AsymKNN

Introduction

[paper]

Title: Efficient Top-N Recommendation for Very Large Scale Binary Rated Datasets

Authors: Fabio Aiolli

Abstract: We present a simple and scalable algorithm for top-N recommendation able to deal with very large datasets and (binary rated) implicit feedback. We focus on memory-based collaborative filtering algorithms similar to the well known neighboor based technique for explicit feedback. The major difference, that makes the algorithm particularly scalable, is that it uses positive feedback only and no explicit computation of the complete (user-by-user or itemby-item) similarity matrix needs to be performed. The study of the proposed algorithm has been conducted on data from the Million Songs Dataset (MSD) challenge whose task was to suggest a set of songs (out of more than 380k available songs) to more than 100k users given half of the user listening history and complete listening history of other 1 million people. In particular, we investigate on the entire recommendation pipeline, starting from the definition of suitable similarity and scoring functions and suggestions on how to aggregate multiple ranking strategies to define the overall recommendation. The technique we are proposing extends and improves the one that already won the MSD challenge last year.

In this article, we introduce a versatile class of recommendation algorithms that calculate either user-to-user or item-to-item similarities as the foundation for generating recommendations. This approach enables the flexibility to switch between UserKNN and ItemKNN models depending on the desired application.

A distinguishing feature of this class of algorithms, exemplified by AsymKNN, is its use of asymmetric cosine similarity, which generalizes the traditional cosine similarity. Specifically, when the asymmetry parameter alpha = 0.5, the method reduces to the standard cosine similarity, while other values of alpha allow for tailored emphasis on specific aspects of the interaction data. Furthermore, setting the parameter beta = 1.0 ensures a traditional UserKNN or ItemKNN, as the final scores are only divided by a fixed positive constant, preserving the same order of recommendations.

Running with RecBole

Model Hyper-Parameters:

  • k (int) : The neighborhood size. Defaults to 100.

  • alpha (float) : Weight parameter for asymmetric cosine similarity. Defaults to 0.5.

  • beta (float) : Parameter for controlling the balance between factors in the final score normalization. Defaults to 1.0.

  • q (int) : The ‘locality of scoring function’ parameter. Defaults to 1.

Additional Parameters:

  • knn_method (str) : Calculate the similarity of users if method is ‘user’, otherwise, calculate the similarity of items.. Defaults to item.

A Running Example:

Write the following code to a python file, such as run.py

from recbole.quick_start import run_recbole

run_recbole(model='AsymKNN', dataset='ml-100k')

And then:

python run.py

Tuning Hyper Parameters

If you want to use HyperTuning to tune hyper parameters of this model, you can copy the following settings and name it as hyper.test.

k choice [10,50,100,200,250,300,400,500,1000,1500,2000,2500]
alpha choice [0.0,0.2,0.5,0.8,1.0]
beta choice [0.0,0.2,0.5,0.8,1.0]
q choice [1,2,3,4,5,6]

Note that we just provide these hyper parameter ranges for reference only, and we can not guarantee that they are the optimal range of this model.

Then, with the source code of RecBole (you can download it from GitHub), you can run the run_hyper.py to tuning:

python run_hyper.py --model=[model_name] --dataset=[dataset_name] --config_files=[config_files_path] --params_file=hyper.test

For more details about Parameter Tuning, refer to Parameter Tuning.

If you want to change parameters, dataset or evaluation settings, take a look at