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.

翻译：用户活动能够影响其后续与帖子的互动，从而激发用户兴趣。通常，用户通过评论和使用反应表情符号与好友的帖子互动，这在Facebook、Twitter和Reddit等社交媒体上反映了他们的兴趣程度。我们的目标是分析用户随时间推移的历史行为，包括其发布的帖子及在不同话题上的参与度。此外，我们综合考虑用户个人资料，探索其活动与社交媒体平台之间的关联。通过整合用户历史、参与度和个人画像，我们基于相关项目分享通过命中率（HR）评估推荐分数，并通过归一化折损累计增益（NDCG）衡量排序系统的质量，其中NeuMF模型分别取得了0.80和0.6的最高值。我们的混合方法解决了新用户冷启动问题，而新物品冷启动问题则因考虑帖子类别值而不会出现。为提升冷启动阶段的模型性能，我们引入协同过滤机制，通过寻找相似用户并依据最高相似度分数进行排序来实现优化。