Real life, real users, and real needs: a study and analysis of user queries on the web
Information Processing and Management: an International Journal
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real time user context modeling for information retrieval agents
Proceedings of the tenth international conference on Information and knowledge management
Display time as implicit feedback: understanding task effects
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
CubeSVD: a novel approach to personalized Web search
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Evaluating implicit feedback models using searcher simulations
ACM Transactions on Information Systems (TOIS)
An adaptive technique for content-based image retrieval
Multimedia Tools and Applications
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
A large-scale evaluation and analysis of personalized search strategies
Proceedings of the 16th international conference on World Wide Web
Studying the use of popular destinations to enhance web search interaction
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
VideoReach: an online video recommendation system
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Online video recommendation based on multimodal fusion and relevance feedback
Proceedings of the 6th ACM international conference on Image and video retrieval
Evaluating the implicit feedback models for adaptive video retrieval
Proceedings of the international workshop on Workshop on multimedia information retrieval
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Search trails using user feedback to improve video search
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Click chain model in web search
Proceedings of the 18th international conference on World wide web
Revisiting IR Techniques for Collaborative Search Strategies
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Use of implicit graph for recommending relevant videos: a simulated evaluation
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
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We present a model for exploiting community-based usage information for video retrieval, where implicit usage information from past users is exploited in order to provide enhanced assistance in video retrieval tasks, and alleviate the effects of the semantic gap problem. We propose a graph-based model for all types of implicit and explicit feedback, in which the relevant usage information is represented. Our model is designed to capture the complex interactions of a user with an interactive video retrieval system, including the representation of sequences of user-system interaction during a search session. Building upon this model, four recommendation strategies are defined and evaluated. An evaluation strategy is proposed based on simulated user actions, which enables the evaluation of our recommendation strategies over a usage information pool obtained from 24 users performing four different TRECVid tasks. Furthermore, the proposed simulation approach is used to simulate usage information pools with different characteristics, with which the recommendation approaches are further evaluated on a larger set of tasks, and their performance is studied with respect to the scalability and quality of the available implicit information.