Big Data Analysis: Recommendation System with Hadoop Framework

Verma, J. P.; Patel, Bankim; Patel, Atul

doi:10.1109/cict.2015.86

Cited by 88 publications

(41 citation statements)

References 11 publications

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“…There are many works using the Hadoop ecosystem for supporting item-based recommender systems. It is the case, for instance, of Bhatia and Prasad [13], Verma et al [14] . This section will first describe the big data architecture used for real-time recommendation and how the recommender uses this architecture.…”

Section: State Of the Artmentioning

confidence: 99%

An efficient and scalable recommender system for the smart web

Baldominos

Sáez

Albacete

et al. 2015

2015 11th International Conference on Innovations in Information Technology (IIT)

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This is a postprint version of the following published document: Abstract: This work describes the development of a web recommender system implementing both collaborative filtering and content-based filtering. Moreover, it supports two differ-ent working modes, either sponsored or related, depending on whether websites are to be recommended based on a list of ongoing ad campaigns or in the user preferences. Novel recommendation algorithms are proposed and imple-mented, which fully rely on set operations such as union and intersection in order to compute the set of recommendations to be provided to end users. The recommender system is deployed over a real-time big data architecture designed to work with Apache Hadoop ecosystem, thus supporting horizontal scalability, and is able to provide recommendations as a service by means of a RESTful API. The performance of the recommender is measured, resulting in the system being able to provide dozens of recommendations in few milliseconds in a single-node cluster setup.

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Section: State Of the Artmentioning

confidence: 99%

An efficient and scalable recommender system for the smart web

Baldominos

Sáez

Albacete

et al. 2015

2015 11th International Conference on Innovations in Information Technology (IIT)

View full text Add to dashboard Cite

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“…But due to localization of data that knowledge may not effective or some time leads a wrong decision. To overcome this localization issue the concept of Big Data arises [5,6,8]. Big data covers large volume of data containing of variety of data types and generating in a large velocity.…”

Section: Big Data and Big Data Analyticsmentioning

confidence: 99%

“…Educational data domain covers all the types of data generated by the different types of functionaries require to run the institution. Big data analytics for educational data domain help to improve the satisfaction level of different stack holders like student, teacher, parents, governing bodies and society [6,8].…”

Section: Big Data and Big Data Analyticsmentioning

confidence: 99%

“…Hadoop enables processing of Big Data in a distributed computing environment built from commodity hardware. Hadoop is part of the Apache project funded by the Apache Software Foundation [6]. For experimental analysis single node Hadoop file system is implemented with Ubuntu operating system.…”

Section: Haddop Frameworkmentioning

confidence: 99%

“…Hadoop file system [6] was implemented for storage and management of this large text dataset. MapReduce programming with Hadoop framework was used for counting the words and word frequencies.…”

Section: Characteristics Of Datasetmentioning

confidence: 99%

See 2 more Smart Citations

An Extractive Text Summarization approach for Analyzing Educational Institution’s Review and Feedback Data

Verma¹,

Patel²

2016

IJCA

Self Cite

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Big Data analytics helps the enterprises and institutions to understand and identify the usability of large amount of data generated by their routine operations. All most third forth part of these types of data is semi-structured text data. Many types of actionable insights can be found from these type of semistructured text data that can help strategic management for making right decision. In this paper we are proposing a recommendation system model for understanding and finding actionable insight from the large amount of text data generated for an educational institution. Here we are discussing different type of data generation sources of this types of data as well as data cleaning processes required. The wordcloud for an educational institution is published that help strategic management for understanding the sentiment of different stack holders mainly students. Herewith we are identifying different types of findings from these sets of words that helps for betterment in the functionaries of an Educational Institution.

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SemRec – An efficient ensemble recommender with sentiment based clustering for social media text corpus

Renjith

Sreekumar

Jathavedan

2021

Concurrency and Computation

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The frequent user interactions happening in the form of textual contents like reviews, ratings, tags, blogs, testimonials, and so forth transformed the social media platform into a contextualized and personalized data warehouse focusing its users' unique likes and dislikes. The huge volume of social media content makes it difficult for the end users to consume relevant information by themselves. The need of a tool to deal with such scenario leads to the development of recommendation systems. This work proposes an ensemble multi-stage recommender system with sentiment based clustering to deal with social media text corpus where each stage performing unique functionalities of information retrieval, natural language processing, user segmentation, prediction, and recommendation generation. The proposed system leverages a hybrid approach of content-based, collaborative, and demographic filtering techniques to predict and recommend contents, products, or services according to user interests. The experimental results gathered using standard datasets are promising and found more efficient than the traditional approaches. K E Y W O R D Scollaborative filtering, content-based filtering, machine learning, natural language processing, recommender system, social media INTRODUCTIONData scientists uncover an emerging trend in the availability of information for solving various business problems using data analytics. One of the key reasons for the exponential growth of information availability and accessibility is the expansion of internet followed by the emergence of digital platforms like e-commerce as popular mode of transactions. Another important reason is the evolution of social media as a channel for various types of user interactions. The retrieval of relevant information and its presentation to the target audience in an appropriate way has become highly important as information availability is no longer a challenge. Recommender systems are evolved as a tool to bridge the gaps among information extraction, information filtering, information processing, and presentation to end user.Recommender system is a type of information filtering technique used for predicting the potential behavior of a user by processing variety of information like past behavior traits, societal activities, contextual information, personal preferences, and so forth. In end user perspective, recommender systems help them to retrieve the right information so that considerable time can be saved in searching for relevant and accurate information. 1 Recommender systems and its applications are now becoming very popular across almost all business/industrial domains like e-commerce, 2 travel and tourism, 3-7 customer retention, 8-11 fraud detection, 12,13 advertisement, 14,15 and so forth.Currently, the vast majority of social media data is residing in the form of textual content like reviews, blog posts, forum updates, testimonials, and so forth. For example, applications like Amazon.com, Internet Movie Database (IMDb), Facebook, Twitter, and so forth, pr...

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Big Data Analysis: Recommendation System with Hadoop Framework

Cited by 88 publications

References 11 publications

An efficient and scalable recommender system for the smart web

An efficient and scalable recommender system for the smart web

An Extractive Text Summarization approach for Analyzing Educational Institution’s Review and Feedback Data

SemRec – An efficient ensemble recommender with sentiment based clustering for social media text corpus

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