A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition

By Ana Belén Barragáns-Martínez, E. Costa-Montenegro, Juan-Carlos Burguillo, Marta Rey-López, F. Mikic-Fonte, Ana Peleteiro-Ramallo. Information Sciences, 2010

373 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/0c54f1693efd52f61538bea287c45b01f134b3c9

How researchers see this paper

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

In Information Systems

1. Fernando Ortega
2. Antonio Hernando
3. Abraham Gutiérrez
4. Jesús Bobadilla
5. Min Chen
6. Jie Lu
7. Shiwen Mao
8. Yunhao Liu

In Sociology and Political Science

1. Jie Lu
2. Mingsong Mao
3. Wei Wang
4. Dianshuang Wu
5. Guangquan Zhang

Citations in the corpus

• Relational Collaborative Topic Regression for Recommender Systems (2015, neutral): “Hybrid methods [2], [3], [6], [12], [15], [16], [17], [20], [32], [34], [40], [43], [47], [48] combine both con-”
• Recommender system application developments: A survey (2015, methodology): “[132], combines the CB approach and item-based CF approach to address the problems of gray sheep, cold start and first rating.”
• Big Data: A Survey (2014, methodology): “Presently, a mixed method is introduced, which integrates advantages of the aforementioned two types of methods to improve recommendation quality [133].”
• Recommender systems survey (2013, methodology): “Commonly uses a combination of CF with demographic filtering [224] or CF with content-based filtering [18,60] to exploit merits of each one of these techniques.”