Hello Everyone.
I am currently working on a project where I am utilizing Neo4j as the database for a platform that allows users to interact with various listings, such as jobs and events. To enhance user engagement and provide valuable insights, I am looking to implement algorithms for weighting and time decay to identify trending listings based on user interactions.
Specifically, I plan to store user interactions as relationships between nodes in the Neo4j graph database, with properties such as timestamp and interaction type. I aim to calculate weighted scores for listings based on these interactions, considering factors such as views, likes, shares, and direct messages. Additionally, I intend to apply time decay to give more weight to recent interactions.
Before proceeding further with the implementation, I wanted to seek your guidance and expertise on the following:
- Are there any best practices or recommended approaches for implementing weighting and time decay algorithms in Neo4j?
- Are there any built-in features or functionalities within Neo4j that could streamline the implementation of such algorithms?
- Are there any specific Neo4j Graph Data Science (GDS) algorithms or procedures that could be beneficial for analyzing user interactions and identifying trends within the graph data?
- Regarding handling multiple interactions (e.g., if users view the same listing multiple times), would it be advisable to store each interaction separately or update the existing interaction? Are there any considerations or implications I should be aware of for each approach?
I would greatly appreciate any insights, recommendations, or resources you could provide to assist me in successfully implementing these algorithms within Neo4j.
Thanks.