Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences

By Manos Papagelis, Dimitris Plexousakis, Themistoklis Kutsuras. Trust Management, 2005

305 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/04240913f2d9af08daaab2c5e6dd7bf1f4f47044

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dietmar Jannach
2. Michael Jugovac
3. Hendrik Drachsler
4. Rob Koper
5. Nikos Manouselis
6. Mozhgan Karimi

In Information Systems

1. Hendrik Drachsler
2. Michael Jugovac
3. Mozhgan Karimi
4. Dietmar Jannach
5. Nikos Manouselis

In Sociology and Political Science

1. Dietmar Jannach
2. Hendrik Drachsler

Citations in the corpus

• Recommender Systems in Technology Enhanced Learning (2011, methodology): “This is a common evaluation practice in systems examined for other domains (e.g. Breese et al. 1998; Deshpande and Karypis 2004; Papagelis et al. 2005; Herlocker et al. 2002), which indicate that careful testing and parameterisation has to be carried out before a recommender system is finally deploy”
• News recommender systems - Survey and roads ahead (2018, background): “Finding a sufficient number of similar users, for example, in a nearestneighbors collaborative filtering approach, can therefore be very challenging [143].”