FR: A Recommender for Finding Faculty Based on CF Technique

Nagpal, Diksha; Kaur, Sumit; Gujral, Shruti; Singh, Amritpal

doi:10.1016/j.procs.2015.10.091

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…The work focused primarily on imp roving the quality of reco mmendations in case of insufficient user-ratings. Diksha Nagpal et al [16] proposed a faculty recommender system. This system used both user-user collaborative filtering and item-item collaborative filtering.…”

Section: Related Workmentioning

confidence: 99%

Guide Me: A Research Work Area Recommender System

Sharma¹,

Vinayak²,

Singh³

2016

IJISA

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With the advent of Industrial Revolution, not only the choices in various fields increased but also the era of co mputer came into existence thereby revolutionizing the global market. People had numerous choices in front of them that often led to the confus ion about what product might actually fu lfill their requirements. So the need for having a system wh ich could facilitate the selection criteria and eradicate the dilemma of masses, was realized and ult imately recommender systems of present day world were introduced. So we can refer reco mmender systems as software tools that narrow down our choices and provide us with the most suitable suggestions as per our requirements. In this paper, we propose a novel recommender system i.e. RWARS (Research Work Area Reco mmender System) that will reco mmend research work area to a user based on his/her characteristics similar to those of other users. The characteristics considered here are hobbies, subjects of interests, programming skills and future objectives. The proposed system will use Cosine Similarity approach of Collaborative Filtering.

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Section: Related Workmentioning

confidence: 99%

Guide Me: A Research Work Area Recommender System

Sharma¹,

Vinayak²,

Singh³

2016

IJISA

View full text Add to dashboard Cite

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“…The results revealed that along with scalability issue, problems like data sparsity and cold-start problem were also resolved. Diksha Nagpal et al 17 introduced a novel faculty recommender system that recommends faculty members to the students, as per their relevant area of expertise and the ratings given to them by the students. This system used both user-user and item-item collaborative filtering approach.…”

mentioning

confidence: 99%

Research Work Area Recommendation based on Collaborative Filtering

Sharma¹,

Vinayak²,

Singh³

2017

IJEME

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In this work we present RWARS, a novel recommender system that recommends research work area. So far a number of recommender systems have been developed in the field of e-commerce, e-services, e-library, entertainment, tourism and social networking sites. However, when it comes to the area of education, not much work has been done. So to extend the utility of Recommender systems in the field of education, we have developed RWARS. We have used Cosine similarity and Tanimoto coefficient for developing our system. The aim of this work is to compare the results obtained using each approach to find the most optimal one. Evaluation parameters that have been used are: Mean square error, Root mean square error and Coverage. At present, RWARS is still in its initial phase and its applicability can be further enhanced by converting it into an online system and it surely will prove to be a great boon for young researchers to select the most appropriate research area for them.

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