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User System Interface CSC 8570-001

User System Interface CSC 8570-001

User System Interface CSC 8570-001 Spring 2009 Instructor: Robert E. Beck Introductions (1) Information sheet Questionnaire: student information Questions Who invented the computer mouse? When? Where? What battery operated devices do you have with you tonight?

By issac
(272 views)

Agent Technology for e-Commerce

Agent Technology for e-Commerce

Agent Technology for e-Commerce. Chapter 6: Recommender Systems Maria Fasli http://cswww.essex.ac.uk/staff/mfasli/ATe-Commerce.htm. Recommender systems: The problem. Too much information: information overload – consumers have too many options

By libitha
(151 views)

Item Based Collaborative Filtering Recommendation Algorithms

Item Based Collaborative Filtering Recommendation Algorithms

Item Based Collaborative Filtering Recommendation Algorithms. Badrul Sarvar, George Karypis, Joseph Konstan & John Riedl. http://citeseer.nj.nec.com/sarwar01itembased.html By Vered Kunik 025483819. Article Layout -. Analyze different item-based recommendation generation algorithms.

By skah
(485 views)

From swarming to collaborative filtering.

From swarming to collaborative filtering.

From swarming to collaborative filtering. http:// www.csml.ucl.ac.uk/images/Netflix_Prize.jpg. Informatics: a possible parsing. Computer Science. STOP! ;-). b. b. b. a. a. a. b. a. b. b. a. a. b. a. b. Psilophyta/Psilotum. Let’s Observe Nature!. What do you see?

By gala
(311 views)

Lessons from the Netflix Prize

Lessons from the Netflix Prize

Lessons from the Netflix Prize. Robert Bell AT&T Labs-Research In collaboration with Chris Volinsky, AT&T Labs-Research & Yehuda Koren, Yahoo! Research. “We’re quite curious, really. To the tune of one million dollars.” – Netflix Prize rules.

By derry
(301 views)

Collaborative Filtering and Recommender Systems

Collaborative Filtering and Recommender Systems

Collaborative Filtering and Recommender Systems. Brian Lewis INF 385Q Knowledge Management Systems November 10, 2005. Presentation Outline. Collaborative filtering and recommender systems defined Novel example Readings - overview & key concepts Glance, Arregui & Dardenne (1997)

By brosh
(177 views)

Chapter 12 (Section 12.4) : Recommender Systems

Chapter 12 (Section 12.4) : Recommender Systems

Chapter 12 (Section 12.4) : Recommender Systems. Second edition of the book, coming soon. Road Map. Introduction Content-based recommendation Collaborative filtering based recommendation K-nearest neighbor Association rules Matrix factorization. Introduction.

By suzy
(332 views)

Realtime BI - Online Targeted Advertising

Realtime BI - Online Targeted Advertising

Zhangxi Lin ISQS 3358 Texas Tech University. Realtime BI - Online Targeted Advertising . Internet-based Targeted Marketing Targeted Banner Advertising Online Recommender Systems. Agenda. Internet-based Targeted Marketing.

By kimn
(103 views)

GAP Analysis – Data and Information

GAP Analysis – Data and Information

GAP Analysis – Data and Information . Technical Challenges. SUMMARY OF THE STATE OF THE ART. Research Areas. CURRENT LIMITATIONS.

By nevaeh
(102 views)

Temporal Diversity in Recommender Systems

Temporal Diversity in Recommender Systems

Temporal Diversity in Recommender Systems. Neal Lathia , Stephen Hailes , Licia Capra , and Xavier Amatriain SIGIR 2010 April 6, 2011 Hyunwoo Kim. Outline. Introduction Why Temporal Diversity? Evaluating for Diversity Promoting Temporal Diversity Conclusion. Introduction.

By moke
(73 views)

Recommendation Websites StumbleUpon and Pandora Radio among many others.

Recommendation Websites StumbleUpon and Pandora Radio among many others.

Recommendation Websites StumbleUpon and Pandora Radio among many others. How personalized can a recommendation website be? . What is the recommendation website/search engines?.

By liuz
(140 views)

ACM Recommender Systems 2012

ACM Recommender Systems 2012

ACM Recommender Systems 2012. Dublin 9-13 September Kompan,Kramár,Zeleník,Bieliková. O čom?. Pracovné dielne: Recommender Systems and the Social Web ( RSWeb ) Human Decision Making in Recommender Systems Context -Aware Recommender Systems (CARS )

By thisbe
(122 views)

Item Based Collaborative Filtering Recommendation Algorithms

Item Based Collaborative Filtering Recommendation Algorithms

Item Based Collaborative Filtering Recommendation Algorithms. Week 7 - 2. Introduction. Recommender Systems – Apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services, usually during a live interaction

By alisa
(279 views)

Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender Systems

Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender Systems

IDEAS 2011 Lisbon 21-23 September. Mining Semantic Data for Solving First-rater and Cold-start Problems in Recommender Systems. Data Mining Research Group http://mida.usal.es. María N. Moreno, Saddys Segrera , Vivian F. López, M. Dolores Muñoz and Ángel Luis Sánchez. Department of

By liko
(123 views)

Machine learning approaches to Attack Detection in Collaborative Recommender Systems

Machine learning approaches to Attack Detection in Collaborative Recommender Systems

Machine learning approaches to Attack Detection in Collaborative Recommender Systems. Runa Bhaumik College of Computing and Digital Media DePaul University Chicago, Illinois. Outline. Vulnerabilities in collaborative recommendation Background, types of attacks and examples

By maitland
(102 views)

Paradigm in Human Computer Interaction

Paradigm in Human Computer Interaction

Paradigm in Human Computer Interaction. Dema Alorini. Paradigms of Interaction. New computing technologies arrive, creating a new perception of the human—computer relationship. We can trace some of these shifts in the history of interactive technologies. Examples of Interaction Paradigms .

By mea
(1173 views)

TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation

TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation

TrustWalker: A Random Walk Model for Combining Trust-based and Item-based Recommendation. Mohsen Jamali & Martin Ester Simon Fraser University, Vancouver, Canada. Introduction TrustWalker Single Random Walk Recommendation Matrix Notation Properties of TrustWalker

By booker
(187 views)

CONCLUSION & FUTURE WORK

CONCLUSION & FUTURE WORK

UIMaP : Mining Short-Term User Interest from Personalized Triage Tasks Sampath Jayarathna, Atish Patra and Frank Shipman Computer Science & Engineering, Texas A&M University – College Station. ABSTRACT. USER INTEREST MINING. SEARCH AND RECOMMENDATIONS.

By willem
(91 views)

fox@vt fox.cs.vt Dept. of Computer Science, Virginia Tech

fox@vt fox.cs.vt Dept. of Computer Science, Virginia Tech

IBM Academic Initiative Introduction for Pamplin School of Business Virginia Tech – October 13, 2011 “IBM Academic Skills Cloud and Computing Education Modules ” by Edward A. Fox . fox@vt.edu http:// fox.cs.vt.edu Dept. of Computer Science, Virginia Tech Blacksburg, VA 24061 USA.

By penny
(160 views)

Memory-Based Recommender Systems : A Comparative Study

Memory-Based Recommender Systems : A Comparative Study

CSCI 572 PROJECT RECOMPARATOR. Memory-Based Recommender Systems : A Comparative Study. Aaron John Mani Srinivas Ramani. Problem definition. This project is a comparative study of two movie recommendation systems based on collaborative filtering. User-User Rating vs Item-Item Rating

By danica
(112 views)

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