Reusable Self-Attention-based Recommender System for Fashion

• URL: http://arxiv.org/abs/2211.16366v1
• Date: Tue, 29 Nov 2022 16:47:20 GMT
• Title: Reusable Self-Attention-based Recommender System for Fashion
• Authors: Marjan Celikik, Jacek Wasilewski, Sahar Mbarek, Pablo Celayes, Pierre Gagliardi, Duy Pham, Nour Karessli, Ana Peleteiro Ramallo
• Abstract summary: We present a reusable Attention-based Fashion Recommendation Algorithm (AFRA) We leverage temporal and contextual information to address both short and long-term customer preferences. We show its effectiveness on outfit recommendation use cases, in particular: 1) personalized ranked feed; 2) outfit recommendations by style; 3) similar item recommendation and 4) in-session recommendations inspired by most recent customer actions.
• Score: 1.978884131103313
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A large number of empirical studies on applying self-attention models in the domain of recommender systems are based on offline evaluation and metrics computed on standardized datasets, without insights on how these models perform in real life scenarios. Moreover, many of them do not consider information such as item and customer metadata, although deep-learning recommenders live up to their full potential only when numerous features of heterogeneous types are included. Also, typically recommendation models are designed to serve well only a single use case, which increases modeling complexity and maintenance costs, and may lead to inconsistent customer experience. In this work, we present a reusable Attention-based Fashion Recommendation Algorithm (AFRA), that utilizes various interaction types with different fashion entities such as items (e.g., shirt), outfits and influencers, and their heterogeneous features. Moreover, we leverage temporal and contextual information to address both short and long-term customer preferences. We show its effectiveness on outfit recommendation use cases, in particular: 1) personalized ranked feed; 2) outfit recommendations by style; 3) similar item recommendation and 4) in-session recommendations inspired by most recent customer actions. We present both offline and online experimental results demonstrating substantial improvements in customer retention and engagement.

Related papers

• Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations [50.03560306423678]
  We propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems. Ada-Retrieval iteratively refines user representations to better capture potential candidates in the full item space.
  arXiv  Detail & Related papers  (2024-01-12T15:26:40Z)
• Reusable Self-Attention Recommender Systems in Fashion Industry Applications [0.0]
  We present live experimental results demonstrating improvements in user retention of up to 30%. We focus on fashion inspiration use-cases, such as outfit ranking, outfit recommendation and real-time personalized outfit generation.
  arXiv  Detail & Related papers  (2023-01-17T10:00:17Z)
• Knowledge Enhancement for Multi-Behavior Contrastive Recommendation [39.50243004656453]
  We propose a Knowledge Enhancement Multi-Behavior Contrastive Learning Recommendation (KMCLR) framework. In this work, we design the multi-behavior learning module to extract users' personalized behavior information for user-embedding enhancement. In the optimization stage, we model the coarse-grained commonalities and the fine-grained differences between multi-behavior of users to further improve the recommendation effect.
  arXiv  Detail & Related papers  (2023-01-13T06:24:33Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• Recommendation with User Active Disclosing Willingness [20.306413327597603]
  We study a novel recommendation paradigm, where the users are allowed to indicate their "willingness" on disclosing different behaviors. We conduct extensive experiments to demonstrate the effectiveness of our model on balancing the recommendation quality and user disclosing willingness.
  arXiv  Detail & Related papers  (2022-10-25T04:43:40Z)
• 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)
• Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation [56.12499090935242]
  This work proposes a Knowledge-Enhanced Hierarchical Graph Transformer Network (KHGT) to investigate multi-typed interactive patterns between users and items in recommender systems. KHGT is built upon a graph-structured neural architecture to capture type-specific behavior characteristics. We show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings.
  arXiv  Detail & Related papers  (2021-10-08T09:44:00Z)
• PURS: Personalized Unexpected Recommender System for Improving User Satisfaction [76.98616102965023]
  We describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches.
  arXiv  Detail & Related papers  (2021-06-05T01:33:21Z)
• Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users [111.28351584726092]
  We consider the conversational recommendation for cold-start users, where a system can both ask the attributes from and recommend items to a user interactively. Our Conversational Thompson Sampling (ConTS) model holistically solves all questions in conversational recommendation by choosing the arm with the maximal reward to play.
  arXiv  Detail & Related papers  (2020-05-23T08:56:37Z)
• Learning Diverse Fashion Collocation by Neural Graph Filtering [78.9188246136867]
  We propose a novel fashion collocation framework, Neural Graph Filtering, that models a flexible set of fashion items via a graph neural network. By applying symmetric operations on the edge vectors, this framework allows varying numbers of inputs/outputs and is invariant to their ordering. We evaluate the proposed approach on three popular benchmarks, the Polyvore dataset, the Polyvore-D dataset, and our reorganized Amazon Fashion dataset.
  arXiv  Detail & Related papers  (2020-03-11T16:17:08Z)

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.