Self-adaptive user profiles for large-scale data delivery

Çetintemel, Uǧur; Franklin, Michael J.; Giles, C. Lee

doi:10.1109/icde.2000.839477

Cited by 61 publications

(49 citation statements)

References 18 publications

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“…Finally, any feedback deriving from the facial expression analysis is regarded as the most significant, because it is generated while the users are watching the actual clip. After each feedback cycle, the user profile is updated, following the multimodal approach presented in [20].…”

Section: Multimodal Recommender Systemmentioning

confidence: 99%

Integrating facial expressions into user profiling for the improvement of a multimodal recommender system

Arapakis

Moshfeghi

Joho

et al. 2009

2009 IEEE International Conference on Multimedia and Expo

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Over the years, recommender systems have been systematically applied in both industry and academia to assist users in dealing with information overload. One of the factors that determine the performance of a recommender system is user feedback, which has been traditionally communicated through the application of explicit and implicit feedback techniques. In this paper, we propose a novel video search interface that predicts the topical relevance of a video by analysing affective aspects of user behaviour. We, furthermore, present a method for incorporating such affective features into user profiling, to facilitate the generation of meaningful recommendations, of unseen videos. Our experiment shows that multimodal interaction feature is a promising way to improve the performance of recommendation.

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Section: Multimodal Recommender Systemmentioning

confidence: 99%

Integrating facial expressions into user profiling for the improvement of a multimodal recommender system

Arapakis

Moshfeghi

Joho

et al. 2009

2009 IEEE International Conference on Multimedia and Expo

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“…In an implementation of our ideas, users can start with a certain relevance threshold and then update it using relevance feedback techniques to achieve a better satisfaction of their information needs. Recent techniques from adaptive IR can be utilised here [7].…”

mentioning

confidence: 99%

Data Models and Languages for Agent-Based Textual Information Dissemination

Koubarakis

Tryfonopoulos

Raftopoulou

et al. 2002

Cooperative Information Agents VI

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Abstract. We define formally the data models WP, AWP and AWPS especially designed for the dissemination of textual information by distributed agent systems using communication languages such as KQML and FIPA-ACL. We also define the problems of satisfaction and filtering and point out that these problems are fundamental for the deployment of our models in distributed agent architectures appropriate for information dissemination. One such architecture currently under development in project DIET is sketched in some detail in this paper. Finally, we present algorithms for the problems of satisfaction and filtering, prove the correctness of these algorithms, and calculate their computational complexity.

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“…As one of long-term goals, we plan to exploit query logs [13] and user profiles [14], etc., as they can be regarded as implicit user feedback. In general, the derived information about a user's interest profile can be used for adaptive ranking of search results.…”

Section: Long-term Profilingmentioning

confidence: 99%

Relevance Feedback in XML Retrieval

Pan

2004

Current Trends in Database Technology - EDBT 2004 Workshops

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Abstract. Highly heterogeneous XML data collections that do not have a global schema, as arising, for example, in federations of digital libraries or scientific data repositories, cannot be effectively queried with XQuery or XPath alone, but rather require a ranked retrieval approach. As known from ample work in the IR field, relevance feedback provided by the user that drives automatic query refinement or expansion can often lead to improved search result quality (e.g., precision or recall). In this paper we present a framework for feedback-driven XML query refinement and address several building blocks including reweighting of query conditions and ontology-based query expansion. We point out the issues that arise specifically in the XML context and cannot be simply addressed by straightforward use of traditional IR techniques, and we present our approaches towards tackling them.

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Self-adaptive user profiles for large-scale data delivery

Abstract: Abstract

Cited by 61 publications

References 18 publications

Integrating facial expressions into user profiling for the improvement of a multimodal recommender system

Integrating facial expressions into user profiling for the improvement of a multimodal recommender system

Data Models and Languages for Agent-Based Textual Information Dissemination

Relevance Feedback in XML Retrieval

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