AN ANALYTICAL STUDY ON DESIGNING INTELLIGENT RECOMMENDATION SYSTEMS USING BIG DATA

In recent years, with the continuous development of the country’s Internet platforms, China has gradually entered the e-commerce era of national online shopping, and more and more e-commerce platforms and stores have adopted intelligent recommendation systems to increase transaction rates. However, it is not easy for consumers to filter out the products they want from a large amount of information. The emergence of intelligent recommendation systems provides great convenience for people to screen out personalized products that meet their own characteristics. However, the algorithms used in traditional recommendation technology focus on the single-computer environment and do not consider the performance of the recommendation method when distributed parallel processing is required in the big data environment, so it cannot meet the personalized needs of users in the big data environment. Aiming at the new requirements for the development of e-commerce intelligent recommendation technology in the big data environment, this paper uses the big data processing technology based on cloud computing and focuses on the realization technology of the e-commerce intelligent recommendation algorithm and the comprehensive evaluation method of the recommendation system in the big data environment. A prototype system of personalized intelligent recommendation based on cloud computing has been developed, which is of great importance to meet the needs of e-commerce personalized intelligent recommendation in the big data environment, improve the effectiveness, scale, and real-time performance of the personalized intelligent recommendation system, and improve the level of personalized precision marketing., which is of theoretical significance and economic value.

The research aims to solve the challenge of filtering out the content that users are really interested in from massive data. In this paper, an intelligent recommendation system named multi-source fusion deep collaborative recommendation algorithm (MSDC-Rec) is proposed, which combines collaborative filtering, content filtering and deep learning (DL) technology to improve the accuracy and diversity of the recommendation system. MSDC-Rec algorithm integrates user behavior data, item attribute data and social network information, and uses deep neural network for advanced feature extraction and pattern learning, thus accurately portraying user portraits and realizing personalized and accurate recommendation services. The experimental results show that MSDC-Rec is superior to the traditional algorithm in accuracy, recall, F1 score and personalized index, and has low root mean square error (RMSE) and acceptable real-time. This study not only solves the problems of data sparsity and cold start, but also significantly improves the accuracy and personalization of recommendation, which provides a new idea for the development of intelligent recommendation system.

This study focuses on developing an advanced tourism destination recommendation system combining big data and artificial intelligence (AI). AI is one of the most advanced technologies in various applications, such as healthcare, industry and various intelligent applications. This study focused on tourism, which helps guide tourist attractions according to the users’ interests. However, reality data are commonly insufficient, and specific traditional measures are not able to be figured out due to the absence of users’ shared rating items. Furthermore, user preferences are ignored by conventional filtering techniques, which leads to a notable drop in suggestion accuracy. This study presents an advanced solution called HITRS — A Hybrid Intelligent Tourism Recommendation System to address these issues. The proposed system combines the strength of collaborative and content-based filtering techniques to provide an accurate recommendation system regarding users’ interests. Here, collaborative filtering is improved with the Jeffries–Matusita Distance (JMSD) algorithm to find similarities between user interests based on their ratings and preferences. At the same time, content-based filtering uses machine learning methods more effectively, such as Google Cloud Vision API, to filter related information from user-generated content such as social media like Instagram pics. Here, the Term Frequency-Inverse Document Frequency (TF-IDF) approach is used to direct these properties. The Cosine Similarity Index is used to compare these properties with the features of tourist attractions. Additionally, the proposed HITRS performs large data volumes from various sources like social media, geolocation and search history to create complete user profiles. Real-time data processing ensures accurate suggestions and the latest trends. The system’s architecture is built to manage big data details, providing essential patterns and information. Through its combined advantages, the hybrid approach improves suggestion accuracy and overcomes the drawbacks of individual filtering techniques. The user-friendly environment of the proposed system allows travelers to communicate easily with the system, providing a simple and pleasurable search for new places. This solution can transform the tourism sector completely by delivering smart, data-driven suggestions regarding every traveler’s desires.

Among thousands of alternatives, most of the time online customers cannot easily decide on which product to purchase or service to utilize. In order to assist online customers in their decision-making process, business owners have started to make their online platforms more intelligent by enhancing their platforms with intelligent recommender systems. Recommender systems, also known as recommender agents or intelligent agents, are intelligent software that provide easily accessible, personalized, highly relevant, and high-quality recommendations to customers in various online platforms. This chapter discusses different types of recommender systems and provides use case examples of recommender systems in various e-commerce platforms. This chapter shows how recommender systems make life easier for online customers in the constantly developing and growing internet environment. This chapter also discusses the challenges posed by recommender systems to online customers.

The emergence of big data platforms has alleviated the restrictions of space and time on people's enjoyment of music information services. The need to improve user experience has become very urgent. One of the effective solutions to information overload is the recommendation system, which can help people discover interesting content from complex information. Therefore, the purpose of this article is to study a personalized film and television recommendation system based on a big data platform. This article first introduces the core technology of big data through an overview of the basic theory of big data. And combined with the traditional film and television recommendation method, analyze the existing problems and shortcomings, and analyze and discuss it with the core technology of big data. This article systematically expounds the overall process, system composition design and algorithm analysis of the personalized film and television recommendation system based on the big data platform. Experimental research shows that the algorithm proposed in this paper is better than the traditional User-basedCF strategy and Item-basedCF strategy under the same number of movies recommended by Hit-rate.

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data. Such statistical model form big data will provide us with more comprehensive understanding of human physiology and disease.

