7,973 research outputs found
Who are Like-minded: Mining User Interest Similarity in Online Social Networks
In this paper, we mine and learn to predict how similar a pair of users'
interests towards videos are, based on demographic (age, gender and location)
and social (friendship, interaction and group membership) information of these
users. We use the video access patterns of active users as ground truth (a form
of benchmark). We adopt tag-based user profiling to establish this ground
truth, and justify why it is used instead of video-based methods, or many
latent topic models such as LDA and Collaborative Filtering approaches. We then
show the effectiveness of the different demographic and social features, and
their combinations and derivatives, in predicting user interest similarity,
based on different machine-learning methods for combining multiple features. We
propose a hybrid tree-encoded linear model for combining the features, and show
that it out-performs other linear and treebased models. Our methods can be used
to predict user interest similarity when the ground-truth is not available,
e.g. for new users, or inactive users whose interests may have changed from old
access data, and is useful for video recommendation. Our study is based on a
rich dataset from Tencent, a popular service provider of social networks, video
services, and various other services in China
Machine Learning enabled models for YouTube Ranking Mechanism and Views Prediction
With the continuous increase of internet usage in todays time, everyone is
influenced by this source of the power of technology. Due to this, the rise of
applications and games Is unstoppable. A major percentage of our population
uses these applications for multiple purposes. These range from education,
communication, news, entertainment, and many more. Out of this, the application
that is making sure that the world stays in touch with each other and with
current affairs is social media. Social media applications have seen a boom in
the last 10 years with the introduction of smartphones and the internet being
available at affordable prices. Applications like Twitch and Youtube are some
of the best platforms for producing content and expressing their talent as
well. It is the goal of every content creator to post the best and most
reliable content so that they can gain recognition. It is important to know the
methods of achieving popularity easily, which is what this paper proposes to
bring to the spotlight. There should be certain parameters based on which the
reach of content could be multiplied by a good factor. The proposed research
work aims to identify and estimate the reach, popularity, and views of a
YouTube video by using certain features using machine learning and AI
techniques. A ranking system would also be used keeping the trending videos in
consideration. This would eventually help the content creator know how
authentic their content is and healthy competition to make better content
before uploading the video on the platform will be ensured.Comment: The Paper has been ACCEPTED at the "2nd International Conference on
Computing and Communication Networks(ICCCN-2022)". This paper will be
published by AIP publishing and DOI will be issued later o
Popularity prediction of instagram posts
Predicting the popularity of posts on social networks has taken on significant importance in recent years, and several social media management tools now offer solutions to improve and optimize the quality of published content and to enhance the attractiveness of companies and organizations. Scientific research has recently moved in this direction, with the aim of exploiting advanced techniques such as machine learning, deep learning, natural language processing, etc., to support such tools. In light of the above, in this work we aim to address the challenge of predicting the popularity of a future post on Instagram, by defining the problem as a classification task and by proposing an original approach based on Gradient Boosting and feature engineering, which led us to promising experimental results. The proposed approach exploits big data technologies for scalability and efficiency, and it is general enough to be applied to other social media as well
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