Personalized recommendation of stories for commenting in forum-based social media

Bach, Ngo Xuan; Hai, Nguyen Do; Phương, Từ Minh

doi:10.1016/j.ins.2016.03.006

Cited by 56 publications

(19 citation statements)

References 7 publications

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“…A hybrid story recommendation technique introduced by using co-commenting patterns of users from two forum-based social networks. The approach combines the collaborative features and content-based features from the learn-to-rank framework and uses 250 users profiles and comments for experimentation [4].…”

Section: ) Hybrid Recommendersmentioning

confidence: 99%

News Recommendation Systems - Accomplishments, Challenges & Future Directions

et al. 2020

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News publishers have decreased disseminating news through conventional newspapers and have migrated to the use of digital means like websites and purpose-built mobile applications. It is observed that news recommendation systems can automatically process lengthy articles and identify similar articles for readers considering predefined criteria. The objectives of the current work are to identify and classify the challenges in news recommendation domain, to identify state-of-the-art approaches and classify on the application domain, to identify datasets used for evaluation and their sources, the evaluation approaches used and to highlight the challenges explicitly addressed. The literature is thoroughly studied over the time span of 2001-2019 and shortlisted 81 related studies, broadly classified into six categories and discussed. The analysis showed that 60% of news recommendation system adopted a hybrid approach, 66% studies little talk about datasets, and addresses a few challenges from a long list of challenges in the news domain. This article is the first in the field to draw a comprehensive big picture of news recommendation and explore different dimensions covered in the studies. The last section presents the future research opportunities that lead to improving the recommendation of news articles in the news domain.

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Section: ) Hybrid Recommendersmentioning

confidence: 99%

News Recommendation Systems - Accomplishments, Challenges & Future Directions

et al. 2020

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“…Choi Il Young et al generated online footwear commercial video recommendations based on changes in user’s facial expression captured every moment based on CF [ 23 ]. Bach Ngo Xuan et al proposed an efficient collaborative filtering method that exploits co-commenting patterns of users to generate recommendations for news services, and this method exhibited high accuracy [ 24 ]. CF can effectively discover the potential interests of users and recommend new items.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Recommendation System for Marine Science Observation Data Based on Content and Literature Filtering

Song

Guo

Chang

et al. 2020

Sensors

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With the development of ocean exploration technology and the rapid growth in the amount of marine science observation data, people are faced with a great challenge to identify valuable data from the massive ocean observation data. A recommendation system is an effective method to improve retrieval capabilities to help users obtain valuable data. The two most popular recommendation algorithms are collaborative filtering algorithms and content-based filtering algorithms, which may not work well for marine science observation data given the complexity of data attributes and lack of user information. In this study, an approach was proposed based on data similarity and data correlation. Data similarity was calculated by analyzing the subject, source, spatial, and temporal attributes to obtain the recommendation list. Then, data correlation was calculated based on the literature on marine science data and ranking of the recommendation list to obtain the re-rank recommendation list. The approach was tested by simulated datasets collected from multiple marine data sharing websites, and the result suggested that the proposed method exhibits better effectiveness.

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“…• Tmall. 3 This dataset was made available for an IJCAI'15 competition. The data are interaction logs collected from tmall.com -an e-commerce website in China -over a period of six months.…”

Section: A Datasetsmentioning

confidence: 99%

“…Recommender systems (RS) have become key components in many online services, e.g. e-commerce, social media, news service, or online music [1], [3], [26], [38]. Such a system helps users find items of interest by proactively recommending items from large collections of available products.…”

Section: Introductionmentioning

confidence: 99%