RedTweet: recommendation engine for reddit

Abstract

Twitter and Reddit are two of the most popular social media sites used today. In this paper, we study the use of machine learning and WordNet-based classifiers to generate an interest profile from a user's tweets and use this to recommend loosely related Reddit threads which the reader is most likely to be interested in. We introduce a genre classification algorithm using a similarity measure derived from WordNet lexical database for English to label genres for nouns in tweets. The proposed algorithm generates a user's interest profile from their tweets based on a referencing taxonomy of genres derived from the genre-tagged Brown Corpus augmented with a technology genre. The top K genres of a user's interest profile can be used for recommending subreddit articles in those genres. Experiments using real life test cases collected from Twitter have been done to compare the performance on genre classification by using the WordNet classifier and machine learning classifiers such as SVM, Random Forests, and an ensemble of Bayesian classifiers. Empirically, we have obtained similar results from the two different approaches with a sufficient number of tweets. It seems that machine learning algorithms as well as the WordNet ontology are viable tools for developing recommendation engine based on genre classification. One advantage of the WordNet approach is simplicity and no learning is required. However, the WordNet classifier tends to have poor precision on users with very few tweets.