Weighting of Features in Content-Based Filtering with Entropy and Dependence Measures
Content-based recommender systems (CBRS) are tools that help users to choose items when they face a huge amount of options, recommending items that better fit the user's profile. In such a process, it is very interesting to know which features of the items are more important for each user, thus...
Main Authors: | Jorge Castro, Rosa M. Rodriguez, Manuel J. Barranco |
---|---|
Format: | Article |
Language: | English |
Published: |
Springer
2014-01-01
|
Series: | International Journal of Computational Intelligence Systems |
Subjects: | |
Online Access: | https://www.atlantis-press.com/article/25868463.pdf |
Similar Items
-
Sistem Rekomendasi Laptop Menggunakan Collaborative Filtering Dan Content-Based Filtering
by: Anderias Eko Wijaya, et al.
Published: (2018-06-01) -
Café Recommendation Using the Content-Based Filtering Method
by: Anggito Whiku Wicaksono, et al.
Published: (2024-09-01) -
Implementasi Metode Content Based Filtering Pada Aplikasi Pencarian Taman Penitipan Anak
by: Yaya Sudarya Triana, et al.
Published: (2019-08-01) -
Application of Content-Based Filtering Method Using Cosine Similarity in Restaurant Selection Recommendation System
by: Fajar Christyawan, et al.
Published: (2024-09-01) -
A view on weighted exponential entropy and examining some of its features
by: S. Mazloum Panjehkeh, et al.
Published: (2023-11-01)