A Case Study on Correctness Evaluation of Content Based Recommender System Based on Text, Semantic Text and Visual Similarity

Author(s):  
Tilottama Goswami ◽  
Y. Vaisshnavi
Author(s):  
David Baneres ◽  
Jordi Conesa

Is my professional knowledge outdated? Do I have the skills needed for the new challenges of the society? What knowledge do I lack to qualify for a job I like? What universities can I address to get knowledge that improves my employment expectations? These are relevant questions that all employees have done in any moment of their life. In addition, when there are high rates of unemployment and job offers that keep unfilled, the answers to these questions are even more relevant. Answering such questions open new opportunities for employed and unemployed people, by allowing them to design a formative plan according to their skills and expectations. It also provides evidences to employers about the skills and knowledge of the society, making them more aware of the skills of their potential future employees. The companies also will have more knowledge to design the professional career of their employees according to the company needs and the knowledge and skills of their employees. This paper proposes a system that helps people by showing which knowledge and skills a person misses for a given job position and what university courses the person can take to acquire the required skills and knowledge. The system has been implemented as a recommender system that helps users in planning their life-long learning. The paper shows the architecture of the proposed system, a case study to explain how it works, a survey to validate its usefulness and usability and some conclusions after its first experimentation.


2021 ◽  
Author(s):  
Ashrf Althbiti ◽  
Shrooq Algarni ◽  
Tami Alghamdi ◽  
Xiaogang Ma
Keyword(s):  

AI Magazine ◽  
2015 ◽  
Vol 36 (3) ◽  
pp. 19-34
Author(s):  
Wei Li ◽  
Justin Matejka ◽  
Tovi Grossmann ◽  
George Fitzmaurice

In 2009 we presented the idea of using collaborative filtering within a complex software application to help users learn new and relevant commands (Matejka et al. 2009). This project continued to evolve and we explored the design space of a contextual software command recommender system and completed a six-week user study (Li et al. 2011). We then expanded the scope of our project by implementing CommunityCommands, a fully functional and deployable recommender system. CommunityCommands was a publically available plug-in for Autodesk’s flagship software application AutoCAD. During a one-year period, the recommender system was used by more than 1100 users. In this article, we discuss how our practical system architecture was designed to leverage Autodesk’s existing Customer Involvement Program (CIP) data to deliver in-product contextual recommendations to end-users. We also present our system usage data and payoff, and provide an in-depth discussion of the challenges and design issues associated with developing and deploying the software command recommender system. Our work sets important groundwork for the future development of recommender systems within the domain of end-user software learning assistance.


2009 ◽  
Vol 36 (4) ◽  
pp. 8071-8075 ◽  
Author(s):  
Yi-Fan Wang ◽  
Ding-An Chiang ◽  
Mei-Hua Hsu ◽  
Cheng-Jung Lin ◽  
I-Long Lin

2014 ◽  
Vol 04 (03) ◽  
pp. 20-28 ◽  
Author(s):  
Nidhi Kushwaha ◽  
Raman Goyal ◽  
Pramiti Goel ◽  
Sidharth Singla ◽  
Om Prakash Vyas

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