A comparative analysis of different recommender systems for university major and career domain guidance

Abstract

Academic advising is inhibited at most of the high schools to help students identify appropriate academic pathways. The choice of a career domain is significantly influenced by the complexity of life and the volatility of the labor market. Thus, high school students feel confused during the shift period from high school to university, especially with the enormous amounts of data available on the Web. In this paper, an extensive comparative study is conducted to investigate five approaches of recommender systems for university study field and career domain guidance. A novel ontology is constructed to include all the needed information for this purpose. The developed approaches considered user-based and item-based collaborative filtering, demographic-based recommendation, knowledge base supported by case-based reasoning, ontology, as well as different hybridizations of them. A case study on Lebanese high school students is analyzed to evaluate the effectiveness and efficiency of the implemented approaches. The experimental results indicate that the knowledge-based hybrid recommender system, combined with the user-based collaborative filtering and braced with case-based reasoning as well as ontology, generated 98% of similar cases, 95% of them are personalized based on the interests of the high school students. The average usefulness feedback and satisfaction level of the students concerning this proposed hybrid approach reached 95% and 92.5% respectively, which could be a solution to similar problems, regardless of the application domain. Besides, the constructed ontology could be reused in other systems in the educational domain.

Dynamic Multi-Criteria Decision Making of Graduate Admission Recommender System: AHP and Fuzzy AHP Approaches

“…By combining the digital number of 32 criteria bits from the example in Figure 4, the binary number 01000000000101110000000010011100 represents the integer value 107,524,930,81010. Conversion from binary to base-10 is: 010000000001011100000000100111002 = (0 × 2 31 ) + (1 × 2 30 ) + (0 × 2 29 ) + (0 × 2 28 ) + (0 × 2 27 ) + (0 × 2 26 ) + (0 × 2 25 ) + (0 × 2 24 ) + (0 × 2 23 ) + (0 × 2 22 ) + (0 × 2 21 ) + (1 × 2 20 ) + (0 × 2 19 ) + (1 × 2 18 ) + (1 × 2 17 ) + (1 × 2 16 ) + (0 × 2 15 ) + (0 × 2 14 ) + (0 × 2 13 ) + (0 × 2 12 ) + (0 × 2 11 ) + (0 × 2 10 ) + (0 × 2 9 ) + (0 × 2 8 ) + (1 × 2 7 ) + (0 × 2 6 ) + (0 × 2 5 ) + (1 × 2 4 ) + (1 × 2 3 ) + (1 × 2 2 ) + (0 × 2 1 ) + (0 × 2 0 ) = 107524930810.…”

“…In the meantime, higher education can contribute to improved careers, attitudes, life opportunities, and salary outcomes. Consequently, long-term success in higher education becomes essential [3]. The achievement of sustainable success in higher education may involve two possible stakeholders: university staff and administrators who provide educational needs and services.…”

Research on the Application of Artificial Intelligence Technology in the Banking Internet Finance Industry

“…λ t Θ T c represents the value function of the user c at time t.λ t is the proportion of different labels in the user c. It represents the area that each tag occupies in the user. Θ c is the ratio of the frequency of each tag occurring in a user c to that tag occurring in many users [8]. It reflects the significant differences of individual marker vectors λ t in the population.…”