NDLI: Taxonomy discovery for personalized recommendation

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Taxonomy discovery for personalized recommendation

Content Provider	ACM Digital Library
Author	Zhang, Yuchen Josifovski, Vanja Ahmed, Amr Smola, Alexander
Abstract	Personalized recommender systems based on latent factor models are widely used to increase sales in e-commerce. Such systems use the past behavior of users to recommend new items that are likely to be of interest to them. However, latent factor model suffer from sparse user-item interaction in online shopping data: for a large portion of items that do not have sufficient purchase records, their latent factors cannot be estimated accurately. In this paper, we propose a novel approach that automatically discovers the taxonomies from online shopping data and jointly learns a taxonomy-based recommendation system. Out model is non-parametric and can learn the taxonomy structure automatically from the data. Since the taxonomy allows purchase data to be shared between items, it effectively improves the accuracy of recommending tail items by sharing strength with the more frequent items. Experiments on a large-scale online shopping dataset confirm that our proposed model improves significantly over state-of-the-art latent factor models. Moreover, our model generates high-quality and human readable taxonomies. Finally, using the algorithm-generated taxonomy, our model even outperforms latent factor models based on the human-induced taxonomy, thus alleviating the need for costly manual taxonomy generation.
Starting Page	243
Ending Page	252
Page Count	10
File Format	PDF
ISBN	9781450323512
DOI	10.1145/2556195.2556236
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-02-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Latent factor model Taxonomy Recommender system
Content Type	Text
Resource Type	Article

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