Authors:
Sonia Ben Ticha
1
;
Azim Roussanaly
2
;
Anne Boyer
2
and
Khaled Bsaïes
3
Affiliations:
1
Lorraine university and Tunis El Manar University, France
;
2
Lorraine University, France
;
3
Tunis El Manar University, Tunisia
Keyword(s):
Hybrid Recommender System, Latent Semantic Analysis, Rocchio Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Enterprise Information Systems
;
Recommendation Systems
;
Software Agents and Internet Computing
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. Hybrid recommendation system combines the two techniques. The aim of this work is to introduce a new approach for semantically enhanced
collaborative filtering. 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 design a new user semantic model by using Rocchio algorithm and we apply a latent semantic analysis to reduce the dimension of data.
Applying our approach to real data, the MoviesLens 1M dat
aset, significant improvement can be noticed compared to usage only approach, and hybrid algorithm.
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