NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS

Skills-based hiring is a talent management approach that empowers employers to align recruitment around business results, rather than around credentials and title. It starts with employers identifying the particular skills required for a role, and then screening and evaluating candidates’ competencies against those requirements. With the recent rise in employers adopting skills-based hiring practices, it has become integral for students to take courses that improve their marketability and support their long-term career success. A 2017 survey of over 32,000 students at 43 randomly selected institutions found that only 34% of students believe they will graduate with the skills and knowledge required to be successful in the job market. Furthermore, the study found that while 96% of chief academic officers believe that their institutions are very or somewhat effective at preparing students for the workforce, only 11% of business leaders strongly agree [11]. An implication of the misalignment is that college graduates lack the skills that companies need and value. Fortunately, the rise of skills-based hiring provides an opportunity for universities and students to establish and follow clearer classroom-to-career pathways. To this end, this paper presents a course recommender system that aims to improve students’ career readiness by suggesting relevant skills and courses based on their unique career interests

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Learning on demand: dynamic creation of customized, coherent eLearning experiences

This paper presents a conceptual model concerning automatic creation of coherent eLearning experiences through dynamic aggregation of heterogeneous learning objects. The model uses detailed user profiles as the main key to create customized learning content, tailored to each user’s needs, preferences and skills. Aggregation of heterogeneous learning objects may result in a very incoherent learning sequence. Therefore, the model incorporates a method to improve the learning coherence of the generated course. The compromise between customization and coherence is fully adjustable from maximum coherence to maximum customization

Ontology Based Personalized Search Engine

An ontology is a representation of knowledge as hierarchies of concepts within domain, using a shared vocabulary to denote the types, properties and inter-relationships of those concepts [1][2]. Ontologies are often equated with classification of hierarchies of classes, class definitions, and the relations, but ontologies need not be limited to these forms. Ontologies are also not limited to conservative definitions, i.e., in the traditional logic sense that only introduce terminology and do not add any knowledge about the world (Enderton, 1972). To specify a conceptualization, axioms need to be proposed that constrain interpretation of defined terms [3]. Ontologies are frameworks for organizing information and are collections of URIs. It is a systematic arrangement of all important categories of objects and concepts within a particular field and relationship between them. Search engines are commonly used for information retrieval from web. The ontology based personalized search engine (OPSE) captures the user’s priorities in the form of concepts by mining through the data which has been previously clicked by them. Search results need to be provided according to user profile and user interest so that highly relevant search data is provided to the user. In order to do this, user profiles need to be maintained. Location information is important for searching data; OPSE needs to classify concepts into content concepts and location concepts. User locations (gathered during user registration) are used to supplement the location concepts in OPSE. Ontology based user profiles are used to organize user preferences and adapt personalized ranking function in order for relevant documents to be retrieved according to a suitable ranking. A client-server architecture is used for design of ontology based personalized search engine. The design involves in collecting and storing client clickthrough data. Functionalities such as re-ranking and concept extraction can be performed at the server side of personalized search engine. As an additional requirement, we can address the privacy issue by restricting the information in the user profile exposed to the personalized mobile search engine server with some privacy parameters. The Prototype of OPSE will be developed on the web platform. Ontology based personalized search engines can significantly improve the precision of results

The contribution of data mining to information science

The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research