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Titlebook: E-Commerce and Web Technologies; 5th International Co Kurt Bauknecht,Martin Bichler,Birgit Pr?ll Conference proceedings 2004 Springer-Verla

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樓主: Stimulant
61#
發(fā)表于 2025-4-1 02:15:04 | 只看該作者
62#
發(fā)表于 2025-4-1 09:25:36 | 只看該作者
Interaction Trust Evaluation in Decentralized Environmentsr peers. This paper first presents a probabilistic approach for evaluating the interaction trust of unfamiliar peers according to their interaction history. In addition, after an interaction, peers can evaluate each other and modify the trust status. Based on it, this paper presents an approach for trust value modification after interactions.
63#
發(fā)表于 2025-4-1 10:30:29 | 只看該作者
64#
發(fā)表于 2025-4-1 15:17:02 | 只看該作者
Using Attributes to Improve Prediction Quality in Collaborative Filteringent the MovieLens dataset of the GroupLens Research Center has been used. The results on various experiments using several neighbor selection methods which are quite popular techniques for recommender systems show that the recommender systems using the attributes provide better prediction qualities
65#
發(fā)表于 2025-4-1 21:49:15 | 只看該作者
https://doi.org/10.1007/978-981-16-5344-5ent the MovieLens dataset of the GroupLens Research Center has been used. The results on various experiments using several neighbor selection methods which are quite popular techniques for recommender systems show that the recommender systems using the attributes provide better prediction qualities
66#
發(fā)表于 2025-4-2 00:03:41 | 只看該作者
Conference proceedings 2004he program committee - lected 37 papers for presentationand publication, a task which was not easy due to the high quality of the submitted papers. We would like to express our thanks to our colleagues who helped with putting together the technical program: the program committee members and external
67#
發(fā)表于 2025-4-2 06:04:12 | 只看該作者
68#
發(fā)表于 2025-4-2 07:35:25 | 只看該作者
Using Association Analysis of Web Data in Recommender Systemstions is to provide users with instruments for personalized selective retrieval of web information. In this paper, a procedure for making personalized recommendations is proposed. The method is based on building a predictive model from an association model of Web data. It uses a set of association r
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