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Learning Users Visual Preferences: Building a Recommendation System for Instagram

Description
Social media users are inundated with information. Especially on Instagram--a social media service based on sharing photos--where for many users, missing important posts is a common issue. By creating a recommendation system which learns each user's preference and gives them a curated list of posts, the information overload issue can…

Social media users are inundated with information. Especially on Instagram--a social media service based on sharing photos--where for many users, missing important posts is a common issue. By creating a recommendation system which learns each user's preference and gives them a curated list of posts, the information overload issue can be mediated in order to enhance the user experience for Instagram users. This paper explores methods for creating such a recommendation system. The proposed method employs a learning model called ``Factorization Machines" which combines the advantages of linear models and latent factor models. In this work I derived features from Instagram post data, including the image, social data about the post, and information about the user who created the post. I also collect user-post interaction data describing which users ``liked" which posts, and this was used in models leveraging latent factors. The proposed model successfully improves the rate of interesting content seen by the user by anywhere from 2 to 12 times.
ContributorsFakhri, Kian (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
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Tenga: A Case Study on Building Web Applications

Description
Tenga is an e-commerce demo web application for students studying Distributed Software Development and Software Integration and Engineering at Arizona State University (ASU). The application, written in C#, aims to empower students to understand how complex systems are build. Complementing the two courses taught at ASU, it seeks to demonstrate…

Tenga is an e-commerce demo web application for students studying Distributed Software Development and Software Integration and Engineering at Arizona State University (ASU). The application, written in C#, aims to empower students to understand how complex systems are build. Complementing the two courses taught at ASU, it seeks to demonstrate how the concepts taught in the two classes can be applied to the real world. In addition to the practical software development process, Tenga also bring in the topics that students are inexperienced with such as recommendation systems and ranking algorithms. Tenga is going to be used in classrooms to help students to learn fundamental issues in Web software development and software integration and to understand tools and skill sets required to built a web application.
ContributorsKawanzaruwa, Allen Tom (Author) / Chen, Yinong (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05