CBRecSys 2014 : Workshop on New Trends in Content-based Recommender Systems

Страна: США

Город: Silicon Valley

Тезисы до: 21.07.2014

Даты: 06.10.14 — 11.10.14

Область наук: Компьютерные;

Е-мейл Оргкомитета: toine@hum.aau.dk

Организаторы: Aalborg University

 


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. In recent years, competitions like the Netflix Prize, CAMRA, and the Yahoo! Music KDD Cup 2011 have spurred on advances in collaborative filtering and how to utilize ratings and usage data. However, there are many domains 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 do not know if and how these data sources should be combined to provided the best recommendation performance.

The CBRecSys 2014 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation. This would include both recommendation in domains where textual content is abundant (e.g., books, news, scientific articles, jobs, educational resources, Web pages, etc.) as well as dedicated comparisons of content-based techniques with collaborative filtering in different domains. Other relevant topics related to content-based recommendations could include opinion mining for text/book recommendation, semantic recommendation, content-based recommendation to alleviate cold-start problems, as well as serendipity, diversity and cross-domain recommendation. To facilitate exploration of these topics the workshop will feature an in-workshop challenge on book recommendation.
 

Description & Objectives

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. In recent years, competitions like the Netflix Prize, CAMRA, and the Yahoo! Music KDD Cup 2011 have spurred on advances in collaborative filtering and how to utilize ratings and usage data. However, there are many domains 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 do not know if and how these data sources should be combined to provided the best recommendation performance.

The aim of the CBRecSys 2014 workshop is to bring together students, faculty, researchers and professionals from both academia and industry who are interested in addressing one or more aspects of content-based recommendation. This would include both recommendation in domains where textual content is abundant (e.g., books, news, scientific articles, jobs, educational resources, Web pages, etc.) as well as dedicated comparisons of content-based techniques with collaborative filtering in different domains. Other relevant topics related to content-based recommendations could include opinion mining for text/book recommendation, semantic recommendation, content-based recommendation to alleviate cold-start problems, as well as serendipity, diversity and cross-domain recommendation.

To facilitate exploration of these topics the workshop will feature an in-workshop challenge on book recommendation. For this challenge a large dataset containing user profiles with book ratings and tags and 2.8 million book descriptions with library metadata, user ratings, tags, and reviews from Amazon and LibraryThing will be made available. The rich textual nature of the task makes the challenge an excellent venue to revisit the questions of the benefits of content-based filtering vs. collaborative filtering and metadata vs. ratings information.
Topics of interest

We invite original contributions in a variety of areas related to content-based recommendation. Topics of interest include, but are not limited to, the following:

    Processing text reviews
        Estimating (implicit) ratings associated with text reviews
        Opinion mining and sentiment analysis of text reviews to support content-based recommendation
        Extracting user personality traits and factors from text reviews for recommendation
    Exploiting user generated contents
        Social tag-based recommender systems
        Mining microblogging data in content-based recommender systems
        Exploiting Semantic Web and Linked Open Data in content-based recommender systems
    Mining contextual data from content
        Extraction of contextual signals from text contents for recommendation
        Considering the time dimension in content-based recommendation
        Mood-based recommender systems
    Addressing limitations of recommender system
        Addressing the cold-start problem with content-based recommendation approaches
        Increasing diversity in content-based recommendations
        Providing novelty in content-based recommendations
    Developing novel recommendation approaches
        Hybrid strategies combining content-based and collaborative filtering recommendations
        Content-based approaches to cross-system and cross-domain recommendation
        Latent factor models for content-based and hybrid recommendation
 

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