In e-commerce, user representations are essential for various applications.
Existing methods often use deep learning techniques to convert customer
behaviors into implicit embeddings. Tuttavia, these embeddings are difficult to
understand and integrate with external knowledge, limiting the effectiveness of
applications such as customer segmentation, search navigation, and product
recommendations. Per affrontare questo problema, our paper introduces the concept of the
customer persona. Condensed from a customer’s numerous purchasing histories, UN
customer persona provides a multi-faceted and human-readable characterization
of specific purchase behaviors and preferences, such as Busy Parents or Bargain
Hunters.
This work then focuses on representing each customer by multiple personas
from a predefined set, achieving readable and informative explicit user
representations. A tal fine, we propose an effective and efficient solution
GPLR. To ensure effectiveness, GPLR leverages pre-trained LLMs to infer
personas for customers. To reduce overhead, GPLR applies LLM-based labeling to
only a fraction of users and utilizes a random walk technique to predict
personas for the remaining customers. We further propose RevAff, which provides
an absolute error $\epsilon$ guarantee while improving the time complexity of
the exact solution by a factor of at least
$O(\frac{\epsilon\cdot|E|N}{|E|+N\log N})$, where $N$ represents the number of
customers and products, and $E$ represents the interactions between them. Noi
evaluate the performance of our persona-based representation in terms of
accuracy and robustness for recommendation and customer segmentation tasks
using three real-world e-commerce datasets. Most notably, we find that
integrating customer persona representations improves the state-of-the-art
graph convolution-based recommendation model by up to 12% in terms of NDCG@K
and F1-Score@K.

Questo articolo esplora i giri e le loro implicazioni.

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