A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation

E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customer’s need is crucial for retailers to increase revenue and retain customers’ loyalty. As users’ interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand users’ long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product recommendation as an application and classifies SRecSys under three main categories as: (i) traditional approaches (sequence similarity, frequent pattern mining and sequential pattern mining), (ii) factorization and latent representation (matrix factorization and Markov models) and (iii) neural network-based approaches (deep neural networks, advanced models). This classification contributes towards enhancing the understanding of existing SRecSys in the literature with the application domain of e-commerce product recommendation and provides current status of the solutions available alongwith future research directions. Furthermore, a classification of surveyed systems according to eight important key features supported by the techniques along with their limitations is also presented. A comparative performance analysis of the presented SRecSys based on experiments performed on e-commerce data sets (Amazon and Online Retail) showed that integrating sequential purchase patterns into the recommendation process and modeling users’ sequential behavior improves the quality of recommendations

Integration of Data Mining and Data Warehousing: a practical methodology

The ever growing repository of data in all fields poses new challenges to the modern analytical systems. Real-world datasets, with mixed numeric and nominal variables, are difficult to analyze and require effective visual exploration that conveys semantic relationships of data. Traditional data mining techniques such as clustering clusters only the numeric data. Little research has been carried out in tackling the problem of clustering high cardinality nominal variables to get better insight of underlying dataset. Several works in the literature proved the likelihood of integrating data mining with warehousing to discover knowledge from data. For the seamless integration, the mined data has to be modeled in form of a data warehouse schema. Schema generation process is complex manual task and requires domain and warehousing familiarity. Automated techniques are required to generate warehouse schema to overcome the existing dependencies. To fulfill the growing analytical needs and to overcome the existing limitations, we propose a novel methodology in this paper that permits efficient analysis of mixed numeric and nominal data, effective visual data exploration, automatic warehouse schema generation and integration of data mining and warehousing. The proposed methodology is evaluated by performing case study on real-world data set. Results show that multidimensional analysis can be performed in an easier and flexible way to discover meaningful knowledge from large datasets

Scalable aggregation predictive analytics: a query-driven machine learning approach

We introduce a predictive modeling solution that provides high quality predictive analytics over aggregation queries in Big Data environments. Our predictive methodology is generally applicable in environments in which large-scale data owners may or may not restrict access to their data and allow only aggregation operators like COUNT to be executed over their data. In this context, our methodology is based on historical queries and their answers to accurately predict ad-hoc queries’ answers. We focus on the widely used set-cardinality, i.e., COUNT, aggregation query, as COUNT is a fundamental operator for both internal data system optimizations and for aggregation-oriented data exploration and predictive analytics. We contribute a novel, query-driven Machine Learning (ML) model whose goals are to: (i) learn the query-answer space from past issued queries, (ii) associate the query space with local linear regression & associative function estimators, (iii) define query similarity, and (iv) predict the cardinality of the answer set of unseen incoming queries, referred to the Set Cardinality Prediction (SCP) problem. Our ML model incorporates incremental ML algorithms for ensuring high quality prediction results. The significance of contribution lies in that it (i) is the only query-driven solution applicable over general Big Data environments, which include restricted-access data, (ii) offers incremental learning adjusted for arriving ad-hoc queries, which is well suited for query-driven data exploration, and (iii) offers a performance (in terms of scalability, SCP accuracy, processing time, and memory requirements) that is superior to data-centric approaches. We provide a comprehensive performance evaluation of our model evaluating its sensitivity, scalability and efficiency for quality predictive analytics. In addition, we report on the development and incorporation of our ML model in Spark showing its superior performance compared to the Spark’s COUNT method

Anomaly detection and event mining in cold forming manufacturing processes

Predictive maintenance is one of the main goals within the Industry 4.0 trend. Advances in data-driven techniques offer new opportunities in terms of cost reduction, improved quality control, and increased work safety. This work brings data-driven techniques for two predictive maintenance tasks: anomaly detection and event prediction, applied in the real-world use case of a cold forming manufacturing line for consumer lifestyle products by using acoustic emissions sensors in proximity of the dies of the press module. The proposed models are robust and able to cope with problems such as noise, missing values, and irregular sampling. The detected anomalies are investigated by experts and confirmed to correspond to deviations in the normal operation of the machine. Moreover, we are able to find patterns which are related to the events of interest

Analytical study and computational modeling of statistical methods for data mining

Today, there is tremendous increase of the information available on electronic form. Day by day it is increasing massively. There are enough opportunities for research to retrieve knowledge from the data available in this information. Data mining and app

Navigating Diverse Datasets in the Face of Uncertainty

When exploring big volumes of data, one of the challenging aspects is their diversity of origin. Multiple files that have not yet been ingested into a database system may contain information of interest to a researcher, who must curate, understand and sieve their content before being able to extract knowledge. Performance is one of the greatest difficulties in exploring these datasets. On the one hand, examining non-indexed, unprocessed files can be inefficient. On the other hand, any processing before its understanding introduces latency and potentially un- necessary work if the chosen schema matches poorly the data. We have surveyed the state-of-the-art and, fortunately, there exist multiple proposal of solutions to handle data in-situ performantly. Another major difficulty is matching files from multiple origins since their schema and layout may not be compatible or properly documented. Most surveyed solutions overlook this problem, especially for numeric, uncertain data, as is typical in fields like astronomy. The main objective of our research is to assist data scientists during the exploration of unprocessed, numerical, raw data distributed across multiple files based solely on its intrinsic distribution. In this thesis, we first introduce the concept of Equally-Distributed Dependencies, which provides the foundations to match this kind of dataset. We propose PresQ, a novel algorithm that finds quasi-cliques on hypergraphs based on their expected statistical properties. The probabilistic approach of PresQ can be successfully exploited to mine EDD between diverse datasets when the underlying populations can be assumed to be the same. Finally, we propose a two-sample statistical test based on Self-Organizing Maps (SOM). This method can outperform, in terms of power, other classifier-based two- sample tests, being in some cases comparable to kernel-based methods, with the advantage of being interpretable. Both PresQ and the SOM-based statistical test can provide insights that drive serendipitous discoveries