RozaAbolghasemi/Group_Recommendation_Syatem_GcPp_clustering
A Jupyter notebook for a project centered around 'Group Recommendation Systems (GRS)' utilizing the 'GcPp' clustering approach.
This project helps create recommendations for groups by understanding individual preferences. It takes in how people compare items (e.g., Car A vs. Car B) and groups them based on similar tastes, then suggests items that the whole group is likely to prefer. It's for researchers or practitioners building systems that recommend things to multiple people at once, like suggesting movies for a family or destinations for a travel group.
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Use this if you need to generate recommendations for a collective of users, leveraging their individual item-by-item comparisons rather than simple ratings.
Not ideal if you're looking for individual recommendations or if your data only consists of absolute ratings (e.g., 1-5 stars) rather than pairwise preferences.
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Mar 05, 2024
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