Recommender system architecture based on Mahout and a main memory database

Piao, Zhegao; Yoo, Seong Joon; Gu, Yeong Hyeon; No, Jaechun; Jiang, Zhiyan; Yin, Helin

doi:10.1007/s11227-017-2111-x

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Saranya and Sudha Sadasivam [28] proposed the rough set-based collaborative filtering technique for imputing the missing news categories for each user. Piao et al [29] suggested news recommendation architecture using Mahout and a main memory database. e news preference rate of users was calculated on the basis of time spent by users on reading specific news articles.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Fuzzy Rule-Based Personalized News Recommendation Using Social Media Mining

Manoharan

Senthilkumar

2020

Computational Intelligence and Neuroscience

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Recommendation of a relevant and suitable news article is an essential but a challenging task due to changes in the user interest categories over time. Moreover, the Internet technology provides abundant news articles from a huge amount of resources. Meanwhile, nowadays, many people are confronted with viral news articles through social media cost-free without considering the news sites. Therefore, mining of social media for addressing such viral news articles has become another key challenge. To overcome the above challenges, this paper proposes fuzzy logic approach for predicting users’ diversified interest and its categories by analysing their implicit user profile. Depending on users’ interest categories, the viral news articles and their categories were determined and analysed through mining social media feeds-Facebook and Twitter. Furthermore, fresh news articles are retrieved from news feeds incorporated with retrieved viral news articles provided as recommendation with respect to users’ diversified interest. The performance of the proposed approach for predicting overall users’ interest for all categories attained 84.238%, and recommendation accuracy from News feed, Facebook, and Twitter attained 100%, 90%, and 100% with respect to users’ interest categories.

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Section: Literature Reviewmentioning

confidence: 99%