Business recommendation based on collaborative filtering and feature engineering – aproposed approach

Rokade, Prakash; D, Aruna Kumari

doi:10.11591/ijece.v9i4.pp2614-2619

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Customers' decisions are more likely to convert and become repeat users and have brand loyalty when the content is important to the consumer. Customers' decisions will always effect the business decisions [24]. The parameter settings of neural network models used in this research can be further improved as suggested by [25][26].…”

Section: Resultsmentioning

confidence: 99%

Exploration on digital marketing as business strategy model among Malaysian entrepreneurs via neurocomputing

Ibrahim

Kamaruddin

Ghani

et al. 2020

IJ-AI

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<span lang="EN-US">Artificial Intelligence is great when it comes to routine activities and vast amounts of data are analyzed. This can be done more quickly and efficiently than men. In the world of digital marketing, Artificial Intelligence is quickly coming into play. With Artificial Intelligence joining the digital marketing environment, predicting user behavior, search cycles, and much more will be easier. This can support websites that are highly user-friendly for organizations. Moreover, with the aid of Artificial Intelligence, content creation has become a faster and easier task for brands. Practically, a company's degree of enterprise marketing can have an effect on its overall business efficiency. Entrepreneurial marketing is driven by entrepreneurial opportunities which involves the proactive identification and exploitation of opportunities for acquiring and retaining profitable customers through Digital approaches to risk management, resource leveraging and value creation. This research was done by collecting data using semi structure questionnaire distributed to 169 start up owners in Klang Valley area. Using two-layer 6-3-1 with hyperbolic tangent-purelin configurations neural network model, it was found that proactiveness, risk taking, resource leveraging, opportunity focus, intensity and value add are the significant factors towards digital marketing respectively. It is expected that the findings would give some inputs to the Malaysian entrepreneurs on innovative digital marketing in their businesses, regardless the sizes.</span>

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Section: Resultsmentioning

confidence: 99%

Exploration on digital marketing as business strategy model among Malaysian entrepreneurs via neurocomputing

Ibrahim

Kamaruddin

Ghani

et al. 2020

IJ-AI

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“…− MovieLens-1M: it contains the information about 6,040 users, 3,900 movies and 1,000,209 ratings in the range [0.5-5]. The range of rating from 0 to5, 0 with being worst and 5 represent the best value [25]. This section presents a comprehensive evaluation of the proposed model on three real-world data 1M MovieLens, 10M and 20M MovieLens.…”

Section: Methodology 31 Datasetmentioning

confidence: 99%

An enhanced framework for solving cold start problem in movie recommendation systems

Elzeheiry

Mekky²,

Atwan³

et al. 2021

IJEECS

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<p>Recommendation systems (RSs) are used to obtain advice regarding decision-making. RSs have the shortcoming that a system cannot draw inferences for users or items regarding which it has not yet gathered sufficient information. This issue is known as the cold start issue. Aiming to alleviate the user’s cold start issue, the proposed recommendation algorithm combined tag data and logistic regression classification to predict the probability of the movies for a new user. First using alternating least square to extract product feature, and then diminish the feature vector by combining principal component analysis with logistic regression to predict the probability of genres of the movies. Finally, combining the most relevant tags based on similarity score with probability and find top N movies with high scores to the user. The proposed model is assessed using the root mean square error (RMSE), the mean absolute error (MAE), recall@N and precision@N and it is applied to 1M, 10M and 20M MovieLens datasets, resulting in an accuracy of 0.8806, 0.8791 and 0.8739.</p>

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“…The similarity calculation is mainly based on user preferences, and the user-based collaborative filtering recommendation algorithm is used for search recommendation [18][19]. The main use of search intelligent recommendation is to calculate the similarity between users using cosine similarity algorithm, and then recommend search matching data for users based on the level of similarity [20]. The similarity algorithm for searching between students and enterprises on university employment platforms is as follows:…”

Section: Search Intelligent Recommendation Algorithmmentioning

confidence: 99%

Design of a Cloud Computing Based Employment Platform for College Students

2023

IJMC

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Currently, the employment situation is becoming increasingly severe, which involves the stability and development of the national economy. Therefore, universities should strengthen the information exchange between talent demand and talent supply, improve the level of employment guidance services, fully play its employment guidance function, and establish an exchange platform between students, enterprises, and employers in our school. At present, the construction of employment service platforms in universities is still incomplete, with some shortcomings that cannot meet the diverse needs of society for graduates. In order to meet the employment needs of college graduates, this article combines the Internet and human resources to build a cloud computing employment service database and a university employment service information exchange platform. In traditional employment service platforms, due to the lack of corresponding information technology equipment and software provided by schools, students are unable to timely obtain relevant employment information and enterprise recruitment situation, seriously affecting the quality of students' employment. Therefore, this paper have developed a college student employment platform based on Hadoop cloud computing platform, which realizes the collection, processing and storage of graduate employment information and enterprise recruitment information data, and carries out client communication through the Strust2 framework. After testing the employment platform for college students, we found that the system has excellent response speed. After using the system, the student signing rate and employment rate are as high as 99% and 100%, which fully proves that the employment system can provide excellent services for enterprises and students, thereby effectively improving employment rates. At the same time, it has also been verified that the algorithm proposed in this study is feasible and can effectively reduce human resource costs.

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Business recommendation based on collaborative filtering and feature engineering – aproposed approach

Cited by 5 publications

References 12 publications

Exploration on digital marketing as business strategy model among Malaysian entrepreneurs via neurocomputing

Exploration on digital marketing as business strategy model among Malaysian entrepreneurs via neurocomputing

An enhanced framework for solving cold start problem in movie recommendation systems

Design of a Cloud Computing Based Employment Platform for College Students

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