1. Enhanced context-aware recommendation using topic modeling and particle swarm optimization
- Author
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Hassina Seridi-Bouchelaghem, Ibtissem Gasmi, Mohamed Walid Azizi, Samir Brahim Belhaouari, and Nabiha Azizi
- Subjects
Statistics and Probability ,Topic model ,business.industry ,Computer science ,General Engineering ,Particle swarm optimization ,Context (language use) ,02 engineering and technology ,Machine learning ,computer.software_genre ,Artificial Intelligence ,020204 information systems ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,business ,computer - Abstract
Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.
- Published
- 2021