'Implicit feedback' presentation slideshows

The Research Progress of Recommender Systems in Social Tagging Systems

The Research Progress of Recommender Systems in Social Tagging Systems. Dr. Guandong Xu Intelligent Web and Information system Department of Computer Science Aalborg University. Outline . Why recommender systems The state-of-the-art of recommender systems Social tagging systems

827 views • 58 slides

Personalizing Information Search: Understanding Users and their Interests

Personalizing Information Search: Understanding Users and their Interests. Diane Kelly School of Information & Library Science University of North Carolina dianek@email.unc.edu. IPAM | 04 October 2007. Background: IR and TREC. What is IR? Who works on problems in IR?

681 views • 56 slides

Personalizing Information Search: Understanding Users and their Interests

Personalizing Information Search: Understanding Users and their Interests. Diane Kelly School of Information & Library Science University of North Carolina dianek@email.unc.edu. IPAM | 04 October 2007. Background: IR and TREC. What is IR? Who works on problems in IR?

651 views • 56 slides

Putting Query Representation and Understanding in Context:

Putting Query Representation and Understanding in Context:. A Decision-Theoretic Framework for Optimal Interactive Retrieval through Dynamic User Modeling. ChengXiang Zhai Department of Computer Science University of Illinois at Urbana-Champaign .

433 views • 32 slides

Personalization of the Digital Library Experience: Progress and Prospects

405 views • 29 slides

Expanding User Features with Social Relationships in Social Recommender Systems

Expanding User Features with Social Relationships in Social Recommender Systems. Chengjie Sun,Lei Lin, Yuan Chen, Bingquan Liu Harbin Institute of Technology School of Computer Science and Technology. Outlines. Background and challenges Related works Methodology description

369 views • 28 slides

Georg Buscher , Andreas Dengel, Ludger van Elst German Research Center for AI (DFKI)

Query Expansion Using Gaze-Based Feedback on the Subdocument Level. Georg Buscher , Andreas Dengel, Ludger van Elst German Research Center for AI (DFKI) Knowledge Management Department Kaiserslautern, Germany. SIGIR 08. Outline. Motivation

373 views • 28 slides

Relevance Feedback for the Modern Searcher: Elicitation Techniques Revisited

357 views • 27 slides

Search Engines that Learn from Implicit Feedback

Search Engines that Learn from Implicit Feedback. Jiawen, Liu Speech Lab, CSIE National Taiwan Normal University. Reference : Search Engines that Learn from Implicit Feedback , IEEE 2007 Thorsten Joachims and Filip Radlinski, Cornell University. Outline. Introduction

265 views • 26 slides

A time-based approach to effective recommender systems using implicit feedback

A time-based approach to effective recommender systems using implicit feedback. Tong Queue Lee 1 , Young Park 2 , Yong-Tae Park 3 Dongyang Technical College 1 , Braddley University 2 , Seoul National University 3 Expert Systems with Applications, vol 34, issue 4, 2008 2009. 03. 27.

289 views • 16 slides

Feedback Overhead Reduction in 802.11be

Feedback Overhead Reduction in 802.11be. Date: 2019-03-09. Authors:. Abstract. In this contribution, we discuss methods to reduce the overhead of channel acquisition at the transmitter to assist in 16 Spatial Stream MIMO and Multi-AP coordination in 802.11be. Introduction.

196 views • 14 slides