Skip to Main content Skip to Navigation
Conference papers

Feature Frequency Inverse User Frequency for Dependant Attribute to Enhance Recommendations

Abstract : Recommender system provides relevant items to users from huge catalogue. Collaborative filtering and content-based filtering are the most widely used techniques in personalized recommender systems. Collaborative filtering uses only the user-ratings data to make predictions, while content-based filtering relies on semantic information of items for recommendation. The aim of this work is to introduce the semantic aspect of items in a collaborative filtering process in order to enhance recommendations. Many works have addressed this problem by proposing hybrid solutions. In this paper, we present another hybridization technique that predicts users preferences for items based on their inferred preferences for semantic information of items. For this, we propose a new approach to build user semantic model by using TF-IDF measure and we provide solution to reduce the dimension of data. Applying our approach to real data, the MoviesLens 1M dataset, significant improvement can be noticed compared to usage only approach, Content only approach and hybrid algorithm
Document type :
Conference papers
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download
Contributor : Azim Roussanaly Connect in order to contact the contributor
Submitted on : Tuesday, January 14, 2014 - 4:27:22 PM
Last modification on : Saturday, October 16, 2021 - 11:26:08 AM
Long-term archiving on: : Tuesday, April 15, 2014 - 4:26:57 PM


Files produced by the author(s)


  • HAL Id : hal-00930911, version 1



Sonia Ben Ticha, Azim Roussanaly, Anne Boyer, Khaled Bsaies. Feature Frequency Inverse User Frequency for Dependant Attribute to Enhance Recommendations. SOTICS 2013, The Third International Conference on Social Eco-Informatics, IARA, Nov 2013, Lisbonne, Portugal. pp.45-50. ⟨hal-00930911⟩



Record views


Files downloads