Personalization Techniques And Recommender Syst...

Methods: A knowledge-based recommender system was implemented using ontology and several natural language processing (NLP) techniques. The development process was divided into 3 stages. In stage 1, an ontology was constructed to describe patient characteristics contained in the data. In stage 2, an algorithm was designed and implemented to generate recommendations based on the ontology. Patient data and educational materials were mapped to the ontology and converted into vectors of the same length, and then recommendations were generated according to similarity between these vectors. In stage 3, the ontology and algorithm were incorporated into an mHealth system for practical use. Keyword extraction algorithms and pretrained word embeddings were used to preprocess educational materials. Three strategies were proposed to improve the performance of keyword extraction. System evaluation was based on a manually assembled test collection for 50 patients and 100 educational documents. Recommendation performance was assessed using the macro precision of top-ranked documents and the overall mean average precision (MAP).

Conclusions: This study demonstrated that a knowledge-based health recommender system has the potential to accurately recommend educational materials to chronic disease patients. Traditional NLP techniques combined with improvement strategies for specific language and domain proved to be effective for improving system performance. One direction for future work is to explore the effect of such systems from the perspective of patients in a practical setting.

The paper is organized as follows. Section 2 summarizes the results of contemporary research about using collaborative tagging for learning process and knowledge acquisition. Section 3 provides descriptions of tag-based recommender systems suitable for applications in e-learning environments and our proposed approach used for personalization process in programming domain. Experimental evaluation of integrated recommender system based on collaborative tagging techniques into our tutoring system is presented in Section 4. The concluding remarks are given in Section 5.

A variety of techniques have been proposed and investigated for delivering personalized recommendations for electronic commerce and other web applications. To improve performance, these methods have sometimes been combined in hybrid recommenders. This chapter surveys the landscape of actual and possible hybrid recommenders, and summarizes experiments that compare a large set of hybrid recommendation designs.

Recommender systems, also known as recommender engines, are information filtering systems that provide individual recommendations in real-time. As powerful personalization tools, recommendation systems leverage machine learning algorithms and techniques to give the most relevant suggestions to particular users by learning data (e.g., past behaviors) and predicting current interests and preferences.\\n\"}}] } Email: solutions@altexsoft.com

In this module, you will learn several techniques for non- and lightly-personalized recommendations, including how to use meaningful summary statistics, how to compute product association recommendations, and how to explore using demographics as a means for light personalization. There is both an assignment (trying out these techniques in a spreadsheet) and a quiz to test your comprehension.

The next topic in this course is content-based filtering, a technique for personalization based on building a profile of personal interests. Divided over two weeks, you will learn and practice the basic techniques for content-based filtering and then explore a variety of advanced interfaces and content-based computational techniques being used in recommender systems.

A Recommender System is a process that seeks to predict user preferences. This Specialization covers all the fundamental techniques in recommender systems, from non-personalized and project-association recommenders through content-based and collaborative filtering techniques, as well as advanced topics like matrix factorization, hybrid machine learning methods for recommender systems, and dimension reduction techniques for the user-product preference space.

This specialization is a substantial extension and update of our original introductory course. It involves about 60% new and extended lectures and mostly new assignments and assessments. This course specifically has added material on stereotyped and demographic recommenders and on advanced techniques in content-based recommendation.

Deep learning (DL) recommender models build upon existing techniques such as factorization to model the interactions between variables and embeddings to handle categorical variables. An embedding is a learned vector of numbers representing entity features so that similar entities (users or items) have similar distances in the vector space. For example, a deep learning approach to collaborative filtering learns the user and item embeddings (latent feature vectors) based on user and item interactions with a neural network.

The Facebook research team addresses personalization by combining perspectives from recommendation systems and predictive analytics. Specifically, they introduce a Deep Learning Recommendation Model (DLRM) that uses embeddings to process sparse features and a multilayer perceptron (MLP) to process dense features. Then, the model combines these features explicitly and defines the event probability using another MLP. The experiments demonstrate the effectiveness of the suggested approach in building a recommender system.

The researchers question the progress that deep learning techniques bring into the recommender system area. They conduct a systematic analysis of 18 research papers that have introduced new algorithms for proposing top-n recommendations and have been presented at top conferences during the last few years. The authors identify two major issues with this research: (1) lack of reproducibility, with only 7 out of 18 papers providing sufficient information for reproducing their research; (2) lack of progress, with 6 out of 7 reproduced models being outperformed using simple heuristic methods. Thus, the researchers call for more rigorous research practices with respect to the evaluation of new contributions in this area. 781b155fdc