A Hybrid Recommender Model based on Information Retrieval for Mexican Tourism Text in Rest-Mex 2022

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).

A Hybrid Recommender System for Improving Rating Prediction of Movie Recommendation

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.

Artificial intelligence-based decision support model for new drug development planning.

New drug development guarantees a very high return on success, but the success rate is extremely low. Pharmaceutical companies have attempted to use various strategies to increase the success rate of drug development, but this goal has been difficult to achieve. In this study, we developed a model that can guide effective decision-making at the planning stage of new drug development by leveraging machine learning. The Drug Development Recommendation (DDR) model, we present here, is a hybrid model for recommending and/or predicting drug groups suitable for development by individual pharmaceutical companies. It combines association rule learning, collaborative filtering, and content-based filtering approaches for enterprise-customized recommendations. In the case of content-based filtering applying a random forest classification algorithm, the accuracy and area under curve were 78% and 0.74, respectively. In particular, the DDR model was applied to predict the success probability of companies developing Coronavirus disease 2019 (COVID-19) vaccines. It was demonstrated that the higher the predicted score from the DDR model, the more progress in the clinical phase of the COVID-19 vaccine development. Although our approach has limitations that should be improved, it makes scientific as well as industrial contributions in that the DDR model can support rational decision-making prior to initiating drug development by considering not only technical aspects but also company-related variables.