A practical guide to building maps for e-commerce scenarios
For recommender system optimization, it is necessary to focus on modeling user behavior patterns and product associations:
- Data preparation phase::
1. Prepare user browsing/purchase logs formatted asuser_id,item_id,action_type,timestamp
2. Add commodity attributes toGAG_data/items_metadata.csv
3. Construct initial prompts such as "simulate that users often jump between products in the same category". - Atlas generation::
- executepython main.py --task ecommerce --build --config "dense"
- add--relation_weightParameter Enhanced Purchase Side Weighting
- utilization--dynamics 7dSimulation of weekly dimensional changes
Application Methods:
1. Path recommendation: running a randomized wandering algorithm based on the generated map
2. Community discovery: identification of potential user groups using the Louvain algorithm
3. Cold-start solution: connecting new commodities to existing nodes with similar characteristics
Assessment of indicators:
- Calculation of graph density reflects the adequacy of user-commodity connectivity
- Detecting clustering coefficients to assess the reasonableness of recommendations
- pass (a bill or inspection etc)evaluate/movie/main.pyAdaptation calculation HitRate@K
This answer comes from the articleGAG: Generating a Social Relationship Graph Using a Large Model to Simulate Human BehaviorThe
