Deep Learning-based Online Alternative Product Recommendations at Scale

• URL: http://arxiv.org/abs/2104.07572v1
• Date: Thu, 15 Apr 2021 16:27:45 GMT
• Title: Deep Learning-based Online Alternative Product Recommendations at Scale
• Authors: Mingming Guo, Nian Yan, Xiquan Cui, San He Wu, Unaiza Ahsan, Rebecca West, Khalifeh Al Jadda
• Abstract summary: We use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision.
• Score: 0.2278231643598956
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Alternative recommender systems are critical for ecommerce companies. They guide customers to explore a massive product catalog and assist customers to find the right products among an overwhelming number of options. However, it is a non-trivial task to recommend alternative products that fit customer needs. In this paper, we use both textual product information (e.g. product titles and descriptions) and customer behavior data to recommend alternative products. Our results show that the coverage of alternative products is significantly improved in offline evaluations as well as recall and precision. The final A/B test shows that our algorithm increases the conversion rate by 12 percent in a statistically significant way. In order to better capture the semantic meaning of product information, we build a Siamese Network with Bidirectional LSTM to learn product embeddings. In order to learn a similarity space that better matches the preference of real customers, we use co-compared data from historical customer behavior as labels to train the network. In addition, we use NMSLIB to accelerate the computationally expensive kNN computation for millions of products so that the alternative recommendation is able to scale across the entire catalog of a major ecommerce site.

Related papers

• End-to-end Learnable Clustering for Intent Learning in Recommendation [54.157784572994316]
  We propose a novel intent learning method termed underlineELCRec. It unifies behavior representation learning into an underlineEnd-to-end underlineLearnable underlineClustering framework. We deploy this method on the industrial recommendation system with 130 million page views and achieve promising results.
  arXiv  Detail & Related papers  (2024-01-11T15:22:55Z)
• Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation [127.35910314813854]
  We present the Amazon Multi-locale Shopping Session dataset, namely Amazon-M2. It is the first multilingual dataset consisting of millions of user sessions from six different locales. Remarkably, the dataset can help us enhance personalization and understanding of user preferences.
  arXiv  Detail & Related papers  (2023-07-19T00:08:49Z)
• A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data [7.427088261927881]
  The proposed model has been deployed in a large-scale E-commerce website for 11 marketplaces in 6 languages. Our proposed model is demonstrated to increase revenue by 19% based on an online A/B experiment.
  arXiv  Detail & Related papers  (2022-11-04T15:57:19Z)
• Identifying Substitute and Complementary Products for Assortment Optimization with Cleora Embeddings [0.0]
  The paper introduces a novel method for finding products' substitutes and complements based on the graph embedding Cleora algorithm. It is concluded that the new approach presented here offers suitable choices of recommended products, requiring a minimal amount of additional information.
  arXiv  Detail & Related papers  (2022-08-10T11:56:36Z)
• 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)
• ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest [60.841761065439414]
  At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
  arXiv  Detail & Related papers  (2022-05-24T02:28:58Z)
• User-Inspired Posterior Network for Recommendation Reason Generation [53.035224183349385]
  Recommendation reason generation plays a vital role in attracting customers' attention as well as improving user experience. We propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users' interests. Experimental results show that our model is superior to traditional generative models.
  arXiv  Detail & Related papers  (2021-02-16T02:08:52Z)
• Personalized Embedding-based e-Commerce Recommendations at eBay [3.1236273633321416]
  We present an approach for generating personalized item recommendations in an e-commerce marketplace by learning to embed items and users in the same vector space. Data ablation is incorporated into the offline model training process to improve the robustness of the production system.
  arXiv  Detail & Related papers  (2021-02-11T17:58:51Z)
• CRACT: Cascaded Regression-Align-Classification for Robust Visual Tracking [97.84109669027225]
  We introduce an improved proposal refinement module, Cascaded Regression-Align- Classification (CRAC) CRAC yields new state-of-the-art performances on many benchmarks. In experiments on seven benchmarks including OTB-2015, UAV123, NfS, VOT-2018, TrackingNet, GOT-10k and LaSOT, our CRACT exhibits very promising results in comparison with state-of-the-art competitors.
  arXiv  Detail & Related papers  (2020-11-25T02:18:33Z)
• Large-scale Real-time Personalized Similar Product Recommendations [28.718371564543517]
  We introduce our real-time personalized algorithm to model product similarity and real-time user interests. Our method achieves a 10% improvement on the add-cart number in the real-world e-commerce scenario.
  arXiv  Detail & Related papers  (2020-04-12T23:16:14Z)

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.