Implementation of a Training Courses Recommender System based on k-means algorithm

Providing the right professional training courses for employees is a critical issue for organizations as well as employees. Its necessity stemmed out on the fulfillment of the organization and employees need. Thus, building a recommender system that would help in the decision making process and planning of the training course offered by organizations. This can be performed using various techniques and methodologies, where the most important one is data mining. Data mining is a process of looking for specific patterns and knowledge from large databases and carrying out predictions for outputs. Therefore, this project aims to build a web-based application for predicting appropriate training recommenders for Princess Norah University employees based on their education and professional information. This helps the university in suggesting the most optimal training recommender for employees, which in turn can enhance their performance and develop their career and working levels. Employees’ data was gathered from the Human Resource of the university and then clustered using the WEKA program to find the centroids of clusters to be then used in the developed application. The developed web-based application is used to suggest the most suitable training recommender for each employee. Results demonstrate that the developed web-based application effectively suggests the most appropriate training courses for employees based on the previously taken courses, evaluation of courses and probability for promotion. Furthermore, this web-based application can be used for describing the appropriate training courses for new employees based on their levels. The achieved accuracy of the developed system was 73.33%

Topic Identification System to Filter Twitter Feeds

Twitter is a micro-blogging service where users publish messages of 140 characters. This simple feature makes Twitter the source for concise, instant and interesting information ranging from friends' updates to breaking news. However, a problem emerge when a user follows many accounts while interested in a subset of its content, which leads to overwhelming tweets he is not interested in receiving. We propose a solution to this problem by filtering incoming tweets based on the user's interests, which is accomplished through a classifier. The proposed classifier system categorizes tweets into generic classes like Entertainment, Health, Sport, News, Food, Technology and Health. This paper describes the creation and evaluation of the classifier until 89% accuracy obtained

Semantic inference using chemogenomics data for drug discovery

<p>Abstract</p> <p>Background</p> <p>Semantic Web Technology (SWT) makes it possible to integrate and search the large volume of life science datasets in the public domain, as demonstrated by well-known linked data projects such as LODD, Bio2RDF, and Chem2Bio2RDF. Integration of these sets creates large networks of information. We have previously described a tool called WENDI for aggregating information pertaining to new chemical compounds, effectively creating evidence paths relating the compounds to genes, diseases and so on. In this paper we examine the utility of automatically inferring new compound-disease associations (and thus new links in the network) based on semantically marked-up versions of these evidence paths, rule-sets and inference engines.</p> <p>Results</p> <p>Through the implementation of a semantic inference algorithm, rule set, Semantic Web methods (RDF, OWL and SPARQL) and new interfaces, we have created a new tool called Chemogenomic Explorer that uses networks of ontologically annotated RDF statements along with deductive reasoning tools to infer new associations between the query structure and genes and diseases from WENDI results. The tool then permits interactive clustering and filtering of these evidence paths.</p> <p>Conclusions</p> <p>We present a new aggregate approach to inferring links between chemical compounds and diseases using semantic inference. This approach allows multiple evidence paths between compounds and diseases to be identified using a rule-set and semantically annotated data, and for these evidence paths to be clustered to show overall evidence linking the compound to a disease. We believe this is a powerful approach, because it allows compound-disease relationships to be ranked by the amount of evidence supporting them.</p

State of the art of a multi-agent based recommender system for active software engineering ontology

Software engineering ontology was first developed to provide efficient collaboration and coordination among distributed teams working on related software development projects across the sites. It helped to clarify the software engineering concepts and project information as well as enable knowledge sharing. However, a major challenge of the software engineering ontology users is that they need the competence to access and translate what they are looking for into the concepts and relations described in the ontology; otherwise, they may not be able to obtain required information. In this paper, we propose a conceptual framework of a multi-agent based recommender system to provide active support to access and utilize knowledge and project information in the software engineering ontology. Multi-agent system and semantic-based recommendation approach will be integrated to create collaborative working environment to access and manipulate data from the ontology and perform reasoning as well as generate expert recommendation facilities for dispersed software teams across the sites

A semantic enhanced hybrid recommendation approach: A case study of e-Government tourism service recommendation system

