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Mining and representing recommendations in actively evolving recommender systems

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Standard

Mining and representing recommendations in actively evolving recommender systems. / Assent, Ira.

International Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA. 2010. p. 282-285.

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Harvard

Assent, I 2010, Mining and representing recommendations in actively evolving recommender systems. in International Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA. pp. 282-285. https://doi.org/10.1109/ICDEW.2010.5452714

APA

Assent, I. (2010). Mining and representing recommendations in actively evolving recommender systems. In International Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA (pp. 282-285) https://doi.org/10.1109/ICDEW.2010.5452714

CBE

Assent I. 2010. Mining and representing recommendations in actively evolving recommender systems. In International Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA. pp. 282-285. https://doi.org/10.1109/ICDEW.2010.5452714

MLA

Assent, Ira "Mining and representing recommendations in actively evolving recommender systems". International Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA. 2010, 282-285. https://doi.org/10.1109/ICDEW.2010.5452714

Vancouver

Assent I. Mining and representing recommendations in actively evolving recommender systems. In International Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA. 2010. p. 282-285 https://doi.org/10.1109/ICDEW.2010.5452714

Author

Assent, Ira. / Mining and representing recommendations in actively evolving recommender systems. International Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA. 2010. pp. 282-285

Bibtex

@inproceedings{f3549b86dd5f4127b9cc326301180571,
title = "Mining and representing recommendations in actively evolving recommender systems",
abstract = "Recommender systems provide an automatic means of filtering out interesting items, usually based on past similarity of user ratings. In previous work, we have suggested a model that allows users to actively build a recommender network. Users express trust, obtain transparency, and grow (anonymous) recommender connections. In this work, we propose mining such active systems to generate easily understandable representations of the recommender network. Users may review these representations to provide active feedback. This approach further enhances the quality of recommendations, especially as topics of interest change over time. Most notably, it extends the amount of control users have over the model that the recommender network builds of their interests.",
author = "Ira Assent",
year = "2010",
doi = "10.1109/ICDEW.2010.5452714",
language = "English",
isbn = "978-1-4244-6522-4",
pages = "282--285",
booktitle = "International Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA",

}

RIS

TY - GEN

T1 - Mining and representing recommendations in actively evolving recommender systems

AU - Assent, Ira

PY - 2010

Y1 - 2010

N2 - Recommender systems provide an automatic means of filtering out interesting items, usually based on past similarity of user ratings. In previous work, we have suggested a model that allows users to actively build a recommender network. Users express trust, obtain transparency, and grow (anonymous) recommender connections. In this work, we propose mining such active systems to generate easily understandable representations of the recommender network. Users may review these representations to provide active feedback. This approach further enhances the quality of recommendations, especially as topics of interest change over time. Most notably, it extends the amount of control users have over the model that the recommender network builds of their interests.

AB - Recommender systems provide an automatic means of filtering out interesting items, usually based on past similarity of user ratings. In previous work, we have suggested a model that allows users to actively build a recommender network. Users express trust, obtain transparency, and grow (anonymous) recommender connections. In this work, we propose mining such active systems to generate easily understandable representations of the recommender network. Users may review these representations to provide active feedback. This approach further enhances the quality of recommendations, especially as topics of interest change over time. Most notably, it extends the amount of control users have over the model that the recommender network builds of their interests.

U2 - 10.1109/ICDEW.2010.5452714

DO - 10.1109/ICDEW.2010.5452714

M3 - Article in proceedings

SN - 978-1-4244-6522-4

SP - 282

EP - 285

BT - International Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA

ER -