SocialRec: User Activity Based Post Weighted Dynamic Personalized Post Recommendation System in Social Media

TL;DR

This paper presents SocialRec, a personalized social media post recommendation system that leverages user activity history, engagement, and profiles, employing a hybrid approach to address cold-start issues and improve ranking accuracy.

Contribution

The paper introduces a novel hybrid recommendation model combining user activity analysis and collaborative filtering to enhance personalization and cold-start performance in social media.

Findings

Achieved highest NDCG of 0.6 and Hit Rate of 0.80 with NeuMF.

Effectively addresses cold-start problem for new users and items.

Improves ranking quality by integrating user history, engagement, and profile data.

Abstract

User activities can influence their subsequent interactions with a post, generating interest in the user. Typically, users interact with posts from friends by commenting and using reaction emojis, reflecting their level of interest on social media such as Facebook, Twitter, and Reddit. Our objective is to analyze user history over time, including their posts and engagement on various topics. Additionally, we take into account the user's profile, seeking connections between their activities and social media platforms. By integrating user history, engagement, and persona, we aim to assess recommendation scores based on relevant item sharing by Hit Rate (HR) and the quality of the ranking system by Normalized Discounted Cumulative Gain (NDCG), where we achieve the highest for NeuMF 0.80 and 0.6 respectively. Our hybrid approach solves the cold-start problem when there is a new user, for new items cold-start problem will never occur, as we consider the post category values. To improve the performance of the model during cold-start we introduce collaborative filtering by looking for similar users and ranking the users based on the highest similarity scores.