Search Results for author: Jacopo Tagliabue

Found 22 papers, 8 papers with code

Beyond NDCG: behavioral testing of recommender systems with RecList

1 code implementation18 Nov 2021 Patrick John Chia, Jacopo Tagliabue, Federico Bianchi, Chloe He, Brian Ko

As with most Machine Learning systems, recommender systems are typically evaluated through performance metrics computed over held-out data points.

Recommendation Systems

DAG Card is the new Model Card

2 code implementations24 Oct 2021 Jacopo Tagliabue, Ville Tuulos, Ciro Greco, Valay Dave

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

2 code implementations15 Jul 2021 Jacopo Tagliabue

We argue that immature data pipelines are preventing a large portion of industry practitioners from leveraging the latest research on recommender systems.

Recommendation Systems

Query2Prod2Vec: Grounded Word Embeddings for eCommerce

1 code implementation NAACL 2021 Federico Bianchi, Jacopo Tagliabue, Bingqing Yu

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.

Word Embeddings

SIGIR 2021 E-Commerce Workshop Data Challenge

3 code implementations19 Apr 2021 Jacopo Tagliabue, Ciro Greco, Jean-Francis Roy, Bingqing Yu, Patrick John Chia, Federico Bianchi, Giovanni Cassani

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

no code implementations NAACL 2021 Federico Bianchi, Ciro Greco, Jacopo Tagliabue

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.

Grounded language learning

Query2Prod2Vec Grounded Word Embeddings for eCommerce

1 code implementation2 Apr 2021 Federico Bianchi, Jacopo Tagliabue, Bingqing Yu

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.

Word Embeddings

On The Plurality of Graphs

no code implementations3 Aug 2020 Nicole Fitzgerald, Jacopo Tagliabue

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.

Fantastic Embeddings and How to Align Them: Zero-Shot Inference in a Multi-Shop Scenario

no code implementations20 Jul 2020 Federico Bianchi, Jacopo Tagliabue, Bingqing Yu, Luca Bigon, Ciro Greco

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

1 code implementation WS 2020 Jacopo Tagliabue, Bingqing Yu, Marie Beaulieu

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

no code implementations11 Mar 2020 Bingqing Yu, Jacopo Tagliabue, Ciro Greco, Federico Bianchi

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

no code implementations30 Oct 2019 Jacopo Tagliabue, Reuben Cohn-Gordon

Information retrieval (IR) systems need to constantly update their knowledge as target objects and user queries change over time.

Information Retrieval

Less (Data) Is More: Why Small Data Holds the Key to the Future of Artificial Intelligence

no code implementations22 Jul 2019 Ciro Greco, Andrea Polonioli, Jacopo Tagliabue

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.

Natural Language Processing Small Data Image Classification

Predicting e-commerce customer conversion from minimal temporal patterns on symbolized clickstream trajectories

no code implementations3 Jul 2019 Jacopo Tagliabue, Lucas Lacasa, Ciro Greco, Mattia Pavoni, Andrea Polonioli

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

no code implementations30 Jun 2019 Luca Bigon, Giovanni Cassani, Ciro Greco, Lucas Lacasa, Mattia Pavoni, Andrea Polonioli, Jacopo Tagliabue

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.

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