The Distinction Between Search and Discovery Systems : A Scholarly Analysis

Mohini Thakkar

doi:10.32628/CSEIT241051049

Authors

Mohini Thakkar Notion Labs, USA Author

DOI:

https://doi.org/10.32628/CSEIT241051049

Keywords:

Information Retrieval, Search Systems, Discovery Systems, User Experience, Hybrid Information Access

Abstract

This comprehensive analysis explores the fundamental distinctions between search and discovery systems in the context of information retrieval and user interaction. The article examines the underlying principles, methodologies, and user experiences associated with each system, elucidating their unique roles in the modern digital landscape. By comparing key aspects such as user intent, interaction models, algorithmic foundations, and evaluation metrics, the article highlights the complementary strengths of these paradigms. The research also investigates emerging hybrid approaches that combine elements of both search and discovery, addressing evolving user expectations and the challenges of information overload. Through this in-depth exploration, the article contributes to the ongoing discourse on the future of information access and informs the development of more effective, user-centric systems capable of meeting diverse information needs in an increasingly complex digital ecosystem.

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References

A. Marr, "How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read," Forbes, May 21, 2018. [Online]. Available: https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/

R. W. White and R. A. Roth, "Exploratory Search: Beyond the Query-Response Paradigm," Synthesis Lectures on Information Concepts, Retrieval, and Services, vol. 1, no. 1, pp. 1-98, 2009. [Online]. Available: https://doi.org/10.2200/S00174ED1V01Y200901ICR003 DOI: https://doi.org/10.2200/S00174ED1V01Y200901ICR003

P. Brusilovsky and D. He, "Social Information Access: Systems and Technologies," Lecture Notes in Computer Science, vol. 10100, Springer, 2018. [Online]. Available: https://doi.org/10.1007/978-3-319-90092-6 DOI: https://doi.org/10.1007/978-3-319-90092-6

C. D. Manning, P. Raghavan, and H. Schütze, "Introduction to Information Retrieval," Cambridge University Press, 2008. [Online]. Available: https://nlp.stanford.edu/IR-book/

J. Mitra and C. Craswell, "An Introduction to Neural Information Retrieval," Foundations and Trends in Information Retrieval, vol. 13, no. 1, pp. 1-126, 2018. [Online]. Available: https://www.microsoft.com/en-us/research/uploads/prod/2017/06/fntir2018-neuralir-mitra.pdf DOI: https://doi.org/10.1561/1500000061

S. Büttcher, C. L. A. Clarke, and G. V. Cormack, "Information Retrieval: Implementing and Evaluating Search Engines," MIT Press, 2010. [Online]. Available: https://research.google/pubs/information-retrieval-implementing-and-evaluating-search-engines/#:~:text=Information%20Retrieval:%20Implementing%20and%20Evaluating%20Search%20Engines.%20Stefan

P. Adamopoulos and A. Tuzhilin, "On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected," ACM Transactions on Intelligent Systems and Technology, vol. 5, no. 4, pp. 1-32, 2014. [Online]. Available: https://doi.org/10.1145/2559952 DOI: https://doi.org/10.1145/2559952

J. Bobadilla, F. Ortega, A. Hernando, and A. Gutiérrez, "Recommender systems survey," Knowledge-Based Systems, vol. 46, pp. 109-132, 2013. [Online]. Available: https://www.sciencedirect.com/science/article/abs/pii/S0950705113001044#:~:text=This%20article%20provides%20an%20overview%20of%20recommender%20systems DOI: https://doi.org/10.1016/j.knosys.2013.03.012

X. Su and T. M. Khoshgoftaar, "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, vol. 2009, Article ID 421425, 2009. [Online]. Available: https://www.hindawi.com/journals/aai/2009/421425/ DOI: https://doi.org/10.1155/2009/421425

G. Marchionini, "Exploratory search: from finding to understanding," Communications of the ACM, vol. 49, no. 4, pp. 41-46, 2006. [Online]. Available: https://doi.org/10.1145/1121949.1121979 DOI: https://doi.org/10.1145/1121949.1121979

P. Morville and J. Callender, "Search Patterns: Design for Discovery," O'Reilly Media, 2010. [Online]. Available: https://books.google.co.in/books/about/Search_Patterns.html?id=LzgNHuKKGJIC&redir_esc=y#:~:text=In%20this%20provocative%20and%20inspiring%20book,%20you'll%20explore

D. Kotkov, S. Wang, and J. Veijalainen, "A survey of serendipity in recommender systems," Knowledge-Based Systems, vol. 111, pp. 180-192, 2016. [Online]. Available: https://doi.org/10.1016/j.knosys.2016.08.014 DOI: https://doi.org/10.1016/j.knosys.2016.08.014

J. Liu, E. Pedersen, and P. Dolan, "Personalized News Recommendation Based on Click Behavior," in Proceedings of the 15th International Conference on Intelligent User Interfaces (IUI '10), 2010, pp. 31-40. [Online]. Available: https://doi.org/10.1145/1719970.1719976 DOI: https://doi.org/10.1145/1719970.1719976

A. Hannak et al., "Measuring Personalization of Web Search," in Proceedings of the 22nd International Conference on World Wide Web (WWW '13), 2013, pp. 527-538. [Online]. Available: https://doi.org/10.1145/2488388.2488435 DOI: https://doi.org/10.1145/2488388.2488435

N. Tintarev and J. Masthoff, "Explaining Recommendations: Design and Evaluation," in Recommender Systems Handbook, F. Ricci, L. Rokach, and B. Shapira, Eds. Boston, MA: Springer, 2015, pp. 353-382. [Online]. Available: https://doi.org/10.1007/978-1-4899-7637-6_10 DOI: https://doi.org/10.1007/978-1-4899-7637-6_10