# Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming

arXiv: https://arxiv.org/abs/2210.01515 · HTML: https://mlsanity.com/paper/2210.01515/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Graph Clustering with Graph Neural Networks](https://mlsanity.com/paper/2006.16904/) (2020)
- [Deep Learning Techniques for Music Generation -- A Survey](https://mlsanity.com/paper/1709.01620/) (2017)
- [Music Generation by Deep Learning - Challenges and Directions](https://mlsanity.com/paper/1712.04371/) (2017)
- [DopeLearning: A Computational Approach to Rap Lyrics Generation](https://mlsanity.com/paper/1505.04771/) (2015)
- [Music Genre Classification using Machine Learning Techniques](https://mlsanity.com/paper/1804.01149/) (2018)
- [SaDe: Learning Models that Provably Satisfy Domain Constraints](https://mlsanity.com/paper/2112.00552/) (2021)

## Built on by (most cited first, 1 of 1)

- [Pruning-Based Extraction of Descriptions from Probabilistic Circuits](https://mlsanity.com/paper/2311.13379/) (2023)

## Abstract (arXiv)

Internet based businesses and products (e.g. e-commerce, music streaming) are becoming more and more sophisticated every day with a lot of focus on improving customer satisfaction. A core way they achieve this is by providing customers with an easy access to their products by structuring them in catalogues using navigation bars and providing recommendations. We refer to these catalogues as product concepts, e.g. product categories on e-commerce websites, public playlists on music streaming platforms. These product concepts typically contain products that are linked with each other through some common features (e.g. a playlist of songs by the same artist). How they are defined in the backend of the system can be different for different products. In this work, we represent product concepts using database queries and tackle two learning problems. First, given sets of products that all belong to the same unknown product concept, we learn a database query that is a representation of this product concept. Second, we learn product concepts and their corresponding queries when the given sets of products are associated with multiple product concepts. To achieve these goals, we propose two approaches that combine the concepts of PU learning with Decision Trees and Clustering. Our experiments demonstrate, via a simulated setup for a music streaming service, that our approach is effective in solving these problems.