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Eng."],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>In recent years, while algorithm-driven recommendation applications have seen widespread use, their negative impacts have also increasingly raised concerns. To gain a more comprehensive understanding of the impact of different recommendation algorithms, we explored the phenomenon of information cocoons, where users are enveloped by homogenized recommended content, in different algorithm-driven recommender systems. We simulated long-term interactions between users and various algorithm-driven recommender systems, trying to recreate multi-stage recommendation scenarios under the influence of complex factors, and explored whether and to what extent users would fall into information cocoons while analyzing the underlying reasons from the perspective of algorithms. We conducted simulation experiments on two real-world recommendation datasets from different fields. The results show that information cocoons is prevalent across different algorithm-driven recommender systems, and the extent of its occurrence varies. Diversity-oriented recommendations can help alleviate information cocoons but are limited in effectiveness. The ability of diversity-aware re-ranking frameworks to alleviate information cocoons is influenced by the basic recommendation models. Not only considering the diversity of the current recommendation list but also the similarity between items and users\u2019 historical consumption content, we proposed a simple and lightweight re-ranking framework called ICMF. Compared to other re-ranking methods, ICMF avoids an average of 12.48% of users encountering homogenized recommended content.<\/jats:p>","DOI":"10.1007\/s41019-025-00288-9","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T04:41:18Z","timestamp":1747629678000},"page":"495-514","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Exploratory Study on Information Cocoon in Recommender Systems"],"prefix":"10.1007","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6509-1651","authenticated-orcid":false,"given":"Lin","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2820-5273","authenticated-orcid":false,"given":"Yahong","family":"Lian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3616-1992","authenticated-orcid":false,"given":"Haixia","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5715-5092","authenticated-orcid":false,"given":"Chunyao","family":"Song","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5876-6856","authenticated-orcid":false,"given":"Xiaojie","family":"Yuan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,19]]},"reference":[{"key":"288_CR1","doi-asserted-by":"crossref","unstructured":"Anderson A, Maystre L, Anderson I et\u00a0al (2020) Algorithmic effects on the diversity of consumption on Spotify. 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We affirm that there are no competing interests relevant to the content of this paper. Furthermore, we have no affiliations with or involvement in any organization or entity that has a conflicting interest in the findings, materials, or conclusions presented in this manuscript. We also confirm that we do not hold any financial or proprietary interests related to the subject matter discussed in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}