© 2015 Elsevier B.V.All rights reserved. Recommender systems are effectively used as a personalized information filtering technology to automatically predict and identify a set of interesting items on behalf of users according to their personal needs and preferences. Collaborative Filtering (CF) approach is commonly used in the context of recommender systems; however, obtaining better prediction accuracy and overcoming the main limitations of the standard CF recommendation algorithms, such as sparsity and cold-start item problems, remain a significant challenge. Recent developments in personalization and recommendation techniques support the use of semantic enhanced hybrid recommender systems, which incorporate ontology-based semantic similarity measure with other recommendation approaches to improve the quality of recommendations. Consequently, this paper presents the effectiveness of utilizing semantic knowledge of items to enhance the recommendation quality. It proposes a new Inferential Ontology-based Semantic Similarity (IOBSS) measure to evaluate semantic similarity between items in a specific domain of interest by taking into account their explicit hierarchical relationships, shared attributes and implicit relationships. The paper further proposes a hybrid semantic enhanced recommendation approach by combining the new IOBSS measure and the standard item-based CF approach. A set of experiments with promising results validates the effectiveness of the proposed hybrid approach, using a case study of the Australian e-Government tourism services

A hybrid matchmaking approach in the ambient assisted living domain

During the recent years, several new Information and Communication Technology solutions have been developed in order to meet the increasing needs of elderly with cognitive impairments and support their autonomous living. Most of these solutions follow a human-centred paradigm that aims to provide users with personalised services according to their needs by also ensuring their safety with mechanisms that can automatically trigger appropriate actions in situations where there may be a risk for an elderly. The present paper presents a hybrid matchmaking approach that uses efficiently both a rule-based and a statistical matchmaker in order to (a) propose ambient assisted living services to the end-users, based on their role, status and context of use and (b) identify and resolve problematic cases by automatically selecting the most proper set of services to be called in a single or combined manner

Design, development and deployment of an intelligent, personalized recommendation system

Personalization and recommendation systems are a solution to the problem of content overload, especially in large information systems. In this thesis, a personalized recommendation system enhanced with semantic knowledge has been developed in order to overcome the most common limitations of traditional approaches: the cold-start and the sparsity problems. The recommender consists of the following two main components. A user-profile learning algorithm combines user’s feedback from different channels and employs domain inferences to construct accurate user profiles. A recommendation algorithm, using content-based filtering, exploits the semantic structure of the domain to obtain accurate predictions and generate the corresponding recommendations. The system’s design proposed is flexible enough to be potentially applied to applications of any domain that can be properly described using ontologies. In addition to the development of the recommendation system, an existing Web-application in the tourism domain has been extended and adapted in order to be able to integrate the recommender into it. The overall recommendation system has been evaluated and the results obtained indicate that it satisfies the requirements established

An intelligent hybrid multi-criteria hotel recommender system using explicit and implicit feedbacks

Recommender systems, also known as recommender engines, have become an important research area and are now being applied in various fields. In addition, the techniques behind the recommender systems have been improved over the time. In general, such systems help users to find their required products or services (e.g. books, music) through analyzing and aggregating other users’ activities and behavior, mainly in form of reviews, and making the best recommendations. The recommendations can facilitate user’s decision making process. Despite wide literature on the topic, using multiple data sources of different types as the input has not been widely studied. Recommender systems can benefit from the high availability of digital data to collect the input data of different types which implicitly or explicitly help the system to improve its accuracy. Moreover, most of the existing research in this area is based on single rating measures in which a single rating is used to link users to items. This dissertation aims to design a highly accurate hotel recommender system, implemented in various layers and tailored for the subject problem. Using multi-rating system and benefitting from large-scale data of different types, the recommender system suggests hotels that are personalized and tailored for the given user. The system employs natural language processing techniques to assess the sentiment of the users’ reviews and extract implicit features. The entire recommender engine contains multiple sub-systems, namely users clustering, matrix factorization module, and hybrid recommender system. Each sub-system contributes to the final composite set of recommendations through covering a specific aspect of the problem. The accuracy of the proposed recommender system has been tested intensively where the results confirm the high performance of the system