Publication Date

Spring 2023

Degree Type

Master's Project

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

William Andreopoulos

Second Advisor

Katerina Potika

Third Advisor

Genya Ishigaki

Keywords

Annotation, image captioning, personalization, recommenda- tion system, twitter recommendation

Abstract

In the era of information explosion, the vast amount of data on social media platforms can overwhelm users. Not only does this information explosion contain irrelevant content, but also intentionally fabricated articles and images. As a result, personalized recommendation systems have become increasingly important to help users navigate and make sense of this data. We propose a novel technique to use users’ image preferences to recommend tweets. We extract vital information by analyzing images liked by users and use it to recommend tweets from Twitter. As many images online have no descriptive metadata associated with them, in this framework, we also provide an opportunity for the users to annotate the images they liked with the hashtags of the recommended tweets.

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