A Survey on Data Mining Methods Available for Recommendation System

Kumar, Niranjan; Sneha, Y. S.; Mungara, Jitendranath; Prasad, Srinivas

doi:10.1109/csitss.2017.8447672

Cited by 16 publications

(2 citation statements)

References 6 publications

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“…Peer to Peer (PP): This method has the ability to resolve the more substantial scale matter, at this time, each peer cluster which has the same perception associated to another dedicated, concerned peer.) It exploits the elementary antiquity to provide recommendations [32] .…”

Section: Modern Techniquesmentioning

confidence: 99%

Survey on Recommender System Using Deep Learning Networks

Jaiswal

2020

Artificial Intelligence Evolution

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In today’s times, the recommended system is a very powerful weapon of shoppers that is very helpful in advancing the Internet, personalized tendencies, and online shopping. The recommended system is used primarily for commercial benefit. The recommended system works on the strength of the user’s past shopping experience and its feedback, whether it is positive or negative. Hence the recommended system is also an innovative method. There is a deferred method of the recommended system which has its own advantages and disadvantages. In this paper, the Recommender system based on deep learning is proposed, and also discussed the challenges and issues which are related to the deep learning based recommender system. i.e., Accuracy, Cold Start Problem, Scalability States etc. In this paper, we have also discussed the work done so far, which has been given by various scientists / researchers / investigators. Advancement of machine learning and deep learning is very big, in today’s era. This study will help the Researcher to move forward.

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Section: Modern Techniquesmentioning

confidence: 99%

Survey on Recommender System Using Deep Learning Networks

Jaiswal

2020

Artificial Intelligence Evolution

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“…Many clients find that it's tough to get the relevant information that we require as there is a drastic increase with the popularity and amount of World Wide Web, even though when a client use a popular search engine like Google and yahoo. Generally these search engines [6] permit clients to specify the queries as simple as possible like keywords, by approaching the traditional information systems. Yet, this sequence of words given does not always provide the required information, hence it is most likely important to retrieve client's required information and to achieve client's satisfaction of given results provided by the search engine.…”

Section: Introductionmentioning

confidence: 99%

Query Recommendation for Search Engine Results Using Machine Learning

N¹,

Nirmala²,

Pranav³

et al. 2023

Advances in Transdisciplinary Engineering

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Now a days, web content over the World Wide Web is growing fast, it has become tougher to satisfy the necessities of the client’s queries results. This paper provides a technique for advising a sequence of queries that are similar with the client’s input search. The related searches are based on past given searches by the clients itself. This method tells about the clustering process where syntactical or semantically similar searches in groups are found. This also proposes some queries which are similar or related to the queries submitted by the client to get the required information which is relevant and efficient. This method does not only detect the similar and related searches and queries but can also rank them considering the similarity measure. And this technique is executed using real data sets from search engine query log. It gives queries in websites like Google, yahoo, Bing etc.

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