieval. Boston, MA: Addison-Wesley Longman Publishing Co., Inc.

    Google Scholar 

  • Balabanovic, M., & Shoham, Y. (1997). Fab: Content-based, collaborative recommendation”. Communications of the ACM, 40(3), 66–72.

    Article  Google Scholar 

  • Basu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. In AAAI ‘98/IAAI ‘98 proceedings of the fifteenth national/tenth conference on artificial intelligence/innovative applications of artificial intelligence, pp. 714–720.

  • Beel, J., Langer, S., Genzmehr, M., Gipp, B., Breitinger, C., & Nürnberger, A. (2013). Research paper recommender system evaluation: a quantitative literature survey. In RepSys ‘13 proceedings of the international workshop on reproducibility and replication in recommender systems evaluation, pp. 15–22.

  • Bhowmik, R. (2008). Keyword extraction from abstracts and titles. In Proceedings of the IEEE southeastcon, pp. 610–617.

  • Billsus, D., & Pazzani, M. J. (1998). Learning collaborative information filters. In ICML ‘98 proceedings of the fifteenth international conference on machine learning, pp. 46–54.

  • Blei, D., Ng, A., & Jordan. M. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

  • Bobadilla, J., Ortega, F., Hernando, A., & GutiéRrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132.

    Article  Google Scholar 

  • Bobadilla, J., Serradilla, F., & Hernando, A. (2009). Collaborative filtering adapted to recommender systems of e-learning. Knowledge-Based Systems, 22(4), 261–265.

    Article  Google Scholar 

  • Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In UAI’98 proceedings of the fourteenth conference on uncertainty in artificial intelligence, pp. 43–52.

  • Buades, A., Coll, B., & Morel, J. M. (2005). A review of image denoising algorithms, with a new one. Multiscale Modeling & Simulation, 4(2), 490–530.

    Article  MATH  MathSciNet  Google Scholar 

  • Burke, R. (2007). Hybrid web recommender systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web: Methods and strategies of web personalization (pp. 377–408). Berlin/Heidelberg: Springer.

  • Burke, R., Hammond, K., & Young, B. (1997). The FindMe approach to assisted browsing. IEEE Expert, 12(4), 32–40.

  • Campos, P. G., Díez, F., & Cantador, I. (2014). Time-aware recommender systems: A comprehensive survey and analysis of existing evaluation protocols. User Modeling and User-Adapted Interaction, 24(1–2), 67–119.

    Article  Google Scholar 

  • Candès, E. J., & Recht, B. (2009). Exact matrix completion via convex optimization. Foundations of Computational Mathematics, 9(6), 717–772.

    Article  MATH  MathSciNet  Google Scholar 

  • Candès, E. J., Romberg, J., & Tao, T. (2006). Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489–509.

    Article  MATH  Google Scholar 

  • Candès, E. J., & Tao, T. (2010). The power of convex relaxation: Near-optimal matrix completion. IEEE Transactions on Information Theory, 56(5), 2053–2080.

    Article  Google Scholar 

  • Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377.

    Article  Google Scholar 

  • Chen, C., Dubin, R., & Kim, M. C. (2014a). Emerging trends and new developments in regenerative medicine: A scientometric update (2000–2014). Expert Opinion on Biological Therapy, 14(9), 1295–1317.

    Article  Google Scholar 

  • Chen, C., Dubin, R., & Kim, M. C. (2014b). Orphan drugs and rare diseases: A scientometric review (2000–2014). Expert Opinion on Orphan Drugs, 2(7), 709–724.

    Article  Google Scholar 

  • Chen, C., Hu, Z., Liu, S., & Tseng, H. (2012). Emerging trends in regenerative medicine: A scientometric analysis in CiteSpace. Expert Opinions on Biological Therapy, 12(5), 593–608.

    Article  Google Scholar 

  • Chen, C., Ibekwe-SanJuan, F., & Hou, J. (2010). The structure and dynamics of co-citation clusters: A multiple-perspective co-citation analysis. Journal of the American Society for Information Science and Technology, 61(7), 1386–1409.

    Article  Google Scholar 

  • Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345–354.

    Article  Google Scholar 

  • Dabov, K., Foi, A., Katkovnik, V., & Egiazarian, K. (2007). Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing, 16(8), 2080–2095.

    Article  MathSciNet  Google Scholar 

  • Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407.

    Article  Google Scholar 

  • Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306.

    Article  MATH  MathSciNet  Google Scholar 

  • Edmunson, H. P. (1969). New methods on automatic exracting. Journal of the ACM, 16(2), 264–285.

