e-commerce

Unposed: Unsupervised pose estimated based product image recommendations

AI-ML Systems 2022

2022

Product images are the most impressing medium of customer interaction on the product detail pages of e-commerce websites. Millions of products are onboarded on to webstore catalogues daily and maintaining a high quality bar for a product’s set of images is a problem at scale. Grouping products by categories, clothing is a very high volume and high velocity category and thus deserves its own attention. Given

Computer vision

Efficient graph based recommender system with weighted averaging of messages

Faizan Ahemad

AI-ML Systems 2022

2022

We showcase a novel solution to a recommendation system problem where we face a perpetual soft item cold start issue. Our system aims to recommend demanded products to prospective sellers for listing in Amazon stores. These products always have only few interactions thereby giving rise to a perpetual soft item cold start situation. Modern collaborative filtering methods solve cold start using content attributes

Machine learning

Shopping queries dataset: A large-scale ESCI benchmark for improving product search

Chandan Reddy, Nurendra Choudhary, Lluís Marquez, Fran Valero, Nikhil Rao, Hugo Zaragoza, Sambaran Bandyopadhyay, Arnab Biswas

2022

We introduce the “Shopping Queries Data Set”, a large dataset of difficult search queries, released with the aim of fostering research in the area of semantic matching of queries and products. For each query, the dataset provides a list of up to 40 potentially relevant results, together with ESCI relevance judgements (Exact, Substitute, Complement, Irrelevant) indicating the relevance of the product to

Search and information retrieval

Deadline-aware multi-agent tour planning

Taoan Huang, Vikas Shivashankar, Michael Caldara, Joseph W. Durham, Jiaoyang Li, Bistra Dilkina, Sven Koenig

JCAI 2022 Workshop on Heuristic Search in Industry

2022

The increasing demand for same-day delivery and the commitment of e-commerce companies to this service raise a number of challenges in logistics. One of these challenges for fulfillment centers is to coordinate hundreds of mobile robots in their automated warehouses efficiently to allow for the retrieval and packing of thousands of ordered items within the promised delivery deadlines. We formulate this

Operations research and optimization

ColdGuess: A general and effective relational graph convolutional network to tackle cold start cases

Bo He, Xiang Song, Vincent Gao, Christos Faloutsos

KDD 2022 Workshop on Mining and Learning with Graphs

2022

Low-quality listings and bad actor behavior in online retail websites threatens e-commerce business as these result in sub-optimal buying experience and erode customer trust. When a new listing is created, how to tell it has good quality? Is the method effective, fast, and scalable? Previous approaches often face three limitations/challenges: (1) unable to handle cold start problems where new sellers/listings

Machine learning

Mitigating targeting bias in content recommendation with causal bandits'

Yan Zhao, Mitchell Goodman, Sameer Kanase, Shenghe Xu, Yannick Kimmel, Brent Payne, Saad Khan, Patricia Grao

Recsys 2022 Workshop on Multi-Objective Recommender Systems

2022

Recommendations systems play a central role in improving customer experience on the Amazon retail website. Commonly, Learning-to-Rank (LTR) methods are employed to rank content, however these methods are subject to bias inherent in the observational data that they use for training. This paper studies a domain-specific self-selection bias, called Content Targeting Bias, introduced when content is generated

Machine learning

Analyzing the support-level for tips extracted from product reviews

Miriam Farber, David Carmel, Lital Kuchy, Avihai Mejer

SIGIR 2022

2022

Useful tips extracted from product reviews assist customers to take a more informed purchase decision, as well as making a better, easier, and safer usage of the product. In this work we argue that extracted tips should be examined based on the amount of support and opposition they receive from all product reviews. A classifier, developed for this purpose, determines the degree to which a tip is supported

Conversational AI

Sub-task imputation via self-labelling to train image moderation models on sparse noisy data

Indraneil Paul, Sumit Negi

CIKM 2022

2022

E-commerce marketplaces protect shopper experience and trust at scale by deploying deep learning models trained on human annotated moderation data, for the identification and removal of advert imagery that does not comply with moderation policies (a.k.a. defective images). However, human moderation labels can be hard to source for smaller advert programs that target specific device types with separate formats

Machine learning

Debiased balanced interleaving at Amazon Search

Nan Bi, Pablo Castells, Daniel Gilbert, Slava Galperin, Patrick Tardif, Sachin Ahuja

CIKM 2022

2022

Interleaving is an online evaluation technique that has shown to be orders of magnitude more sensitive than traditional A/B tests. It presents users with a single merged result of the compared rankings and then attributes user actions back to the evaluated rankers. Different interleaving methods in the literature have their advantages and limitations with respect to unbiasedness, sensitivity, preservation

Search and information retrieval

Catalog Phrase Grounding (CPG): Grounding of product textual attributes in product images for e-commerce vision-language applications

Wenyi Wu, Karim Bouyarmane, Ismail Tutar

KDD 2022 Workshop on First Content Understanding and Generation for e-Commerce

2022

We present Catalog Phrase Grounding (CPG), a model that can associate product textual data (title, brands) into corresponding regions of product images (isolated product region, brand logo region) for e-commerce vision-language applications. We use a state-of-the-art modulated multimodal transformer encoder-decoder architecture unifying object detection and phrase-grounding. We train the model in self-supervised

Computer vision

e-commerce

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