Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.

https://downloads.hindawi.com/journals/misy/2022/2854741.pdf

Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices

With an ascent in the development of web-based business, the utilization of credit cards for internet shopping has expanded significantly. This, in turn, has brought about a great deal of credit card fakes. However, once in a while. Consequently, the execution of effective fraud detection frameworks has turned out to be fundamental for all banks to limit their misfortunes as far as credit card transactions are concerned. Numerous advanced systems have been created to monitor different credit card exchanges in literature. In this way, individuals have been attempting their best to identify the extortion in credit card exchanges as much as they can. Various machine learning techniques have been applied to predict whether a particular transaction is fraudulent or not. The biggest challenge with the techniques is the unavailability of the balanced dataset. Which is due to the nature of the transaction: the fraud transactions are too less when compared to genuine transactions. This work handles the challenge by balancing the dataset. Five machine learning techniques: Random forest, Naive Bayes, Support Vector Machine, K-Nearest Neighbor and Logistic regression were applied on the balanced dataset with different sampling techniques such as Oversampling, Undersampling, Both sampling, ROSE and SMOTE. The performance metric AUC – ROC suggests that logistic regression performs with an accuracy of 97.04 % and precision of 99.99%.

Credit Card Fraud Detection Using Various Classification and Sampling Techniques: A Comparative Study

Nowadays machine learning algorithms are being used extensively in industrial applications. Many a times these algorithms are modified and fine tuned so as to improve the current products and get better results. In this paper, we analyse an industrial problem that was put forward in the ‘IDA 2016 challenge’ and propose an improved solution over the best solution identified as part of the challenge.

An Improved Random Forest Algorithm for classification in an imbalanced dataset.

Today, recommendation algorithms are widely used by companies in multiple sectors with the aim of increasing their profits or offering a more specialized service to their customers. Moreover, there are countless applications in which classification algorithms are used, seeking to find patterns that are difficult for people to detect or whose detection cost is very high. Sometimes, it is necessary to use a mixture of both algorithms to give an optimal solution to a problem. This is the case of the ADAGIO, a R&D project that combines machine learning (ML) strategies from heterogeneous data sources to generate valuable knowledge based on the available open data. In order to support the ADAGIO project requirements, the main objective of this paper is to provide a clear vision of the existing classification and recommendation ML systems to help researchers and practitioners to choose the best option. To achieve this goal, this work presents a systematic review applied in two contexts: scientific and industrial. More than a thousand papers have been analyzed resulting in 80 primary studies. Conclusions show that the combination of these two algorithms (classification and recommendation) is not very used in practice. In fact, the validation presented for both cases is very scarce in the industrial environment. From the point of view of software development life cycle, this review also shows that the work being done in the ML (for classification and recommendation) research and industrial environment is far from earlier stages such as business requirements and analysis. This makes it very difficult to find efficient and effective solutions that support real business needs from an early stage. It is therefore that the article suggests the development of new ML research lines to facilitate its application in the different domains.

/pdf/recommendation-and-classification-systems-a-systematic-1hxlwybmua.pdf

Recommendation and Classification Systems: A Systematic Mapping Study

The researchers need to access a large number of articles so as to keep abreast with recent developments in their respective research areas. Finding a relevant article from a huge collection of information, therefore, becomes a huge challenge. Recommender systems play a vital role in assisting researchers to filter correlated information from large volume articles. In this work, we have used the topic modeling approach, latent Dirichlet allocation (LDA), for recommending research articles based on user queries. We have collected reference articles of a query given by the user from the Wikipedia site. The proposed algorithm comprises four steps: 1. LDA first iteration to find the top N words. 2. LDA second iteration elicits weighted topic distribution. 3. Pointwise mutual information (PMI) obtains the correlation between the top N words and the weighted topic distribution words. 4. Reference articles are collected for top correlated words, and the top-ranked articles are recommended to the user by applying collaborative filtering algorithm. The two-pass LDA helps to avoid the ambiguities. The proposed algorithm performance is evaluated using the parameters of precision and recall. As compared to the results obtained from the existing recommender systems, our work demonstrates that the LDA-based algorithm gives a powerful recommender framework.

Recommending Research Article Based on User Queries Using Latent Dirichlet Allocation

Analysis and Performance of Collaborative Filtering and Classification Algorithms

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Analysis and Performance of Collaborative Filtering and Classification Algorithms