ABSTRACT
Nowadays, the tourism is a principal economic sector for the world due to the exportations are improved, the jobs number is enhanced and the economic is developed. In México, the tourism represents 8.7% of GDP and generates 4.5 million direct jobs, however this economic sector has been affected by COVID-19 pandemic. For these reasons, a hybrid recommender model based on information retrieval is presented in this research to tackle the recommendation systems task of Rest-Mex 2022. A vector space model with tf-idf weighting scheme and cosine similarity is implemented. Besides, a hybrid recommender model is generated applying the recommendation techniques item-item collaborative filtering, content-based filtering and switching hybrid approach. Finally, our proposal won the second and third place in the competition. © 2022 Copyright for this paper by its for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
ABSTRACT
Because of COVID-19 pandemic, online movies are now extremely popular. While the movie theaters have not serviced and people are staying quarantine, movies are the best choice for relaxing and treating stress. In present, recommender systems are widely integrated into many platforms of movie applications. A hybrid recommender system is one promising technique to improve the system performance, especially for cold-start, data sparsity, and scalability. This paper proposed a hybrid of matrix factorization, biased matrix factorization, and factor wise matrix factorization to solve all mentioned drawback problems. Simulation shows that the proposed hybrid algorithm can decrease approximately 11.91% and 10.70% for RMSE and MAE, respectively, when compared with the traditional methods. In addition, the proposed algorithm is capable of scalability. While the number of datasets is tremendously increased by 10 times, it is still effectively executed. © 2022 IEEE.