Supercharging Recommender Systems using Taxonomies for Learning User Purchase Behavior

By Bhargav Kanagal, Amr Ahmed, Sandeep Pandey, Vanja Josifovski, Jeffrey Yuan, Lluís Garcia Pueyo. 2012

78 citations recorded by the source corpus.

Recommender systems based on latent factor models have been effectively used for understanding user interests and predicting future actions. Such models work by projecting the users and items into a smaller dimensional space, thereby clustering similar users and items together and subsequently compute similarity between unknown user-item pairs. When user-item interactions are sparse ( sparsity problem) or when new items continuously appear ( cold start problem), these models perform poorly. In this paper, we exploit the combination of taxonomies and latent factor models to mitigate these issues and improve recommendation accuracy. We observe that taxonomies provide structure similar to that of a latent factor model: namely, it imposes human-labeled categories (clusters) over items. This leads to our proposed taxonomy-aware latent factor model (TF) which combines taxonomies and latent factors using additive models. We develop efficient algorithms to train the TF models, which scales to large number of users/items and develop scalable inference/recommendation algorithms by exploiting the structure of the taxonomy. In addition, we extend the TF model to account for the temporal dynamics of user interests using high-order Markov chains . To deal with large-scale data, we develop a parallel multi-core implementation of our TF model. We empirically evaluate the TF model for the task of predicting user purchases using a real-world shopping dataset spanning more than a million users and products. Our experiments demonstrate the benefits of using our TF models over existing approaches, in terms of both prediction accuracy and running time.

Subfields: Information Systems, Management Science and Operations Research, Transportation

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Citations in the corpus

Inferring Networks of Substitutable and Complementary Products (2015, background): “Rather, each product should draw from a small number of topics, which can be encoded using a sparse representation [13].”
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence GBPR: Group Preference Based Bayesian Personalized Ranking for One-Class Collaborative Filtering (unknown): “…[Du et al. , 2011] than pointwise methods [Pan et al. , 2008; Hu et al. , 2008], and has been successfully adopted in many applications, e.g., tag recommendation [Rendle and Schmidt-Thieme, 2010], news recommendation [Yang et al. , 2011], and shopping items recommendation [Kanagal et al. , 2012].”
Explainable Recommendation through Attentive Multi-View Learning (2019, methodology): “Famous examples are taxonomy-based meth-ods that incorporate taxonomies of items into latent factor models (Kanagal et al. 2012; Zhang et al. 2014b).”
Improving pairwise learning for item recommendation from implicit feedback (2014, methodology): “…systems use BPR for learning, including tensor factorization models for tag recommendation [15], relation extraction [17], sequential shopping recommender with taxonomies [8], focused matrix factorization for advertisement [7], hierarchical latent factor models [1] or co-factorization machines [5].”
A Generic Coordinate Descent Framework for Learning from Implicit Feedback (2016, background): “It requires more complex, non-uniform, sampling strategies for dealing with this problem [12, 6].”