Mining recommendations from the web

Guy Shani; Max Chickering; Christopher Meek

doi:10.1145/1454008.1454015

Mining recommendations from the web

Guy Shani, Max Chickering, Christopher Meek

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

35 Scopus citations

Abstract

In this paper we study the challenges and evaluate the effectiveness of data collected from the web for recommendations. We provide experimental results, including a user study, showing that our methods produce good recommendations in realistic applications. We propose a new evaluation metric, that takes into account the difficulty of prediction. We show that the new metric aligns well with the results from a user study.

Original language	English
Title of host publication	RecSys'08
Subtitle of host publication	Proceedings of the 2008 ACM Conference on Recommender Systems
Pages	35-42
Number of pages	8
DOIs	https://doi.org/10.1145/1454008.1454015
State	Published - 1 Dec 2008
Externally published	Yes
Event	2008 2nd ACM International Conference on Recommender Systems, RecSys'08 - Lausanne, Switzerland Duration: 23 Oct 2008 → 25 Oct 2008

Publication series

Name	RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems

Conference

Conference	2008 2nd ACM International Conference on Recommender Systems, RecSys'08
Country/Territory	Switzerland
City	Lausanne
Period	23/10/08 → 25/10/08

Keywords

Evaluation metrics
Recommender systems
Web mining

ASJC Scopus subject areas

Information Systems
Control and Systems Engineering

Access to Document

10.1145/1454008.1454015

Cite this

@inproceedings{d8b6ba4f44514748b9f09f4aef039438,

title = "Mining recommendations from the web",

abstract = "In this paper we study the challenges and evaluate the effectiveness of data collected from the web for recommendations. We provide experimental results, including a user study, showing that our methods produce good recommendations in realistic applications. We propose a new evaluation metric, that takes into account the difficulty of prediction. We show that the new metric aligns well with the results from a user study.",

keywords = "Evaluation metrics, Recommender systems, Web mining",

author = "Guy Shani and Max Chickering and Christopher Meek",

year = "2008",

month = dec,

day = "1",

doi = "10.1145/1454008.1454015",

language = "English",

isbn = "9781605580937",

series = "RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems",

pages = "35--42",

booktitle = "RecSys'08",

note = "2008 2nd ACM International Conference on Recommender Systems, RecSys'08 ; Conference date: 23-10-2008 Through 25-10-2008",

}

Shani, G, Chickering, M & Meek, C 2008, Mining recommendations from the web. in RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems. RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 35-42, 2008 2nd ACM International Conference on Recommender Systems, RecSys'08, Lausanne, Switzerland, 23/10/08. https://doi.org/10.1145/1454008.1454015

TY - GEN

T1 - Mining recommendations from the web

AU - Shani, Guy

AU - Chickering, Max

AU - Meek, Christopher

PY - 2008/12/1

Y1 - 2008/12/1

N2 - In this paper we study the challenges and evaluate the effectiveness of data collected from the web for recommendations. We provide experimental results, including a user study, showing that our methods produce good recommendations in realistic applications. We propose a new evaluation metric, that takes into account the difficulty of prediction. We show that the new metric aligns well with the results from a user study.

AB - In this paper we study the challenges and evaluate the effectiveness of data collected from the web for recommendations. We provide experimental results, including a user study, showing that our methods produce good recommendations in realistic applications. We propose a new evaluation metric, that takes into account the difficulty of prediction. We show that the new metric aligns well with the results from a user study.

KW - Evaluation metrics

KW - Recommender systems

KW - Web mining

UR - http://www.scopus.com/inward/record.url?scp=63449133370&partnerID=8YFLogxK

U2 - 10.1145/1454008.1454015

DO - 10.1145/1454008.1454015

M3 - Conference contribution

AN - SCOPUS:63449133370

SN - 9781605580937

T3 - RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems

SP - 35

EP - 42

BT - RecSys'08

T2 - 2008 2nd ACM International Conference on Recommender Systems, RecSys'08

Y2 - 23 October 2008 through 25 October 2008

ER -

Mining recommendations from the web

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this