A state-of-the-art survey on recommendation system and prospective extensions

Patel, Krupa; Patel, Hiren

doi:10.1016/j.compag.2020.105779

Cited by 46 publications

(10 citation statements)

References 35 publications

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“…Such systems are supposed to "understand" that "children's films" and "films for children" mean the same. For synonymy in recommender systems, see Moon (2019), a general review is presented in Patel & Patel (2020).…”

Section: Application-oriented Tasks Of Computational Linguisticsmentioning

confidence: 99%

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Computational linguistics and discourse complexology: Paradigms and research methods

Solovyev

Solnyshkina

McNamara

2022

Russian Journal of Linguistics

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The dramatic expansion of modern linguistic research and enhanced accuracy of linguistic analysis have become a reality due to the ability of artificial neural networks not only to learn and adapt, but also carry out automate linguistic analysis, select, modify and compare texts of various types and genres. The purpose of this article and the journal issue as a whole is to present modern areas of research in computational linguistics and linguistic complexology, as well as to define a solid rationale for the new interdisciplinary field, i.e. discourse complexology. The review of trends in computational linguistics focuses on the following aspects of research: applied problems and methods, computational linguistic resources, contribution of theoretical linguistics to computational linguistics, and the use of deep learning neural networks. The special issue also addresses the problem of objective and relative text complexity and its assessment. We focus on the two main approaches to linguistic complexity assessment: “parametric approach” and machine learning. The findings of the studies published in this special issue indicate a major contribution of computational linguistics to discourse complexology, including new algorithms developed to solve discourse complexology problems. The issue outlines the research areas of linguistic complexology and provides a framework to guide its further development including a design of a complexity matrix for texts of various types and genres, refining the list of complexity predictors, validating new complexity criteria, and expanding databases for natural language.

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Section: Application-oriented Tasks Of Computational Linguisticsmentioning

confidence: 99%

“…По проблеме синонимии в рекомендательных системах см. работу (Moon 2019), а общий обзор представлен в (Patel & Patel 2020).…”

Section: прикладные задачи компьютерной лингвистикиunclassified

Computational linguistics and discourse complexology: Paradigms and research methods

Solovyev

Solnyshkina

McNamara

2022

Russian Journal of Linguistics

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“…In order to anticipate a user's interests, these systems employ a wide range of data processing techniques to examine the user's past activity and the actions of users who are similar to the user. There have been a number of attempts made by different authors to find a solution to this issue so that the results can be more accurate [10] [11] [12] [13]. They do this by utilising opinion mining techniques in conjunction with neural networks in order to get around the cold-start problem that is inherent with content-based recommendation systems.…”

Section: Related Workmentioning

confidence: 99%

Machine learning for weather-specific crop recommendation

Pachade¹,

Sharma²

2022

ijhs

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Agriculture and its related sectors are unquestionably the most important sources of income in rural India. Additionally, having a big impact on the nation's GDP is the agriculture sector. The sector of agriculture is so large, which is great for the nation. The crop production per hectare, however, falls short of international standards. This is one of the most likely causes of the greater rate of suicide among marginal farmers in India. This research proposed best recommendation system. The proposed system recommends crops for farmers to grow based on input from the farmer’s field, such as the temperature, soil, moisture, and nutrient like NPK, pH, and rainfall. Machine learning algorithms allow for optimal crop selection to be made in light of all relevant parameters. three popular machine learning algorithms were tested in this study which includes the Decision Tree, the Random Forest (RF), and the Logistic Regression. The Random Forest among them demonstrated the highest outcomes with 99.32% accuracy.

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“…CBF, one of the most signi cant models in RSs, which are of a high importance for CR, as well as yield estimation. Examples of CBF model applications included in [66], the model of this study is based on the contents that use soil and crop properties, then suggests the list of ve high priority crops based on the corresponding properties between the crop and the land for matching soil properties. The algorithm takes two inputs, the land soil details and the re-quired property value for each crop.…”

Section: Flmentioning

confidence: 99%

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

Ommane

RHANBOURI

CHOUIKH

et al. 2022

Preprint

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This paper draws a systematic literature review about the use of Machine learning based recommender systems for crop selection, with respect to the PRISMA protocol for systematic reviews. The second section, describes an overview of existing recommender systems in literature. The outline of this study is explained, as well as the method of content analysis used in this article to sort out the papers is introduced in the third section. In the fourth section, the selection process and the literature review matrix are provided. Additionally, the evolvement of research on crop recommendation over the years is considered, a detailed study of the main input features is done. Further, the current challenges found in crop recommendation are listed. The fifth section discusses the outcomes of the study. The last section recapitulates the outcomes of the study, shedding the light of research perspectives worthwhile pursuing.

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A state-of-the-art survey on recommendation system and prospective extensions

Cited by 46 publications

References 35 publications

Computational linguistics and discourse complexology: Paradigms and research methods

Computational linguistics and discourse complexology: Paradigms and research methods

Machine learning for weather-specific crop recommendation

Machine Learning based Recommender Systems for Crop Selection: A Systematic Literature Review

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