48,562 research outputs found
Personalized Course Sequence Recommendations
Given the variability in student learning it is becoming increasingly
important to tailor courses as well as course sequences to student needs. This
paper presents a systematic methodology for offering personalized course
sequence recommendations to students. First, a forward-search
backward-induction algorithm is developed that can optimally select course
sequences to decrease the time required for a student to graduate. The
algorithm accounts for prerequisite requirements (typically present in higher
level education) and course availability. Second, using the tools of
multi-armed bandits, an algorithm is developed that can optimally recommend a
course sequence that both reduces the time to graduate while also increasing
the overall GPA of the student. The algorithm dynamically learns how students
with different contextual backgrounds perform for given course sequences and
then recommends an optimal course sequence for new students. Using real-world
student data from the UCLA Mechanical and Aerospace Engineering department, we
illustrate how the proposed algorithms outperform other methods that do not
include student contextual information when making course sequence
recommendations
Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia
Only few digital libraries and reference managers offer recommender systems,
although such systems could assist users facing information overload. In this
paper, we introduce Mr. DLib's recommendations-as-a-service, which allows third
parties to easily integrate a recommender system into their products. We
explain the recommender approaches implemented in Mr. DLib (content-based
filtering among others), and present details on 57 million recommendations,
which Mr. DLib delivered to its partner GESIS Sowiport. Finally, we outline our
plans for future development, including integration into JabRef, establishing a
living lab, and providing personalized recommendations.Comment: Accepted for publication at the JCDL conference 201
IMPROVING THE DEPENDABILITY OF DESTINATION RECOMMENDATIONS USING INFORMATION ON SOCIAL ASPECTS
Prior knowledge of the social aspects of prospective destinations can be very influential in making travel destination decisions, especially in instances where social concerns do exist about specific destinations. In this paper, we describe the implementation of an ontology-enabled Hybrid Destination Recommender System (HDRS) that leverages an ontological description of five specific social attributes of major Nigerian cities, and hybrid architecture of content-based and case-based filtering techniques to generate personalised top-n destination recommendations. An empirical usability test was conducted on the system, which revealed that the dependability of recommendations from Destination Recommender Systems (DRS) could be improved if the semantic representation of social
attributes information of destinations is made a factor in the destination recommendation process
The state-of-the-art in personalized recommender systems for social networking
With the explosion of Web 2.0 application such as blogs, social and professional networks, and various other types of social media, the rich online information and various new sources of knowledge flood users and hence pose a great challenge in terms of information overload. It is critical to use intelligent agent software systems to assist users in finding the right information from an abundance of Web data. Recommender systems can help users deal with information overload problem efficiently by suggesting items (e.g., information and products) that match users’ personal interests. The recommender technology has been successfully employed in many applications such as recommending films, music, books, etc. The purpose of this report is to give an overview of existing technologies for building personalized recommender systems in social networking environment, to propose a research direction for addressing user profiling and cold start problems by exploiting user-generated content newly available in Web 2.0
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