A micro-video recommendation system based on big data

Shang, Songtao; Shi, Minjia; Shang, Wenqian; Hong, Zhiguo

doi:10.1109/icis.2016.7550932

Cited by 17 publications

(17 citation statements)

References 11 publications

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“…Video is a new form of information media. With the development of the Internet, 3G (the 3 rd Generation mobile communication technology), [4] and 4G (the 4 th Generation mobile communication technology) network, the bandwidth and speed of network become faster and faster. These technologies provide Conditions for dissemination of information media.…”

Section: Introductionmentioning

confidence: 99%

“…Video is a short time video, which lasts for 30 seconds to 300 seconds. The short time micro-videos are popular with young people, because the teenagers prefer to watch the micro-video [1] [4][6] on their comfortable time through mobile devices. For micro-video producers, the problem is they do not know how many people like their products, and do not know how many times their video have been watched.…”

Section: Introductionmentioning

confidence: 99%

“…For micro-video producers, the problem is they do not know how many people like their products, and do not know how many times their video have been watched. Therefore, this paper proposes a video recommendation system (MRS) [4]. One of the purposes is an overview of videos for the producer.…”

Section: Introductionmentioning

confidence: 99%

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A Survey on Micro Video Recommendation System for Content Writer

Girish¹

2019

IJRASET

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With the development of the Internet and social networking service, the micro-video is becoming more popular, especially for youngsters. However, for many users, they spend a lot of time to get their favorite micro-videos from amounts videos on the Internet; for the micro-video producers, they do not know what kinds of viewers like their products. Recommendation systems play an important role in helping Micro Video system. Micro-videos afford many potential opportunities ranging from network content caching to online advertising. Micro-videos are created by individuals and contain some unique characteristics, including egocentric and self-facing views. Micro Video Recommendation System for Content writer.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Survey on Micro Video Recommendation System for Content Writer

Girish¹

2019

IJRASET

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“…The dataset of tweets is used to evaluate the result of eliminating stop words from the dataset. The proposed system in [21] uses the slope-one recommendation algorithm to recommend micro-videos. The result shows that the strength of used algorithm provides better visualization interface and Hadoop framework provides high-level performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Recommender System for Journal Articles using Opinion Mining and Semantics

Sardar¹,

Ferzund²,

Suryani³

et al. 2017

ijacsa

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Abstract-Till date, the dominant part of Recommender Systems (RS) work focusing on single domain, i.e. for films, books and shopping and so on. However, human inclinations may traverse over numerous areas. Thus, utilization practices on related things from various domains can be valuable for RS to make recommendations. Academic articles, such as research papers are the way to express ideas and thoughts for the research community. However, there have been a lot of journals available which recognize these technical writings. In addition, journal selection procedure should consider user experience about the journals in order to recommend users most relevant journal. In this work of journal recommendation system, the data about the user experience targeting various aspects of journals has been gathered which addresses user experience about any journal. In addition, data set of archive articles has been developed considering the user experience in this regard. Moreover, the user experience and gathered data of archives are analyzed using two different frameworks based on semantics in order to have better consolidated recommendations. Before submission, we offer services on behalf of the research community that exploit user reviews and relevant data to suggest suitable journal according to the needs of the author.

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“…And the deviation between any two items can be got by subtracting the average rating of thetwo items. The prediction process of slope one scheme consists of two sections: (1).Calculate the average deviation matrix ij {dev }. Given a training set, we use formula 4 to compute the value ijdev of ij {dev }, ijdev is the average deviation of item i with respect to item j In formula 4, user u rates both item i and item j .…”

Section: Slope One Scheme-mentioning

confidence: 99%

A Micro-Video Recommendation System Using Improved Slope One Algorithm Based on Big Data

2017

IJAERD

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A micro-video recommendation system based on big data

Cited by 17 publications

References 11 publications

A Survey on Micro Video Recommendation System for Content Writer

A Survey on Micro Video Recommendation System for Content Writer

Recommender System for Journal Articles using Opinion Mining and Semantics

A Micro-Video Recommendation System Using Improved Slope One Algorithm Based on Big Data

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