Reusable Self-Attention Recommender Systems in Fashion Industry Applications

• URL: http://arxiv.org/abs/2301.06777v1
• Date: Tue, 17 Jan 2023 10:00:17 GMT
• Title: Reusable Self-Attention Recommender Systems in Fashion Industry Applications
• Authors: Marjan Celikik, Jacek Wasilewski, Ana Peleteiro Ramallo
• Abstract summary: 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.
• Score: 0.0
• 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. Moreover, many of them do not consider side information such as item and customer metadata although deep-learning recommenders live up to their full potential only when numerous features of heterogeneous type are included. Also, normally the model is used only for a single use case. Due to these shortcomings, even if relevant, previous works are not always representative of their actual effectiveness in real-world industry applications. In this talk, we contribute to bridging this gap by presenting live experimental results demonstrating improvements in user retention of up to 30\%. Moreover, we share our learnings and challenges from building a re-usable and configurable recommender system for various applications from the fashion industry. In particular, we focus on fashion inspiration use-cases, such as outfit ranking, outfit recommendation and real-time personalized outfit generation.

Related papers

• Diversified Batch Selection for Training Acceleration [68.67164304377732]
  A prevalent research line, known as online batch selection, explores selecting informative subsets during the training process. vanilla reference-model-free methods involve independently scoring and selecting data in a sample-wise manner. We propose Diversified Batch Selection (DivBS), which is reference-model-free and can efficiently select diverse and representative samples.
  arXiv  Detail & Related papers  (2024-06-07T12:12:20Z)
• Personalized Negative Reservoir for Incremental Learning in Recommender Systems [20.346543608461204]
  In commercial settings, once a recommendation system model has been trained and deployed it needs to be updated frequently as new client data arrive. Naively fine-tuning solely on the new data runs into the well-documented problem of catastrophic forgetting. We propose a personalized negative reservoir strategy, which is used to obtain negative samples for the standard triplet loss of graph-based recommendation systems.
  arXiv  Detail & Related papers  (2024-03-06T19:08:28Z)
• Pre-trained Recommender Systems: A Causal Debiasing Perspective [19.712997823535066]
  We develop a generic recommender that captures universal interaction patterns by training on generic user-item interaction data extracted from different domains. Our empirical studies show that the proposed model could significantly improve the recommendation performance in zero- and few-shot learning settings.
  arXiv  Detail & Related papers  (2023-10-30T03:37:32Z)
• Impression-Aware Recommender Systems [53.48892326556546]
  We present a systematic literature review on recommender systems using impressions. We define a theoretical framework to delimit recommender systems using impressions and a novel paradigm for personalized recommendations, called impression-aware recommender systems.
  arXiv  Detail & Related papers  (2023-08-15T16:16:02Z)
• Reusable Self-Attention-based Recommender System for Fashion [1.978884131103313]
  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.
  arXiv  Detail & Related papers  (2022-11-29T16:47:20Z)
• When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications [0.0]
  We present an application-focused empirical survey of neural OpenIE models, training sets, and benchmarks. We find that the different assumptions made by different models and datasets have a statistically significant effect on performance.
  arXiv  Detail & Related papers  (2022-11-15T15:48:27Z)
• 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)
• Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling [66.02953670238647]
  Tail users suffer from significantly lower-quality recommendation than the head users after joint training. A model trained on tail users separately still achieve inferior results due to limited data. We propose a novel approach that significantly improves the recommendation performance of the tail users.
  arXiv  Detail & Related papers  (2022-08-19T02:50:19Z)
• Perceptual Score: What Data Modalities Does Your Model Perceive? [73.75255606437808]
  We introduce the perceptual score, a metric that assesses the degree to which a model relies on the different subsets of the input features. We find that recent, more accurate multi-modal models for visual question-answering tend to perceive the visual data less than their predecessors. Using the perceptual score also helps to analyze model biases by decomposing the score into data subset contributions.
  arXiv  Detail & Related papers  (2021-10-27T12:19:56Z)
• 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)
• 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.