Overcoming Incomplete User Models in Recommendation Systems Via an Ontology

As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative, content-based and case-based recommendation systems and their hybrids have been used for music recommendation. There are already a number of online music recommendation systems. Although specific user information, such as, demographic data, education, and origin have been shown to affect music preferences, they are usually not collected by the online music recommendation systems, because users would not like to disclose their personal data. Therefore, user models mostly contain information about which music pieces a user liked and which ones s/he did not and when.

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Black-box testing of practical movie recommendation systems: A comparative study

Computer Science and Information Systems ◽

10.2298/csis130226006l ◽

2014 ◽

Vol 11 (1) ◽

pp. 241-249 ◽

Cited By ~ 3

Author(s):

Namhee Lee ◽

Jason Jung ◽

Ali Selamat ◽

Dosam Hwang

Keyword(s):

Recommendation Systems ◽

Black Box ◽

Evaluation Framework ◽

Attribute Selection ◽

Practical Recommendation ◽

User Models ◽

Main Research ◽

Modeling Process ◽

Black Box Testing ◽

User Ratings

Many practical recommendation systems have been studied, and also the services based on such recommendation systems have been opened in real world. The main research questions of this work are i) how these recommendation services provide users with useful information, and ii) how different the results from the systems are from each other. In this paper, we propose a black-box evaluation framework of the practical recommendation services. Thus, we have designed user modeling process for generating synthesized user models as the inputs for the recommendation services. User models (i.e., a set of user ratings) have been synthesized to discriminate the recommendation results. Given a set of practical recommendation systems, the proposed black-box testing scheme has been applied by comparing recommendation results. Particularly, we focus on investigating whether the services consider attribute selection.

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Recommendation Systems, Incorporating Sentiment Analysis with Specific Reference to the Academic Domain

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i9.1722 ◽

2018 ◽

Vol 6 (9) ◽

pp. 17-22

Author(s):

Anil Kumar ◽

Sonal Chawla

Keyword(s):

Sentiment Analysis ◽

Recommendation Systems ◽

Specific Reference

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A feature-based hybrid recommender system for risk prediction : Machine learning approach (Preprint)

10.2196/preprints.11010 ◽

2020 ◽

Author(s):

Uzair Bhatti

Keyword(s):

Machine Learning ◽

Risk Prediction ◽

Predictive Accuracy ◽

Correct Diagnosis ◽

Recommendation Systems ◽

Data Integrity ◽

Machine Learning Algorithms ◽

Patient Counseling ◽

Hybrid Filtering ◽

Novel Algorithm

BACKGROUND In the era of health informatics, exponential growth of information generated by health information systems and healthcare organizations demands expert and intelligent recommendation systems. It has become one of the most valuable tools as it reduces problems such as information overload while selecting and suggesting doctors, hospitals, medicine, diagnosis etc according to patients’ interests. OBJECTIVE Recommendation uses Hybrid Filtering as one of the most popular approaches, but the major limitations of this approach are selectivity and data integrity issues.Mostly existing recommendation systems & risk prediction algorithms focus on a single domain, on the other end cross-domain hybrid filtering is able to alleviate the degree of selectivity and data integrity problems to a better extent. METHODS We propose a novel algorithm for recommendation & predictive model using KNN algorithm with machine learning algorithms and artificial intelligence (AI). We find the factors that directly impact on diseases and propose an approach for predicting the correct diagnosis of different diseases. We have constructed a series of models with good reliability for predicting different surgery complications and identified several novel clinical associations. We proposed a novel algorithm pr-KNN to use KNN for prediction and recommendation of diseases RESULTS Beside that we compared the performance of our algorithm with other machine algorithms and found better performance of our algorithm, with predictive accuracy improving by +3.61%. CONCLUSIONS The potential to directly integrate these predictive tools into EHRs may enable personalized medicine and decision-making at the point of care for patient counseling and as a teaching tool. CLINICALTRIAL dataset for the trials of patient attached

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Recommendation Systems for Education: Systematic Review

Electronics ◽

10.3390/electronics10141611 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1611

Author(s):

María Cora Urdaneta-Ponte ◽

Amaia Mendez-Zorrilla ◽

Ibon Oleagordia-Ruiz

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Formal Education ◽

Research Work ◽

Hybrid Approach ◽

Recommendation Systems ◽

Educational Resources ◽

Future Research ◽

Collaborative Approach ◽

Developmental Approach

Recommendation systems have emerged as a response to overload in terms of increased amounts of information online, which has become a problem for users regarding the time spent on their search and the amount of information retrieved by it. In the field of recommendation systems in education, the relevance of recommended educational resources will improve the student’s learning process, and hence the importance of being able to suitably and reliably ensure relevant, useful information. The purpose of this systematic review is to analyze the work undertaken on recommendation systems that support educational practices with a view to acquiring information related to the type of education and areas dealt with, the developmental approach used, and the elements recommended, as well as being able to detect any gaps in this area for future research work. A systematic review was carried out that included 98 articles from a total of 2937 found in main databases (IEEE, ACM, Scopus and WoS), about which it was able to be established that most are geared towards recommending educational resources for users of formal education, in which the main approaches used in recommendation systems are the collaborative approach, the content-based approach, and the hybrid approach, with a tendency to use machine learning in the last two years. Finally, possible future areas of research and development in this field are presented.

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