An electronic performance support system based on a hybrid content-collaborative recommender system

Title:

An electronic performance support system based on a hybrid content-collaborative recommender system

Creator:

Basile, Pierpaolo , de Gemmis, Marco, Gentile, Anna Lisa, Iaquinta , Leo, Lops , Pasquale, and Semeraro, Giovanni

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:8fe77a4f-c78b-48e7-a214-fcca06409873
uuid:8fe77a4f-c78b-48e7-a214-fcca06409873

Subject:

User modeling, collaboritave filtering, content-based filtering, hybrid recommenders, machine learning, neighborhood formation in recommender systems, EPSS, and WordNet

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

An Electronic Performance Support System (EPSS) introduces challenges on contextualized and personalized information delivery. Recommender systems aim at delivering and suggesting relevant information according to users preferences, thus EPSSs could take advantage of the recommendation algorithms that have the effect of guiding users in a large space of possible options. The JUMP project (JUst-in-tiMe Performance support systém for dynamic organizations, co-funded by POR Puglia 2000-2006 - Mis. 3.13, Sostegno agli Investimenti in Ricerca Industriale, Sviluppo Precompetitivo e Trasferimento Tecnologico) aims at integrating an EPSS with a hybrid recommender system. Collaborative and content-based filtering are the recommendation techniques most widely adopted to date. The main contribution of this paper is a content-collaborative hybrid recommender which computes similarities between users relying on their content-based profiles in which user preferences are stored, instead of comparing their rating styles. A distinctive feature of our systém is that a statistical model of the user interests is obtained by machine learning techniques integrated with linguistic knowledge contained in WordNet. This model, named ``semantic user profile'', is exploited by the hybrid recommender in the neighborhood formation process.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2007 Volume:17 | Number:6

Harvested from:

CDK

Metadata only:

false