Content Based Call for Papers Recommendation to Researchers

Asim, Muhammad; Khusro, Shah

doi:10.1109/icosst.2018.8632174

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Asim & Khusro [8]). They are regarded as the metrics for decision support as they can be used to differentiate between right and wrong predictions in an RS.…”

Section: Relationship Between Assessment Methods In Recommender Systemmentioning

confidence: 99%

Assessment Methods for Evaluation of Recommender Systems: A Survey

Kuanr

Mohapatra

2021

Foundations of Computing and Decision Sciences

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The recommender system (RS) filters out important information from a large pool of dynamically generated information to set some important decisions in terms of some recommendations according to the user’s past behavior, preferences, and interests. A recommender system is the subclass of information filtering systems that can anticipate the needs of the user before the needs are recognized by the user in the near future. But an evaluation of the recommender system is an important factor as it involves the trust of the user in the system. Various incompatible assessment methods are used for the evaluation of recommender systems, but the proper evaluation of a recommender system needs a particular objective set by the recommender system. This paper surveys and organizes the concepts and definitions of various metrics to assess recommender systems. Also, this survey tries to find out the relationship between the assessment methods and their categorization by type.

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“…Asim & Khusro [8]). They are regarded as the metrics for decision support as they can be used to differentiate between right and wrong predictions in an RS.…”

Section: Relationship Between Assessment Methods In Recommender Systemmentioning

confidence: 99%

Assessment Methods for Evaluation of Recommender Systems: A Survey

Kuanr

Mohapatra

2021

Foundations of Computing and Decision Sciences

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“…Asim and Khusro [7] proposed a model to create a content-based recommender system that uses a specific amount of selected features according to the researchers' preferences. By doing this the authors hope to address the previous issues regarding content-based recommender systems which faced problems like the limited content analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

A Citation Recommendation System Using Deep Reinforcement Learning

Nair

Paul

George

2021

Lecture Notes on Data Engineering and Communications Technologies

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Recommender systems have seen tremendous growth in the last few years due to the emergence of web services like YouTube, Netflix, and Amazon, etc. An excessive amount of data is being utilized to give proper recommendations to the users. The number of research articles getting published every day is increasing exponentially and thus an efficient model is required to provide accurate and relevant recommendations to the research scholars. The proposed Deep Reinforcement Recommender for Citations (DRRC) model uses reinforcement learning to train the available citation network to achieve the most relevant recommendations. The proposed DRRC model outperforms the state-of-the-art models.

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