A Novel Approach of Product Recommendation using Utility Based Association Rules

An exorbitant source of data is easily available but the actual task lies in using this data efficiently. In this article, the aim is to analyse the significant information embedded in the customer purchase behaviour to recommend new products to them. Our proposed scheme is a two-fold approach. First, the authors retrieve various product correlations from the vast library of user transactions. Based on these product correlations, utility based association rules are learned which depict the customer purchase behaviour. These rules are then applied in a recommender system for novel product suggestions to the customers. With improved utility based mining the paper tries to incorporate the usefulness of an item set like cost, profit or any other factor along with their frequency. In this paper the authors have deployed the rules discovered from both the conventional Frequent Item Set Mining and Improved Utility Based Mining on an e-commerce platform to compare the accuracy of the algorithms. The obtained results establish the efficacy of the proposed algorithm.

Pillar 3: Does banking regulation support stakeholders’ interest in banks financial and risk profile?

The paper examines the interest of the commercial banks’ stakeholders in Pillar 3 disclosures and their behaviour during the timing of serious market turbulence. The aim is to discover to which extent current banking regulation supports stakeholders’ interest in the information required by regulators to be disclosed. The examined data consists of log files that were pre-processed using web mining techniques and from which were extracted frequent item sets by quarters and evaluated in terms of quantity. The authors have proposed a methodology to evaluate frequent item sets of web parts over a dedicated time. Based on the verification of applied methodology on two commercial banks, the results show that stakeholders’ interest in disclosures is highest in the first quarter at each year and after turbulent times in 2009 their interests decreased. Moreover, the results suggest that stakeholders expressed higher interest than in regulatory required Pillar 3 information in the following group of information: Pillar3 related information, Annual reports, Information on Group. Following our results, the paper contributes to cover the gap in the research by analysing Pillar 3 disclosures and their compliance with regulatory requirements, which also increase the interest of the relevant stakeholders to conduce them as an effective market discipline tool.

Construction and Analysis of Scientific and Technological Personnel Relational Graph for Group Recognition

The key to the in-depth management of science and technology is to model the behavior characteristics of scientific and technological personnel and then find groups by analyzing the diverse associations among them. Aiming at the analysis of team relationship among scientific and technological personnel, this paper proposed a method to recognize the group of scientific and technological personnel based on relational graph. The relationship model of scientific and technological personnel was designed, and based on this, the entity and relationship recognition and extraction are performed on the structured and unstructured source data to construct a relational graph. An improved frequent item mining algorithm based on Hadoop was proposed, which enabled getting the group of scientific and technological personnel by mining and analyzing the data in the relational graph. In this paper, the proposed method was experimented on both open and private datasets, and compared with several classical algorithms. The results showed that the method proposed in this paper has a significant improvement in execution efficiency.

A Federated Learning Approach to Frequent Itemset Mining in Cyber-Physical Systems

Effective vector representation has been proven useful for transaction classification and clustering tasks in Cyber-Physical Systems. Traditional methods use heuristic-based approaches and different pruning strategies to discover the required patterns efficiently. With the extensive and high dimensional availability of transactional data in cyber-physical systems, traditional methods that used frequent itemsets (FIs) as features suffer from dimensionality, sparsity, and privacy issues. In this paper, we first propose a federated learning-based embedding model for the transaction classification task. The model takes transaction data as a set of frequent item-sets. Afterward, the model can learn low dimensional continuous vectors by preserving the frequent item-sets contextual relationship. We perform an in-depth experimental analysis on the number of high dimensional transactional data to verify the developed models with attention-based mechanism and federated learning. From the results, it can be seen that the designed model can help and improve the decision boundary by reducing the global loss function while maintaining both security and privacy.

Automatic Detection of a Student’s Affective States for Intelligent Teaching Systems

AutoTutor is an automated computer tutor that simulates human tutors and holds conversations with students in natural language. Using data collected from AutoTutor, the following determinations were sought: Can we automatically classify affect states from intelligent teaching systems to aid in the detection of a learner’s emotional state? Using frequency patterns of AutoTutor feedback and assigned user emotion in a series of pairs, can the next pair of feedback/emotion series be predicted? Through a priori data mining approaches, we found dominant frequent item sets that predict the next set of responses. Thirty-four participants provided 200 turns between the student and the AutoTutor. Two series of attributes and emotions were concatenated into one row to create a record of previous and next set of emotions. Feature extraction techniques, such as multilayer-perceptron and naive Bayes, were performed on the dataset to perform classification for affective state labeling. The emotions ‘Flow’ and ‘Frustration’ had the highest classification of all the other emotions when measured against other emotions and their respective attributes. The most common frequent item sets were ‘Flow’ and ‘Confusion’.