The objective is a neural-based feature selection in intelligent recommender systems. In particular, a hybrid neural genetic architecture is modeled based on human nature, interactions, and behaviour. The main contribution of this chapter is the development of a novel genetic algorithm based on human nature, interactions, and behaviour. The novel genetic algorithm termed “Buabin Algorithm” is fully integrated with a hybrid neural classifier to form a Hybrid Neural Genetic Architecture. The research presents GA in a more attractive manner and opens up the various departments of a GA for active research. Although no scientific experiment is conducted to compare network performance with standard approaches, engaged techniques reveal drastic reductions in genetic operator operations. For illustration purposes, the UCI Molecular Biology (Splice Junction) dataset is used. Overall, “Buabin Algorithm” seeks to integrate human related interactions into genetic algorithms as imitate human genetics in recommender systems design and understand underlying datasets explicitly.

Modelling and analysis of big data platform group adoption behaviour based on social network analysis

Due to the low cognitive ability of children, the intelligent recommendation system should assist children in learning with the assistance of parents. Therefore, the intelligent recommendation system needs to evaluate the ability of users (children learners and their parents), and assign learning content with suitable difficulty Match with the user’s ability, assist the user to learn efficiently, and gradually cultivate the user’s learning interest and habits, and can be a useful supplement to the user’s regular learning. This paper uses JAVA EE language to develop an intelligent recommendation system for children’s learning platform based on Hadoop big data framework and machine learning algorithms. Learning platform with intelligent recommendation function. By analyzing the cognitive ability of children, and using the collaborative recommendation algorithm to recommend learning resources suitable for this age stage for children, in order to improve learning efficiency and cultivate children’s learning interest from an early age, at the same time, the resource library in the learning platform has more Practical value.

Recommender systems are used widely in each and every web services to recommend and suggest user interested information or products. The advancement of digital information era and pain in traversing the vast amount of information made users to realise the importance of intelligent recommender systems. Intelligent recommender system suffers over many issues some among them are scrutability, scalability and in-memory purchase problem. In-memory purchase problem is one of the currently persisting recommender system problems. In this paper the deep learning models based recommendation scheme are framed to address the scrutability and in-memory purchase issue of the recommender system. Initially the working of deep learning models like feed forward and recurrent neural network are discussed. Then the recommendation schemes based on the deep learning models in addressing the issues of the recommender system are detailed.

The introduction of computerized formative assessments in the classroom has opened a new area of effective progress monitoring with more accessible test administrations. With computerized formative assessments, all students could be tested at the same time and with the same number of test administrations within a school year. Alternatively, the decision for the number and frequency of such tests could be made by teachers based on their observations and personal judgments about students. However, this often results in rigid test scheduling that fails to take into account the pace at which students acquire knowledge. To administer computerized formative assessments efficiently, teachers should be provided with systematic guidance regarding effective test scheduling based on each student's level of progress. In this study, we introduce an intelligent recommendation system that can gauge the optimal number and timing of testing for each student. We discuss how to build an intelligent recommendation system using a reinforcement learning approach. Then, we present a case study with a large sample of students' test results in a computerized formative assessment. We show that the intelligent recommendation system can significantly reduce the number of testing for the students by eliminating unnecessary test administrations where students do not show significant progress (i.e., growth). Also, the proposed recommendation system is capable of identifying the optimal test time for students to demonstrate adequate progress from one test administration to another. Implications for future research on personalized assessment scheduling are discussed.

This research proposes the method to develop the recommendation system of automobile parts using task ontology technology. The proposed intelligent recommendation system is designed to learn the assembly process of automobile parts and the automobile parts are composed by ontology method for the recommendation of the parts. Using hierarchical taxonomy based on is-a relationship, the relationship between each part that makes up automotive engine was set. Each part has each different weighted value according to the knowledge of automobile experts. The weighted value is created by the number of selection that the users of the automobile recommendation system select while using the system and the final value calculated by the multiplication of the weighted value, which is recorded within the system. As a result, the users can easily identify which factor in which part is important by the output in the order of the priority. The intelligent recommendation system for automobile parts is a system to inform of the assembly, the usage and the importance of automobile parts without any specialized knowledge by expressing the parts that are closely related with the applicable parts when selecting any part on the basis of the generated data for the automobile parts that are difficult to access by users.

Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyze it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes. With regard to the definition of big data, IBM Company uses volume, velocity, variety, value and veracity as 5Vs to summarize the concept of big data. There are different types of big data, for example, structured, semi-structured and un-structured data. The contents of big data can be text data, audio data, video data and still image and it indicates that the big data may have diverse data types as well as data qualities. Big data has variety of sources such as healthcare center, commercial system, industries, social media, telecommunication, transportation, sensor machines and others. In this paper, I reviewed three the most security challenging perspectives and I studied lack of concentrations in these areas by most research works. To confirm security in the big data platforms, it is critical to ascertain the data rendering points and their security techniques to safeguard the data in this pacing digital world. Then I envisage directions for the future research. In this paper, I have reviewed the big data sources and its security issues in the three directions such as data at rest, data at communication and data in process/use. Keywords: Big Data, Big Data Security, Big Data source, Attribute based encryption, storage path, Transport layer security, secure shell, Cloud service Provider DOI: 10.7176/CEIS/12-3-01 Publication date: November 30 th 2021

Cross-domain recommender systems and transfer learning approaches are useful to help integrate knowledge from different places, so that we alleviate some existing problems (such as the cold-start problem), or improve the quality of recommender systems. With the advantages of these techniques, we host the first international workshop on intelligent recommender systems by knowledge transfer and learning (RecSysKTL) to provide such a forum for academia researchers and application developers from around the world to present their work and discuss exciting research ideas or outcomes. The workshop is held in conjunction with the ACM Conference on Recommender Systems 2017 on August 27th at Como, Italy.

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.