Selection of Similarity Function for Context-Aware Recommendation Systems

The design and implementation of generic frameworks to support an easy development of context-aware recommendation systems has been relatively unexplored. In this paper, we briefly present our ongoing work for the definition of a framework that will support the development of context-aware recommendation systems in distributed and mobile environments. Those systems will allow suggesting relevant items to mobile users.

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Machine Learning Applied to Neuroreabilitation: A Systematic Mapping

Journal on Advances in Theoretical and Applied Informatics ◽

10.26729/jadi.v5i1.3072 ◽

2020 ◽

Vol 5 (1) ◽

Author(s):

Flávia Gonçalves Fernandes ◽

Eder Manoel De Santana

Keyword(s):

Machine Learning ◽

Recommendation Systems ◽

Well Being ◽

Public Access ◽

Journal Articles ◽

Systematic Mapping ◽

Logical Operators ◽

Health Area ◽

New Research ◽

Selection Of

Machine learning and recommendation systems are tools used to improve the search indices of the most relevant items in large amounts of data that can be applied in the health area. To present a systematic mapping in the area of neurorehabilitation that uses machine learning. Analyze the references of the work carried out involving the theme on the application of machine learning in the area of neurorehabilitation. Search for studies enrolled in databases through logical operators for the selection of peer-reviewed journal articles. In addition, it was verified that the application of the systematic mapping in the elaboration of the bibliographic review allows to identify the main gaps for the development of new research, and to direct to the main publications related to the study. Therefore, it is necessary to promote this area of research to offer this public access to the techniques of neuro-rehabilitation as a form of treatment, acquisition of knowledge, motivation or even inclusion. In this way, it will be possible to obtain a greater maturity in the obtained results and, thus, to promote a systematization in the use of neuro-rehabilitation in the promotion of the well-being of these people.

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Hotel Recommender System based on Knowledge Graph and Collaborative Approach

International Journal of Computing ◽

10.47839/ijc.20.1.2093 ◽

2021 ◽

pp. 63-71

Author(s):

Yousef Abuzir ◽

Mohamed Dwieb

Keyword(s):

Recommendation System ◽

Hybrid Approach ◽

Recommendation Systems ◽

Knowledge Graph ◽

Similarity Function ◽

Collaborative Approach ◽

Analysis Techniques ◽

Business Sectors ◽

F Measure

With the rapid increase of Information technology, online services and social media, recommendation system becomes an important issue and a need for both the customer and business sectors. The main aim of traditional and online recommendation systems is to recommend the desired and the necessary services that are appropriate recommendations to users. Traditional recommendation systems often suffer from inefficient data analysis techniques, rating the different services without regard to the previous preferences of the users and do not meet the personal demands of the users. Therefore, in this paper we used a hybrid approach based on Knowledge graph and Machine Learning similarity function as a recommendation system. We used real datasets to conduct the experiment. We built the knowledge graph for the visitors, hotels and their ranks, and we used the knowledge graph and similarity scores to recommend a hotel or a set of hotels for the visitors based on former preferences and ratings of other visitors. The results show significant accuracy and good quality of service recommender systems with 93.5% for f-measure.

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Considering Fine-Grained and Coarse-Grained Information for Context-Aware Recommendations

The Computer Journal ◽

10.1093/comjnl/bxaa095 ◽

2020 ◽

Author(s):

Yiqin Luo ◽

Yanpeng Sun ◽

Liang Chang ◽

Tianlong Gu ◽

Chenzhong Bin ◽

...

Keyword(s):

State Of The Art ◽

Recommendation Systems ◽

Coarse Grained ◽

Evaluation Metrics ◽

Parallel Structure ◽

Context Aware ◽

Preference Information ◽

Fine Grained ◽

Category Information ◽

Candidate Item

Abstract In context-aware recommendation systems, most existing methods encode users’ preferences by mapping item and category information into the same space, which is just a stack of information. The item and category information contained in the interaction behaviours is not fully utilized. Moreover, since users’ preferences for a candidate item are influenced by the changes in temporal and historical behaviours, it is unreasonable to predict correlations between users and candidates by using users’ fixed features. A fine-grained and coarse-grained information based framework proposed in our paper which considers multi-granularity information of users’ historical behaviours. First, a parallel structure is provided to mine users’ preference information under different granularities. Then, self-attention and attention mechanisms are used to capture the dynamic preferences. Experiment results on two publicly available datasets show that our framework outperforms state-of-the-art methods across the calculated evaluation metrics.

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