Content-Based Recommenders¶
The Content-Based Recommenders module of WarpRec focuses on generating recommendations by analyzing item attributes and comparing them with user preferences. These models rely on side information (e.g., tags, descriptions, metadata) to build user profiles and match them with similar items. They are particularly useful in cold-start scenarios or when collaborative signals are sparse.
In the following sections, you will find the list of available content-based models within WarpRec, together with their respective parameters.
API Reference
For class signatures, parameters, and source code, see the Content-Based API Reference.
Summary of Available Content Models¶
| Category | Model | Description |
|---|---|---|
| Vector Space | VSM | Classical content-based model using TF-IDF and cosine similarity. |
Vector Space¶
Vector Space models represent items and users as vectors in a multi-dimensional space derived from item features. Similarity is calculated based on the geometric distance or angle between these vectors.
VSM¶
VSM (Vector Space Model): A classical content-based recommender that represents items and users in a shared vector space. Items are encoded using TF-IDF or binary features, while user profiles are typically aggregated from consumed items. Recommendations are generated by computing the similarity (e.g., cosine) between user and item vectors. This model requires side information to function properly.
For further details, please refer to the paper.