# NOTE: this takes about 30 seconds for 750,000 interactions on my 2.3 GHz i5 8GB RAM MacBook fit( interactions_train, epochs = 20, verbose = True) rankfm import RankFM model = RankFM( factors = 20, loss = 'warp', max_samples = 20, alpha = 0.01, sigma = 0.1, learning_rate = 0.1, learning_schedule = 'invscaling') Now let's import the library, initialize our model, and fit on the training data:įrom rankfm. Implicit feedback is far more common in real-world recommendation contexts and doesn't suffer from the missing-not-at-random problem of pure explicit feedback approaches. watches, page views, purchases, clicks) as opposed to explicit feedback data (e.g. Notice that there is no rating column - this library is for implicit feedback data (e.g. It has just two columns: a user_id and an item_id (you can name these fields whatever you want or use a numpy array instead). Let's first look at the required shape of the interaction data: user_id The data we'll be using here may already be somewhat familiar: you know it, you love it, it's the MovieLens 1M! Let's work through a simple example of fitting a model, generating recommendations, evaluating performance, and assessing some item-item similarities. It's highly recommended that you use an Anaconda base environment to ensure that all core numpy C extensions and linear algebra libraries have been installed and configured correctly. Check to see whether you already have it installed: To install RankFM's C extensions you will need the GNU Compiler Collection (GCC). see the Medium Article for contextual motivation and a detailed mathematical description of the algorithm. see the Online Documentation for more comprehensive documentation on the main model class and separate evaluation module.see the /examples folder for more in-depth jupyter notebook walkthroughs with several popular open-source data sets.see the Quickstart section below to get started with the basic functionality.A number of popular recommendation/ranking evaluation metric functions have been included in the separate evaluation module to streamline model tuning and validation. In addition to the familiar fit(), predict(), recommend() methods, RankFM includes additional utilities similiar_users() and similar_items() to find the most similar users/items to a given user/item based on latent factor space embeddings. RankFM internally maps all user/item identifiers to zero-based integer indexes, but always converts its output back to the original user/item identifiers from your data, which can be arbitrary (non-zero-based, non-consecutive) integers or even strings. Designed for ease-of-use, RankFM accepts both pd.DataFrame and np.ndarray inputs - you do not have to convert your data to scipy.sparse matrices or re-map user/item identifiers prior to use. The core (training, prediction, recommendation) methods are written in Cython, making it possible to scale to millions of user/item interactions. It can (optionally) incorporate sample weights and user/item auxiliary features to augment the main interaction data. It uses Bayesian Personalized Ranking (BPR) and a variant of Weighted Approximate-Rank Pairwise (WARP) loss to learn model weights via Stochastic Gradient Descent (SGD). RankFM is a python implementation of the general Factorization Machines model class adapted for collaborative filtering recommendation/ranking problems with implicit feedback user/item interaction data.
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