    Article  Google Scholar 

  • Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations over learned dictionaries. IEEE Transactions on Image Processing, 15(12), 3736–3745.

    Article  MathSciNet  Google Scholar 

  • Fontes, A. G., Gama-Rodrigues, A. C., Gama-Rodrigues, E. F., Sales, M. V. S., Costa, M. G., & Machado, R. C. R. (2014). Nutrient stocks in litterfall and litter in cocoa agroforests in Brazil. Plant and Soil, 383(1–2), 313–335.

    Article  Google Scholar 

  • Garfield, E. (1979). Citation indexing: Its Theory and applications in science, technology, and humanities. New York: Wiley.

    Google Scholar 

  • Goh, K. J. (2004). Fertilizer recommendation systems for oil palm: Estimating the fertilizer rates. In Proceedings of MOSTA best practices workshops: Agronomy and crop management, pp. 235–268.

  • Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12), 61–70.

    Article  Google Scholar 

  • Good, N., Schafer, J. B., Konstan, J. A., Borchers A., Sarwar, B., Herlocker, J. L., & Riedl, J. (1999). Combining collaborative filtering with personal agents for better recommendation. In AAAI ‘99/IAAI ‘99 proceedings of the sixteenth national conference on artificial intelligence and the eleventh innovative applications of artificial intelligence conference innovative applications of artificial intelligence, pp. 439–446.

  • Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In: SIGIR ‘99 Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval, pp. 230–237.

  • Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5–53.

    Article  Google Scholar 

  • Herrera-Viedmaa, E., & López-Herrera, A. G. (2010). A review on information accessing systems based on fuzzy linguistic modelling. International Journal of Computational Intelligence Systems, 3(4), 420–437.

    Article  Google Scholar 

  • Hofmann, T. (1999). Probabilistic latent semantic indexing. In SIGIR ‘99 proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval, pp. 50–57.

  • Johnson, C. R. (Ed.). (1990) Matrix completion problems: A survey. In Matrix theory and applications (pp. 171–198). American Mathematical Society.

  • Jøsang, A., Ismail, R., & Boyd, C. (2007). A survey of trust and reputation systems for online service provision. Decision Support Systems, 43(2), 618–644.

    Article  Google Scholar 

  • Klasnja-Milicevic, A., Vesin, B., Ivanovic, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885–899.

    Article  Google Scholar 

  • Kobsa, A., Koenemann, J., & Pohl, W. (2001). Personalised hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review, 16(2), 111–155.

    Article  MATH  Google Scholar 

  • Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., & Riedl, J. (1997). GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM, 40(3), 77–87.

    Article  Google Scholar 

  • Koren, Y. (2008). Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 426–434.

  • Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37.

    Article  Google Scholar 

  • Lee, S. K., Cho, Y. H., & Kim, S. H. (2010). Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Information Sciences, 180(11), 2142–2155.

    Article  Google Scholar 

  • Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007). The dynamics of viral marketing. ACM Transactions on the Web (TWEB), 1(1), 5.

    Article  Google Scholar 

  • Liu, J., Li, S., Ouyang, Z., Tam, C., & Chen, X. (2008). Ecological and socioeconomic effects of China’s policies for ecosystem services. Proceedings of the National Academy of Sciences of the United States of America, 105(28), 9477–9482.

    Article  Google Scholar 

  • Lü, L., Medo, M., Yeung, C. H., Zhang, Y. C., Zhang, Z. K., & Zhou, T. (2012). Recommender systems. Physics Reports, 519(1), 1–49.

    Article  Google Scholar 

  • Ma, S., Goldfarb, D., & Chen, L. (2011a). Fixed point and Bregman iterative methods for matrix rank minimization. Mathematical Programming, 128(1–2), 321–353.

    Article  MATH  MathSciNet  Google Scholar 

  • Ma, H., Zhou, T. C., Lyu, M. R. & King, I. (2011). Improving recommender systems by incorporating social contextual information. ACM Transactions on Information Systems, 29(2), Article 9.

  • Maes, P. (1994). Agents that reduce work and information overload. Communications of the ACM, 37(7), 30–40.

    Article  Google Scholar 

  • Mahmood, T., & Ricci, F. (2009). Improving recommender systems with adaptive conversational strategies. In HT '09 Proceedings of the 20th ACM conference on Hypertext and hypermedia, pp. 73–82.

  • Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. New York, NY: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Mobasher, B., Cooley, R., & Srivastava, J. (2000). Automatic personalization based on Web usage mining. Communications of the ACM, 43(8), 142–151.

