SVP-CF: Selection via Proxy for Collaborative Filtering Data

By Noveen Sachdeva, Carole-Jean Wu, Julian McAuley. 2021

21 citations recorded by the source corpus.

We study the practical consequences of dataset sampling strategies on the 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 -- masking performance deficiencies in algorithms or altering the relative performance of algorithms, as compared to models trained on the complete dataset. Following this observation, this paper makes the following 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.); and (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-tail interaction data. Detailed experiments show that SVP-CF is more accurate than commonly used sampling schemes in retaining the relative ranking of different recommendation algorithms.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research, Recommender Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Carole-Jean Wu
2. Bilge Acun
3. Michael G. Rabbat
4. Kim M. Hazelwood
5. Hsien-Hsin S. Lee
6. Udit Gupta
7. Newsha Ardalani
8. Maximilian Balandat

In Information Systems

1. Carole-Jean Wu
2. Bilge Acun
3. Michael G. Rabbat
4. Kim M. Hazelwood
5. Myle Ott

In Management Science and Operations Research

1. Michael G. Rabbat
2. Maximilian Balandat
3. Myle Ott
4. Konstantinos I. Tsianos

In Recommender Systems

1. Carole-Jean Wu
2. Bilge Acun

Citations in the corpus

• Sustainable AI: Environmental Implications, Challenges and Opportunities (2021, background): “For instance, Sachdeva et al. demonstrated that intelligent data sampling with merely 10% of data sub-samples can effectively preserve the relative ranking performance of different recommendation algorithms [42].”