On Sampling Collaborative Filtering Datasets

TL;DR

This paper investigates how different dataset sampling strategies affect recommendation algorithm performance and introduces methods to select sampling schemes that preserve model effectiveness, enabling more efficient data usage.

Contribution

It characterizes the impact of sampling on recommendation performance, proposes SVP-CF for better sampling, and develops Data-Genie to recommend optimal sampling schemes for datasets.

Findings

Data sampling significantly influences algorithm performance.

Data-Genie can discard up to 5x more data without performance loss.

SVP-CF effectively preserves model rankings after sampling.

Abstract

We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms. Recommender systems are generally trained and evaluated on samples of larger datasets. Samples are often taken in a naive or ad-hoc fashion: e.g. by sampling a dataset randomly or by selecting users or items with many interactions. As we demonstrate, commonly-used data sampling schemes can have significant consequences on algorithm performance. Following this observation, this paper makes three main contributions: (1) characterizing the effect of sampling on algorithm performance, in terms of algorithm and dataset characteristics (e.g. sparsity characteristics, sequential dynamics, etc.); (2) designing SVP-CF, which is a data-specific sampling strategy, that aims to preserve the relative performance of models after sampling, and is especially suited to long-tailed interaction data; and (3) developing an oracle, Data-Genie, which can suggest the sampling scheme that is most likely to preserve model performance for a given dataset. The main benefit of Data-Genie is that it will allow recommender system practitioners to quickly prototype and compare various approaches, while remaining confident that algorithm performance will be preserved, once the algorithm is retrained and deployed on the complete data. Detailed experiments show that using Data-Genie, we can discard upto 5x more data than any sampling strategy with the same level of performance.