Second Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2015)

Toine Bogers; Marijn Koolen

doi:10.1145/2792838.2798718

Second Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2015)

Toine Bogers, Marijn Koolen

Aalborg University

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Communication

Abstract

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2015 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation.

Original language	English
Title of host publication	RecSys ‘15: Proceedings of the 2015 ACM Conference on Recommender Systems
Number of pages	2
Publisher	Association for Computing Machinery
Publication date	16 Sept 2015
Pages	339-340
ISBN (Electronic)	978-1-4503-3692-5
DOIs	https://doi.org/10.1145/2792838.2798718
Publication status	Published - 16 Sept 2015
Externally published	Yes
Event	RecSys 2015: 9th ACM Conference on Recommender Systems - Vienna, Austria Duration: 16 Sept 2015 → 20 Sept 2015

Conference

Conference	RecSys 2015
Country/Territory	Austria
City	Vienna
Period	16/09/2015 → 20/09/2015

Keywords

Content-Based Recommendation
Collaborative Filtering
Metadata Integration
Recommendation Performance
Information Science

Access to Document

10.1145/2792838.2798718

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http://dl.acm.org/citation.cfm?doid=2792838.2798718

Cite this

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title = "Second Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2015)",

abstract = "While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2015 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation.",

keywords = "Content-Based Recommendation, Collaborative Filtering, Metadata Integration, Recommendation Performance, Information Science, Content-Based Recommendation, Collaborative Filtering, Metadata Integration, Recommendation Performance, Information Science",

author = "Toine Bogers and Marijn Koolen",

year = "2015",

month = sep,

day = "16",

doi = "10.1145/2792838.2798718",

language = "English",

pages = "339--340",

booktitle = "RecSys {\textquoteleft}15: Proceedings of the 2015 ACM Conference on Recommender Systems",

publisher = "Association for Computing Machinery",

address = "United States",

note = "RecSys 2015 : 9th ACM Conference on Recommender Systems ; Conference date: 16-09-2015 Through 20-09-2015",

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T1 - Second Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2015)

AU - Bogers, Toine

AU - Koolen, Marijn

PY - 2015/9/16

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N2 - While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2015 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation.

AB - While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2015 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation.

KW - Content-Based Recommendation

KW - Collaborative Filtering

KW - Metadata Integration

KW - Recommendation Performance

KW - Information Science

KW - Content-Based Recommendation

KW - Collaborative Filtering

KW - Metadata Integration

KW - Recommendation Performance

KW - Information Science

U2 - 10.1145/2792838.2798718

DO - 10.1145/2792838.2798718

M3 - Article in proceedings

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BT - RecSys ‘15: Proceedings of the 2015 ACM Conference on Recommender Systems

PB - Association for Computing Machinery

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Y2 - 16 September 2015 through 20 September 2015

ER -

Second Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2015)

Abstract

Conference

Keywords

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Cite this