Using Deep Learning to Recommend Discussion Threads to Users in an Online Forum
Nicholas Buhagiar, Bahram Zahir, Abdolreza Abhari · 2018
Using comments made in discussion threads on the social media aggregation website Reddit, the topics of conversation were identified using the probabilistic topic model Latent Dirichlet Allocation (LDA). Employing these topics as features for a neural network, several different neural network frameworks were trained on the data to serve as models to identify which threads a given user would be interested in contributing to based on their previously shown interests. This was done on a sample set of 30 users using 10 different initial random weights for each framework. The ideal model for each user was identified as being the one that scored the highest F2-Score, the harmonic mean of precision and recall with a bias towards recall, on a development set. Testing these ideal models on a test set, they achieved an average F2-Score of 0.825, as well as an average precision of 0.542 and an average recall of 0.956 for a sample set of users.