For more than ten years, online job boards have provided their services to both job seekers and employers who want to hire potential candidates. The provided services are generally based on traditional information retrieval techniques, which may not be appropriate for both job seekers and employers. The reason is that the number of produced results for job seekers may be enormous. Therefore, they are required to spend time reading and reviewing their finding criteria. Reciprocally, recruitment is a crucial process for every organization. Identifying potential candidates and matching them with job offers requires a wide range of expertise and knowledge. This article proposes a reciprocal recommendation based on bi-directional correspondence as a way to support both recruiters’ and job seekers’ work. Recruiters can find the best-fit candidates for every job position in their job postings, and job seekers can find the best-match jobs to match their resumes. We show how machine learning can solve problems in natural language processing of text content and similarity scores depending on job offers in major Saudi cities scraped from Indeed. For bi-directional matching, a similarity calculation based on the integration of explicit and implicit job information from two sides (recruiters and job seekers) has been used. The proposed system is evaluated using a resume/job offer dataset. The performance of generated recommendations is evaluated using decision support measures. Obtained results confirm that the proposed system can not only solve the problem of bi-directional recommendation, but also improve the prediction accuracy.

/pdf/nlp-based-bi-directional-recommendation-system-towards-21tm4swn.pdf

NLP-Based Bi-Directional Recommendation System: Towards Recommending Jobs to Job Seekers and Resumes to Recruiters

With one of the most potent technologies ever developed in human history, the world is moving toward a new digitalized era. These technological advancements are enabling people to make things that, in the past, were merely the stuff of fairy tales. The model put out by this research incorporates one of the newest and most potent technologies of the decade. This research proposes to integrate the 5G network with the industrial internet of things, which is based on machine learning to develop an intelligent machine capable of mimicking humans. A system of this power is extremely susceptible to issues like hacking, cyberattacks, and other issues. This problem is solved with the blockchain. Since blockchain offers a decentralized approach to maintaining transparency, the research incorporates it into the model to make it more efficient and secure. IoT with blockchain has been the subject of other studies, but this study is an enhanced version that also incorporates industrial IoT with AI to create an intelligent internet of things. 

Machine Learning and Blockchain for 5G-Enabled IIoT

Abstract Content-based recommendation technology is widely used in the field of e-commerce and education because of its intuitive and easy to explain advantages. However, due to the congenital defect of insufficient semantic analysis of TF-IDF vector space model, the traditional content-based recommendation technology has the problem of insufficient semantic analysis in item modeling, fails to consider the role of semantic information in knowledge expression and similarity calculation, and is not accurate enough in calculating item content similarity. The items with semantic relevance in content can not be well mined. The research goal of this paper is to improve the semantic analysis ability of the traditional content-based recommendation algorithm by integrating semantic information with TF-IDF vector space model for item modeling and similarity calculation and proposed an improved content recommendation algorithm integrating semantic information. In order to prove the effectiveness of the proposed method, several groups of experiments are carried out. The experiments results showed that the overall performance of the proposed algorithm in this paper is the best and relatively stable. This verified the validity of our method. 

/pdf/improved-content-recommendation-algorithm-integrating-g41476uz.pdf

Improved content recommendation algorithm integrating semantic information

YouTube, Netflix and some other OTT applications follow a drastic and systematic approach to how they portray movie recommendations to their users in order to keep them attracted and interested in their websites and applications. The concepts of Machine Learning and Artificial Intelligence are being used in so many different ways ranging from smallest keyword suggestions while users’ type, to the highly complex recommendation systems such as YouTube, that we solely use so often. In other words, recommendation system is used to suggest similar items to the user. The items can be movies, products, videos or anything depending on organizations or industries. Some modes of approach includes either using a candidate generation model or a ranking model to sort various forms of information which could vary from time spent while watching the videos to the age group of people watching the same videos. proposed work implemented using KNN machine learning model and obtained an accuracy of 98%. 

YouTube and Movie Recommendation System Using Machine Learning

In recent years, recommender systems have gained a lot of traction in both the commercial world and the academic research community. The term “recommendation system for facial skin care” refers to an automated system that gives consumers advice on which facial skin care products to use based on their specific skin type, skin issues, and preferences. Product recommendation systems analyze user data using a variety of methods, including collaborative filtering, content-based filtering, and machine learning algorithms. Better skincare results may be achieved through the usage of a suggestion system in which consumers receive advice related to their own skin issues. The problem with current models is that they don’t adequately address users’ issues and concerns while being based on collaborative or content-based filtering. In order to create an effective face skincare model, the authors of this study suggest a hybrid approach that integrates several existing methods (including KNN, CNN, Transfer Learning of Efficient Net BO, and a content-based filtering recommendation engine). The user’s skin tone, skin type, and acne severity are all inputs into the algorithm, which then outputs a recommendation for the most suitable product. The results of a comparison between the suggested model and other models prove its superiority. Using a hybrid model that takes into account a number of different methodologies and user inputs, the article aspires to deliver a more all-encompassing and individualized solution for facial skincare recommendations. The proposed model has the potential to enhance the precision and efficiency of current skincare recommendation systems. The implementation of EfficientNet B0 improves the model’s accuracy, which now has a validation accuracy of 80% and a training accuracy of 87.10%. 

Efficient Net-based Expert System for Personalized Facial Skincare Recommendations

Currently, the user profile based online recommender system has become a hit both in research and engineering domain. Accurately capturing users' profile is the key of recommendation. Recently, lots of researches on user profile extraction have been launched, including content-based recommendation. To better capture users' profiles, a three-step profiling method is adopted in this work. (1) Purchase item prediction is made based on Logistic Regression. (2) Purchase category prediction is made based on support vector machine (SVM), and (3) User's rating prediction is made based on convolutional neural network (CNN) and Long Short-Term Memory (LSTM). This work outperformed the baseline model on the user dataset collected from Amazon. So, in conclusion, the work has the ability of giving reasonable recommendation for users who would like to purchase online. In the future, the video signal processing techniques will also be taken under consideration to capture users' face expression for better recommendation.

Content-Based Recommendation Using Machine Learning

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Content-Based Recommendation Using Machine Learning