    Article  Google Scholar 

  • Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185–200.

    Google Scholar 

  • Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research. Expert Systems with Applications, 39(11), 10059–10072.

    Article  Google Scholar 

  • Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5–6), 393–408.

    Article  Google Scholar 

  • Recht, B., Fazel, M., & Parrilo, P. A. (2010). Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM Review, 52(3), 471–501.

    Article  MATH  MathSciNet  Google Scholar 

  • Resnick, P., Iakovou, N., Sushak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. In CSCW ‘94 Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175–186.

  • Resnick, P., & Varian, H. R. (1997). Recommender systems. Communications of the ACM, 40(3), 56–58.

    Article  Google Scholar 

  • Ricci, F., Rokach, L., Shapira, B., & Kantor, P. B. (Eds.). (2011). Recommender systems handbook. New York: Springer.

    MATH  Google Scholar 

  • Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. New York, NY: McGraw-Hill Inc.

    MATH  Google Scholar 

  • Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2000). Analysis of recommendation algorithms for e-commerce. In EC ‘00 Proceedings of the 2nd ACM conference on electronic commerce, pp 158–167.

  • 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.

  • Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. (2007). Collaborative filtering recommender systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web: Methods and strategies of web personalization (pp. 291–324). Berlin/Heidelberg: Springer.

  • Schafer, J. B., Konstan, J. A., & Riedl, J. (2001). E-commerce recommendation applications. Data Mining and Knowledge Discovery, 5(1/2), 115–153.

    Article  MATH  Google Scholar 

  • Serrano-Guerrero, J., Herrera-Viedma, E., Olivas, J. A., Cerezo, A., & Romero, F. P. (2011). A google wave-based fuzzy recommender system to disseminate information in University Digital Libraries 2.0. Information Sciences, 181(9), 1503–1516.

    Article  Google Scholar 

  • Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating ‘Word of Mouth’. In CHI ‘95 proceedings of the SIGCHI conference on human factors in computing systems, pp. 210–217.

  • Srivastava, J., Cooley, R., Deshpande, M., & Tan, P. N. (2000). Web usage mining: Discovery and applications of usage patterns from web data. ACM SIGKDD Explorations Newsletter, 1(2), 12–23.

    Article  Google Scholar 

  • Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 4.

    Article  Google Scholar 

  • Tan, S., Bu, J., Chen, Ch. & He, X. (2011). Using rich social media information for music recommendation via hypergraph model. ACM Transactions on Multimedia Computing, Communications, and Applications, 7(1): Article 7.

  • Terveen, L., Hill, W., Amento, B., McDonald, D., & Creter, J. (1997). PHOAKS: A system for sharing recommendations. Communications of the ACM, 40(3), 59–62.

    Article  Google Scholar 

  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58, 267–288.

  • Tóth, G., Guicharnauda, R.-A., Tóth, B., & Hermann, T. (2014). Phosphorus levels in croplands of the European Union with implications for P fertilizer use. European Journal of Agronomy, 55, 42–52.

    Article  Google Scholar 

  • Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4), 600–612.

    Article  Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442.

    Article  Google Scholar 

  • Wu, J., Chen, L., Feng, Y., Zheng, Z., Zhou, M. C., & Wu, Z. (2013). Predicting quality of service for selection by neighborhood-based collaborative filtering. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43(2), 428–439.

    Article  Google Scholar 

  • Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209.

    Google Scholar 

  • Zheng, Z., Ma, H., Lyu, M. R., & King, I. (2013). Collaborative web service qos prediction via neighborhood integrated matrix factorization. IEEE Transcations on Services Computing, 6(3), 289–299.

    Article  Google Scholar 

  • Zhou, T., Kiet, H. A., Kim, B. J., Wang, B. H., & Holme, P. (2008). Role of activity in human dynamics. Europhysics Letters, 82(2), 28002.

    Article  Google Scholar 

  • Zhou, T., Kuscsik, Z., Liu, J. G., Medo, M., Wakeling, J. R., & Zhang, Y. C. (2010). Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences of the United States of America, 107(10), 4511–4515.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Drexel University, Philadelphia, PA, USA

    Meen Chul Kim & Chaomei Chen

Authors
  1. Meen Chul Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chaomei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Meen Chul Kim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, M.C., Chen, C. A scientometric review of emerging trends and new developments in recommendation systems. Scientometrics 104, 239–263 (2015). https://doi.org/10.1007/s11192-015-1595-5

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-015-1595-5

Keywords

Search

Navigation