A Recommender System for Youtube Based on its Network of Reviewers

Qin, Song; Menezes, Ronaldo; Silaghi, Marius

doi:10.1109/socialcom.2010.53

Cited by 20 publications

(11 citation statements)

References 10 publications

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“…This network is considered as the leader in online video sharing; every day people watch hundreds of millions of videos and upload another hundreds of thousands [35]. This purpose-driven [36] OSN allows users to upload and share videos through websites, e-mails, and mobile devices [35]. Friendships are based on users' interests in videos [36]; users can share their own videos and add comments on others' videos.…”

Section: Youtubementioning

confidence: 99%

Understanding social effects in online networks

Alhazmi

Gokhale

2015

2015 International Conference on Computing, Networking and Communications (ICNC)

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Understanding the motives behind people's interactions online can offer sound bases to predict how a social network may evolve and also support a host of applications. We hypothesize that three offline social factors, namely, stature, relationship strength, and egocentricity may also play an important role in driving users' interactions online. Therefore, we study the influence of these three social factors in online interactions by analyzing the transitivity in triads or three-way relationships among users. Analyzing transitivity through the lens of triad census for four popular social networks, namely, Facebook, Twitter, YouTube and Slashdot, we find that: (i) users' interactions are largely influenced by intermediary relations, which enhances the mediators' stature; (ii) the strength of offline relationships plays a salient role in transitivity of relations online; and (iii) egocentricity, embodied in over-active and popular users, has a significant effect on the dynamics of online interactions.

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Section: Youtubementioning

confidence: 99%

Understanding social effects in online networks

Alhazmi

Gokhale

2015

2015 International Conference on Computing, Networking and Communications (ICNC)

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“…Nowadays, the RS has evolved from a single, simple development to an efficient recommendation system that combines big data, cloud computeing and deep learning [1], [2]. The RS plays a very important role in many websites, such as Ali's product recommendation, video recommend in YouTube, Google search association, and so on [3]- [5]. When the RS is established, the user's feedback data is a vital factor influencing the user's recommendation.…”

Section: Introductionmentioning

confidence: 99%

A New Similarity Computing Model of Collaborative Filtering

2020

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Collaborative filtering has become one of the most widely used methods for a variety of commercial recommendations. The key to collaborative filtering is use similarity calculation formula to find similar neighbors or projects. However, most similarity calculation methods only use the user common score and provide bad recommendations. This paper proposes a new similarity measure method, which effectively utilizes the user context information. The new method uses a singularity factor to adjust nonlinear equation and takes into account the user scoring habits. It can improve the accuracy of the prediction. The new method has been tested on the dataset and compared with other algorithms. The results show that the proposed method can improve the recommendation quality. INDEX TERMS Recommender system, collaborative filtering, context information.

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“…Since the beginning of the 1990's, many algorithms have been developed to deal with this problem, these make use of the behaviour passed on by the users (clicks, purchases, ratings) in order to produce recommendations [1]. The RS helps individuals to find products and/or services that correspond to their preferences and give support, so that individuals can make decisions in a variety of contexts, such as which products to buy [2], which film to watch [3], which music to listen to [4], which painting to go and see [5]. In this work, the author is interested in the Recommendation System for products that are associated with images (IRS).…”

Section: Introductionmentioning

confidence: 99%

Improving Collaborative Filtering-Based Image Recommendation through Use of Eye Gaze Tracking

Melo

2018

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Due to the overwhelming variety of products and services currently available on electronic commerce sites, the consumer finds it difficult to encounter products of preference. It is common that product preference be influenced by the visual appearance of the image associated with the product. In this context, Recommendation Systems for products that are associated with Images (IRS) become vitally important in aiding consumers to find those products considered as pleasing or useful. In general, these IRS use the Collaborative Filtering technique that is based on the behaviour passed on by users. One of the principal challenges found with this technique is the need for the user to supply information concerning their preference. Therefore, methods for obtaining implicit information are desirable. In this work, the author proposes an investigation to discover to which extent information concerning user visual attention can aid in producing a more precise IRS. This work proposes therefore a new approach, which combines the preferences passed on from the user, by means of ratings and visual attention data. The experimental results show that our approach exceeds that of the state of the art.

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A Recommender System for Youtube Based on its Network of Reviewers

Cited by 20 publications

References 10 publications

Understanding social effects in online networks

Understanding social effects in online networks

A New Similarity Computing Model of Collaborative Filtering

Improving Collaborative Filtering-Based Image Recommendation through Use of Eye Gaze Tracking

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