UNICON: A unified framework for behavior-based consumer segmentation in e-commerce

• URL: http://arxiv.org/abs/2309.13068v1
• Date: Mon, 18 Sep 2023 14:58:13 GMT
• Title: UNICON: A unified framework for behavior-based consumer segmentation in e-commerce
• Authors: Manuel Dibak, Vladimir Vlasov, Nour Karessli, Darya Dedik, Egor Malykh, Jacek Wasilewski, Ton Torres, Ana Peleteiro Ramallo
• Abstract summary: Group-based personalization provides a moderate level of personalization built on broader common preferences of a consumer segment. We introduce UNICON, a unified deep learning consumer segmentation framework. We demonstrate through extensive experimentation our framework effectiveness in fashion to identify lookalike Designer audience and data-driven style segments.
• Score: 0.9213852038999552
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven personalization is a key practice in fashion e-commerce, improving the way businesses serve their consumers needs with more relevant content. While hyper-personalization offers highly targeted experiences to each consumer, it requires a significant amount of private data to create an individualized journey. To alleviate this, group-based personalization provides a moderate level of personalization built on broader common preferences of a consumer segment, while still being able to personalize the results. We introduce UNICON, a unified deep learning consumer segmentation framework that leverages rich consumer behavior data to learn long-term latent representations and utilizes them to extract two pivotal types of segmentation catering various personalization use-cases: lookalike, expanding a predefined target seed segment with consumers of similar behavior, and data-driven, revealing non-obvious consumer segments with similar affinities. We demonstrate through extensive experimentation our framework effectiveness in fashion to identify lookalike Designer audience and data-driven style segments. Furthermore, we present experiments that showcase how segment information can be incorporated in a hybrid recommender system combining hyper and group-based personalization to exploit the advantages of both alternatives and provide improvements on consumer experience.

Related papers

• Preserving Individuality while Following the Crowd: Understanding the Role of User Taste and Crowd Wisdom in Online Product Rating Prediction [31.42047941275096]
  We propose a unique and practical approach that emphasizes historical ratings at both the user and product levels. We have developed an efficient data processing strategy that makes this approach highly scalable and easily deployable.
  arXiv  Detail & Related papers  (2024-09-06T23:16:06Z)
• Retrieval Augmentation via User Interest Clustering [57.63883506013693]
  Industrial recommender systems are sensitive to the patterns of user-item engagement. We propose a novel approach that efficiently constructs user interest and facilitates low computational cost inference. Our approach has been deployed in multiple products at Meta, facilitating short-form video related recommendation.
  arXiv  Detail & Related papers  (2024-08-07T16:35:10Z)
• Personalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph [49.66272783945571]
  We propose Personalized Federated knowledge graph Embedding with client-wise relation graph. PFedEG learns personalized supplementary knowledge for each client by amalgamating entity embedding from its neighboring clients. We conduct extensive experiments on four benchmark datasets to evaluate our method against state-of-the-art models.
  arXiv  Detail & Related papers  (2024-06-17T17:44:53Z)
• Exploring 360-Degree View of Customers for Lookalike Modeling [3.264007084815591]
  We propose a novel framework that unifies the customers different behaviors or features such as demographics, buying behaviors on different platforms, customer loyalty behaviors and build a lookalike model to improve customer targeting for Rakuten Group, Inc.
  arXiv  Detail & Related papers  (2023-04-17T14:01:12Z)
• PARSRec: Explainable Personalized Attention-fused Recurrent Sequential Recommendation Using Session Partial Actions [0.5801044612920815]
  We propose an architecture that relies on common patterns as well as individual behaviors to tailor its recommendations for each person. Our empirical results on Nielsen Consumer Panel dataset indicate that the proposed approach achieves up to 27.9% performance improvement.
  arXiv  Detail & Related papers  (2022-09-16T12:07:43Z)
• Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval [152.3504607706575]
  This research aims to conduct weakly-supervised multi-modal instance-level product retrieval for fine-grained product categories. We first contribute the Product1M datasets, and define two real practical instance-level retrieval tasks. We exploit to train a more effective cross-modal model which is adaptively capable of incorporating key concept information from the multi-modal data.
  arXiv  Detail & Related papers  (2022-06-17T15:40:45Z)
• SuperCone: Modeling Heterogeneous Experts with Concept Meta-learning for Unified Predictive Segments System [8.917697023052257]
  We present SuperCone, our unified predicative segments system. It builds on top of a flat concept representation that summarizes each user's heterogeneous digital footprints. It can outperform state-of-the-art recommendation and ranking algorithms on a wide range of predicative segment tasks.
  arXiv  Detail & Related papers  (2022-03-09T04:11:39Z)
• Overcoming Data Sparsity in Group Recommendation [52.00998276970403]
  Group recommender systems should be able to accurately learn not only users' personal preferences but also preference aggregation strategy. In this paper, we take Bipartite Graphding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way.
  arXiv  Detail & Related papers  (2020-10-02T07:11:19Z)
• Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding [53.02059906193556]
  We propose to predict consumers' purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers. Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
  arXiv  Detail & Related papers  (2020-07-14T06:06:41Z)
• Disentangled Graph Collaborative Filtering [100.26835145396782]
  Disentangled Graph Collaborative Filtering (DGCF) is a new model for learning informative representations of users and items from interaction data. By modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. DGCF achieves significant improvements over several state-of-the-art models like NGCF, DisenGCN, and MacridVAE.
  arXiv  Detail & Related papers  (2020-07-03T15:37:25Z)
• Counterfactual Inference for Consumer Choice Across Many Product Categories [6.347014958509367]
  We build on techniques from the machine learning literature on probabilistic models of matrix factorization. We show that our model improves over traditional modeling approaches that consider each category in isolation. Using held-out data, we show that our model can accurately distinguish which consumers are most price sensitive to a given product.
  arXiv  Detail & Related papers  (2019-06-06T15:11:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.