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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.

Recommender systems are tools that provide personalized suggestions of items for users. They must be able to adapt constantly to user preferences and behavior in order to generate relevant recommendations. However, initial works in this field do not consider the context in which the users are. In recent years, a new recommendation technique, called Context-Aware Recommender System (CARS), has emerged. This approach integrates contextual information about users and/or items in the recommendation process to satisfy even more users’ needs. Therefore, accurate prediction depends upon the degree to which a recommendation method has incorporated the relevant contextual data. To address this issue, we propose to combine user based collaborative filtering with the Genetic Algorithm based meta-heuristic in order to provide better predictions for users. The proposed model uses a weighting function which incorporates the contextual factors that influence the users’ decisions. It is based on the Genetic Algorithm based meta-heuristic to estimate, for each contextual parameter, a degree of importance that would reduce the mean absolute error and increase the F-measure. Experimental results from Movielens dataset validate that our proposed algorithm improves recommendations accuracy.