https://www.ijert.org/building-personalised-recommendation-system-with-big-data-and-hadoop-mapreduce https://www.ijert.org/research/building-personalised-recommendation-system-with-big-data-and-hadoop-mapreduce-IJERTV3IS042291.pdf Recommender systems are found in many e-commerce applications today. Recommender systems usually provide the user with a list of recommendations that they might prefer, or supply predictions on how much the user might prefer each item. Two common approaches for providing recommendations are collaborative filtering and content based filtering. By combining these two approaches, hybrid recommendation systems can be developed that considers both the ratings of the user and the item's feature to recommend the items to the user. The features of limited amount of data can be analyzed with the existing data analysis tools but when considering an e-book dataset of size in Terabytes, a big data analysis tool such as Hadoop is used. Hadoop is a software framework for distributed processing of large data sets. Hadoop uses MapReduce paradigm to perform distributed processing over clusters of computers to reduce the time involved in analyzing the item's feature (keywords of a book). The proposed system is reliable and fault tolerant when compared to the existing recommendation systems as it collects the ratings from the user to predict the interest and analyses the item to find the features. The system is also adaptive as it updates the rating list frequently and finds the updated interest of the user. Experimental results show that the proposed system is more accurate than the existing recommender systems.
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International Journal of Advance Research and Innovative Ideas in Education
Recommendation systems use one of the most intelligent technology for user when we surf on internet using recommendation system user can easily find valuable product from supplier. So here in these paper work on recommendation system for user an try to design. Artificially intelligent system work on time complexity and accuracy.
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Journal of Big Data
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:Big data created by Social network contains all sorts of information of the real world such as human relations, time, space and etc. Now it is possible to collect huge amount of data and store it. But the more data we get, the more difficult it is to get the meaningful and requisite information for each person. Thus, it is necessary for us to have a customized recommendation system with a high degree of accuracy which reflects personal characteristics using big data. In this paper,I organized key factors that affect the recommendation by analyzing the characteristics of big data provided by SNS. On the basis of these key factors and relations, I designed a big data model and embodied it for information recommendation systems using MongoDB.The recommendation algorithm can also be parallelized by using the map-reduce approach.
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International Journal for Research in Applied Science & Engineering Technology
In the digital world of online information huge amounts of Massive Open Online Courses (MOOCs) are available of different category and domain. Multiple online courses are available on different platform finding appropriate course from this massive available course is difficult for students. Recommender system plays vital role in finding appropriate courses to students. Managing massive amount of information and identifying individual users' choice and behavior has become tedious task nowadays, so the aim of recommender system is to suggest relevant course to student based on user behavior and similarity with another course. Several recommender system techniques are being implemented like content based, collaborative, Knowledge based. This paper aims to build a hybrid approach using collaborative filtering with content base filtering. This system recommendation is based on course description and ratings. Experiments were conducted on real datasets to get the overall performance of proposed system. I. INTRODUCTION With the ever-growing large volume of online information, recommender systems are an efficient strategy to beat such information overload. Recommender systems are the systems that are designed to recommend things to the user supported various factors. Companies like YouTube, Netflix, Amazon, etc. use recommender systems to help their users to recommend the correct product, video or movies for them. Advances in technology has changed the way of education. Massive Open Online Courses (MOOCs) are capable of providing several learners to access courses over the web. Recommender System (RS) is computerized system that suggest/recommend item to user. The number of MOOCs and the number of students registered in MOOCs are growing per annum. In 2018, more than 900 universities were offering MOOCs with 11,400 courses available, and around 101 million students had registered in them (Shah, 2018), providing learners with a good sort of choices. With such a high number of courses available, learners now face the matter of choosing courses without being overwhelmed. With the rise in e-commerce and online business, the number of users interested in online Web services has increased. Both MOOC providers and online businesses advertise their courses and services while learners look for courses that match their interests and needs. In these situations, recommender systems play a crucial role, and have attracted the attention of researchers. Recommender systems are algorithms and techniques that, based on their preferences, suggest matching and related courses or services to the learner, knowledge about which comes from learner profiles and systems-gathered histories. Recommender systems help MOOC providers grow and learners find more appropriate and customized services based on their personalities and interests. Recommender systems discover patterns in considerable datasets to find out preferences of different users and predict items that correlate to their needs. Recommender systems is divided into two broad categories: collaborative filtering recommender systems and content-based recommender systems. Collaborative filtering recommender systems perform recommendations on users who have had similar taste in the past will make similar choices in the future. Content based recommender systems consider the profile of users and items. The online course recommendation systems suggest to the students the best courses in which they are interested. This paper presents a recommendation methodology that recommends courses to students based on similarity between courses taken by the target student and other students. It aims to provide an effective course recommendation using multiple techniques. The students will be clustered into groups based on traditional data-mining (DM) techniques will to Collaborative filtering using knowledgebase.
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We are living in an age of Data and Information. Online social networks are contributing in enlargement of this data on high scale and Recommendation systems are helping industries to make this data useful for business purposes. It is helping to enhance the opportunities in online social data. Online social network generate large quantity of data from its users and recommendation system use this data for suggesting right piece of information to the user. But in the time of Big Data, processing large volumes of data for generating suggestions is a difficult job. We are aiming to implement recommendation algorithm using Apache Mahout, a machine learning tool, on Hadoop platform to provide a scalable system for processing large data sets efficiently.
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International Journal of Advance Research and Innovative Ideas in Education
More E-business Websites give items distinctive costs which made it hard for customers to discover the items and administrations they need. Keeping in mind the end goal to defeat this information over-burden, clever proposal motors are utilized to recommend items and to furnish customers with applicable information to help them choose which items to buy. Suggestion motors are exceedingly computational and subsequently perfect for the Hadoop Platform. Result shows approximately 75 to 80 percent is achieved for register users and 65 to 70 percent is achieved for unregister users. For register users accuracy is comparatively high for propose system then the existing ones E-commerce product to the register as well as the unregister user with increased accuracy by analyzing the interest of the users. This framework goes for building a Web Recommendation motor which utilizes thing or client based suggestion for prescribing Items. It will investigate the information and give recommendation.
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International Journal of Advanced Computer Science and Applications
