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Subject:
From:
Tanya Capawana <[log in to unmask]>
Reply To:
Tanya Capawana <[log in to unmask]>
Date:
Tue, 19 Jul 2011 10:08:35 -0400
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I am pleased to announce the publication of another issue of Foundations and Trends in Human-Computer Interaction:

Collaborative Filtering Recommender Systems
By: Michael D. Ekstrand, John T. Riedl, and Joseph A. Konstan (University of Minnesota, USA)
http://dx.doi.org/10.1561/1100000009

Description:

Recommender systems are an important part of the information and e-commerce ecosystem. They represent a powerful method for enabling users to filter through large information and product spaces. Nearly two decades of research on collaborative filtering have led to a varied set of algorithms and a rich collection of tools for evaluating their performance. Research in the field is moving in the direction of a richer understanding of how recommender technology may be embedded in specific domains. The differing personalities exhibited by different recommender algorithms show that recommendation is not a one-size-fits-all problem. Specific tasks, information needs, and item domains represent unique problems for recommenders, and design and evaluation of recommenders needs to be done based on the user tasks to be supported. Effective deployments must begin with careful analysis of prospective users and their goals. Based on this analysis, system designers have a host of options for the choice of algorithm and for its embedding in the surrounding user experience.

Collaborative Filtering Recommender Systems provides a broad overview of the current state of collaborative filtering research. It discusses the core algorithms for collaborative filtering and traditional means of measuring their performance against user rating data sets. It then moves on to discuss building reliable, accurate data sets; understanding recommender systems in the broader context of user information needs and task support; and the interaction between users and recommender systems.

Collaborative Filtering Recommender Systems provides both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.

Contents:

1: Introduction 2: Collaborative Filtering Methods 3: Evaluating Recommender Systems 4: Building the Data Set 5: User Information Needs 6: User Experience 7: Conclusion and Resources.  References.

View all FnT HCI issues here:
www.nowpublishers.com/hci<http://www.nowpublishers.com/hci>


Tanya Capawana
now -- the essence of knowledge
P.O. Box 1024
Hanover, MA 02339 USA
t: +1-781-871-0245
f: +1-781-871-6172
em:  [log in to unmask]<mailto:[log in to unmask]>
www.nowpublishers.com<http://www.nowpublishers.com>

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