Multiple-vector user profiles in support of knowledge sharing

Vertommen, Joris; Janssens, Frizo; Moor, Bart De; Duflou, Joost R.

doi:10.1016/j.ins.2008.05.001

Cited by 11 publications

(7 citation statements)

References 30 publications

(28 reference statements)

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“…These descriptions permit a recommender system to learn new knowledge about the user's interests, which is not possible with many of the existing user modeling techniques: Some existing works define too simple user models, containing only flat lists of key words (e.g. attributes) or ratings referred to each item defined in the user's profile [11,24,39]. These proposals provide little knowledge about the user's preferences, and therefore hamper the application of advanced reasoning processes.…”

Section: User Modeling Techniquementioning

confidence: 99%

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Blanco‐Fernández¹,

López‐Nores²,

Gil‐Solla³

et al. 2011

Information Sciences

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Section: User Modeling Techniquementioning

confidence: 99%

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Blanco‐Fernández¹,

López‐Nores²,

Gil‐Solla³

et al. 2011

Information Sciences

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“…For knowledge sharing, the agent based technologies and distributed knowledge management (KM) methods were widely used [10,17,25,27]. In addition, game theory [3], cognitive theory [6], and some social science method [20] were also employed to model knowledge sharing interactions among people.…”

Section: Related Workmentioning

confidence: 99%

Distributed recommender for peer-to-peer knowledge sharing

Lü

Jiang

Song

2010

Information Sciences

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“…A weight of 0 indicates that a word from the vocabulary is not present. There exists a large number of weighting schemes to assign weights to dimensions, the most popular including TF-IDF, BM25 and Log-entropy [6].…”

Section: Constructing and Matchmaking User Cognitive Profilesmentioning

confidence: 99%

Strategic interaction support for knowledge sharing in virtual communities

Lei

Zhang²,

Yang³

2008

2008 3rd International Conference on Intelligent System and Knowledge Engineering

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Knowledge sharing in VCs heavily relies on members' interaction, which takes participants' time and effort to collaborate, and the costs of and benefits from interactions in knowledge sharing vary with different collaborator selection. To achieve efficient and effective knowledge sharing, strategic interactions are needed. In this paper, we tried to tackle the strategic interaction support problem for knowledge sharing in VCs from the perspective of cognitive background and social relationship context.

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Multiple-vector user profiles in support of knowledge sharing

Cited by 11 publications

References 30 publications

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems

Distributed recommender for peer-to-peer knowledge sharing

Strategic interaction support for knowledge sharing in virtual communities

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