202511200951 Status: idea Tags: Datascience, Recommender systems
Content-Based Filtering
- Content-based methods describe users and items by their known metadata. Each item i is represented by a set of relevant tagsāe.g. movies of the IMDb platform can be tagged asāactionā, ācomedyā, etc. Each user u is represented by a user profile, which can created from known user informationāe.g. sex and ageāor from the userās past activity.
- To train a Machine Learning model with this approach we can use a k-NN model. For instance, if we know that user u bought an item i, we can recommend to u the available items with features most similar to it.
The advantage of this approach is that items metadata are known in advance, so we can also apply it to Cold-Start scenarios where a new item or user is added to the platform and we donāt have user-item interactions to train our model. The disadvantages are that we donāt use the full set of known user-item interactions (each user is treated independently), and that we need to know metadata information for each item and user.
References
- Dit is iets wat we leren voor Datascience. dit was informatie vanuit avans 2-2 datascience 2025-11-18. en daarbij horen deze slides