  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.
