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

arXiv: https://arxiv.org/abs/2407.09747 · HTML: https://mlsanity.com/paper/2407.09747/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 13 Jul 2024.

## Builds on (most cited first, 6 of 6)

- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://mlsanity.com/paper/1701.04783/) (2017)
- [Contrastive Learning for Cold-Start Recommendation](https://mlsanity.com/paper/2107.05315/) (2021)
- [Generative AI for Synthetic Data Generation: Methods, Challenges and the Future](https://mlsanity.com/paper/2403.04190/) (2024)
- [Hybrid Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/1901.03888/) (2019)
- [Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes](https://mlsanity.com/paper/1408.2039/) (2014)

## Built on by (most cited first, 4 of 4)

- [FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG](https://mlsanity.com/paper/2408.05242/) (2024)
- [SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks](https://mlsanity.com/paper/2408.05243/) (2024)
- [Multi-modal clothing recommendation model based on large model and VAE enhancement](https://mlsanity.com/paper/2410.02219/) (2024)
- [UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure](https://mlsanity.com/paper/2607.14102/) (2026)

## Abstract (arXiv)

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.