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Found 28 papers, 12 papers with code
“Does it come in black?” CLIP-like models are zero-shot recommenders
Product discovery is a crucial component for online shopping.
(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis
As ecommerce continues growing, huge investments in ML and NLP for Information Retrieval are following.
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems
Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user retention and fairness, often taking a back seat.
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios.
Reasonable Scale Machine Learning with Open-Source Metaflow
As Machine Learning (ML) gains adoption across industries and new use cases, practitioners increasingly realize the challenges around effectively developing and iterating on ML systems: reproducibility, debugging, scalability, and documentation are elusive goals for real-world pipelines outside tech-first companies.
Witgenstein's influence on artificial intelligence
We examine how much of the contemporary progress in artificial intelligence (and, specifically, in natural language processing), can be, more or less directly, traced back to the seminal work and ideas of the Austrian-British philosopher Ludwig Wittgenstein, with particular focus on his late views.
EvalRS: a Rounded Evaluation of Recommender Systems
Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of user interfaces.
Contrastive language and vision learning of general fashion concepts
The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models.
"Does it come in black?" CLIP-like models are zero-shot recommenders
Product discovery is a crucial component for online shopping.
Beyond NDCG: behavioral testing of recommender systems with RecList
As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points.
DAG Card is the new Model Card
Following the intuition behind Model Cards, we propose DAG Cards as a form of documentation encompassing the tenets of a data-centric point of view.
You Do Not Need a Bigger Boat: Recommendations at Reasonable Scale in a (Mostly) Serverless and Open Stack
We argue that immature data pipelines are preventing a large portion of industry practitioners from leveraging the latest research on recommender systems.
"Are you sure?": Preliminary Insights from Scaling Product Comparisons to Multiple Shops
Large eCommerce players introduced comparison tables as a new type of recommendations.
Query2Prod2Vec: Grounded Word Embeddings for eCommerce
We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent space of products in a digital shop.
SIGIR 2021 E-Commerce Workshop Data Challenge
The 2021 SIGIR workshop on eCommerce is hosting the Coveo Data Challenge for "In-session prediction for purchase intent and recommendations".
Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction
We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines; in particular, we explore the emergence of semantic generalization from unsupervised dense representations outside of synthetic environments.
Query2Prod2Vec Grounded Word Embeddings for eCommerce
We present Query2Prod2Vec, a model that grounds lexical representations for product search in product embeddings: in our model, meaning is a mapping between words and a latent space of products in a digital shop.
BERT Goes Shopping: Comparing Distributional Models for Product Representations
Word embeddings (e. g., word2vec) have been applied successfully to eCommerce products through~\textit{prod2vec}.
Blending Search and Discovery: Tag-Based Query Refinement with Contextual Reinforcement Learning
We tackle tag-based query refinement as a mobile-friendly alternative to standard facet search.
On The Plurality of Graphs
We conduct a series of experiments designed to empirically demonstrate the effects of varying the structural features of a multi-agent emergent communication game framework.
Shopping in the Multiverse: A Counterfactual Approach to In-Session Attribution
We tackle the challenge of in-session attribution for on-site search engines in eCommerce.
Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario
This paper addresses the challenge of leveraging multiple embedding spaces for multi-shop personalization, proving that zero-shot inference is possible by transferring shopping intent from one website to another without manual intervention.
How to Grow a (Product) Tree: Personalized Category Suggestions for eCommerce Type-Ahead
In an attempt to balance precision and recall in the search page, leading digital shops have been effectively nudging users into select category facets as early as in the type-ahead suggestions.
"An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalization
We address the problem of personalizing query completion in a digital commerce setting, in which the bounce rate is typically high and recurring users are rare.
Lexical Learning as an Online Optimal Experiment: Building Efficient Search Engines through Human-Machine Collaboration
Information retrieval (IR) systems need to constantly update their knowledge as target objects and user queries change over time.
Less (Data) Is More: Why Small Data Holds the Key to the Future of Artificial Intelligence
The claims that big data holds the key to enterprise successes and that Artificial Intelligence is going to replace humanity have become increasingly more popular over the past few years, both in academia and in the industry.
Predicting e-commerce customer conversion from minimal temporal patterns on symbolized clickstream trajectories
Knowing if a user is a buyer or window shopper solely based on clickstream data is of crucial importance for e-commerce platforms seeking to implement real-time accurate NBA (next best action) policies.
Prediction is very hard, especially about conversion. Predicting user purchases from clickstream data in fashion e-commerce
Knowing if a user is a buyer vs window shopper solely based on clickstream data is of crucial importance for ecommerce platforms seeking to implement real-time accurate NBA (next best action) policies.
