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This study focuses on developing an advanced tourism destination recommendation system combining big data and artificial intelligence (AI). AI is one of the most advanced technologies in various applications, such as healthcare, industry and various intelligent applications. This study focused on tourism, which helps guide tourist attractions according to the users’ interests. However, reality data are commonly insufficient, and specific traditional measures are not able to be figured out due to the absence of users’ shared rating items. Furthermore, user preferences are ignored by conventional filtering techniques, which leads to a notable drop in suggestion accuracy. This study presents an advanced solution called HITRS — A Hybrid Intelligent Tourism Recommendation System to address these issues. The proposed system combines the strength of collaborative and content-based filtering techniques to provide an accurate recommendation system regarding users’ interests. Here, collaborative filtering is improved with the Jeffries–Matusita Distance (JMSD) algorithm to find similarities between user interests based on their ratings and preferences. At the same time, content-based filtering uses machine learning methods more effectively, such as Google Cloud Vision API, to filter related information from user-generated content such as social media like Instagram pics. Here, the Term Frequency-Inverse Document Frequency (TF-IDF) approach is used to direct these properties. The Cosine Similarity Index is used to compare these properties with the features of tourist attractions. Additionally, the proposed HITRS performs large data volumes from various sources like social media, geolocation and search history to create complete user profiles. Real-time data processing ensures accurate suggestions and the latest trends. The system’s architecture is built to manage big data details, providing essential patterns and information. Through its combined advantages, the hybrid approach improves suggestion accuracy and overcomes the drawbacks of individual filtering techniques. The user-friendly environment of the proposed system allows travelers to communicate easily with the system, providing a simple and pleasurable search for new places. This solution can transform the tourism sector completely by delivering smart, data-driven suggestions regarding every traveler’s desires.

The wide range of digital educational resources calls for developing an accurate and efficient method for categorizing and recommending English teaching materials. An automatic classification and recommendation system has been created and implemented using Natural Language Processing (NLP) techniques. Data from essays produced by English Language Learners (ELLs) in grades 8 through 12, as well as components including content, competency levels and score notes, are organized for this study. Models for precise language proficiency assessments are planned to be developed to enhance automated feedback mechanisms. Preprocessing methods such as stop-word removal, stemming and tokenization were applied to tidy up the data. Term Frequency–Inverse Document Frequency (TF–IDF) and word embeddings were two strategies used in the feature extraction process to convert textual data into numerical vectors. Then, the recently created Support Vector Machine–Neural Network–Genetic Algorithm (SVM–NN–GA) was fed to classify these vectors. The model’s performance was evaluated using F1-measure, accuracy, precision and recall metrics. Methods of collaborative filtering and content-based filtering were studied for the recommendation system. In contrast to collaborative filtering, which used user interaction data to identify patterns and suggest relevant items, content-based filtering matched materials with user preferences based on attributes gathered from NLP models. A hybrid recommendation system combines different approaches, increasing recommendations’ personalization and relevance. The results demonstrate that the hybrid recommendation and NLP-based categorization approach as a combination method suggestively improves the effectiveness of selecting appropriate teaching materials, helping teachers to enhance the learning process.

The generalized arrival of Digital TV will lead to a significant increase in the amount of channels and programs available to end users, making it difficult to find interesting programs among a myriad of irrelevant contents. Thus, in this field, automatic content recommenders should receive special attention in the following years to improve assistance to users. Current approaches of content recommenders have significant well-known deficiencies that hamper their wide acceptance. In this paper, a new approach for automatic content recommendation is presented that considerably reduces those deficiencies. This approach, based on the so-called Semantic Web technologies, has been implemented in the AVATAR tool, a hybrid content recommender that makes extensive use of well-known standards, such as TV-Anytime and OWL. Our proposal has been evaluated experimentally with real users, showing significant increases in the recommendation accuracy with respect to other existing approaches.

Recommender systems offer a solution to the problem of successful information search in the knowledge reservoirs of the Internet by providing individualized recommendations. Content-based and Collaborative Filtering are usually applied to predict recommendations. A combination of the results of the above techniques is used in this work to construct a system that provides precise recommendations concerning movies. The content filtering part of the system is based on trained neural networks representing individual user preferences. Filtering results are combined using Boolean and fuzzy aggregation operators. The proposed hybrid system was tested on the MovieLens data yielding high accuracy predictions.

The electronic mail (email) is nowadays an essential communication service being widely used by most Internet users. One of the main problems affecting this service is the proliferation of unsolicited messages (usually denoted by spam) which, despite the efforts made by the research community, still remains as an inherent problem affecting this Internet service. In this perspective, this work proposes and explores the concept of a novel symbiotic feature selection approach allowing the exchange of relevant features among distinct collaborating users, in order to improve the behavior of anti-spam filters. For such purpose, several Evolutionary Algorithms (EA) are explored as optimization engines able to enhance feature selection strategies within the anti-spam area. The proposed mechanisms are tested using a realistic incremental retraining evaluation procedure and resorting to a novel corpus based on the well-known Enron datasets mixed with recent spam data. The obtained results show that the proposed symbiotic approach is competitive also having the advantage of preserving end-users privacy.

Recommendation systems are becoming more and more present in our daily lives, whether it is for suggesting items to buy, movies to watch or music to listen. They can be used in a large number of contexts. In this paper, we propose the use of a recommendation system in the context of a recruitment platform. The use of the recommendation system allows to obtain precise profile recommendations based on the competences of a candidate to meet the stated requirements and to avoid companies to have to perform a very time-consuming manual sorting of the candidates. Thus, this paper presents the context in which we propose this recommendation system, the data preprocessing, the general approach based on a hybrid content-based filtering (CBF) and similarity index (SI) system, as well as the means implemented to reduce the computational cost of such a system with the increasing evolution of the platform.

We have developed a PubMed article recommendation system, PURE, which is based on content-based filtering. PURE has a web interface by which users can add/delete their preferred articles. Once articles are registered, PURE then performs model-based clustering of the preferred articles and recommends the highly-rated articles by the prediction using the trained model. PURE updates the PubMed articles and reports the recommendation by email on daily-base. This system will be helpful for biologists to reduce the time required for gathering information from PubMed. PURE is downloadable under GPL license, via http://www.bic.kyoto-u.ac.jp/pathway/mami/out/PURE.tar.gz.