Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming
Kshitij Goyal, Wannes Meert, Hendrik Blockeel, Elia Van Wolputte, Koen, Vanderstraeten, Wouter Pijpops, Kurt Jaspers

TL;DR
This paper introduces methods to automatically generate product concepts from positive examples using database queries, with applications demonstrated in music streaming services to improve catalog organization and recommendations.
Contribution
It presents novel approaches combining PU learning with decision trees and clustering to learn product concepts from positive examples, applicable across different product types.
Findings
Effective in a simulated music streaming setup
Successfully learns product concepts from positive examples
Demonstrates potential for improving catalog structuring
Abstract
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…
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Taxonomy
TopicsText and Document Classification Technologies · Music and Audio Processing · Web Data Mining and Analysis
Methodstravel james
