Deep learning for recommender systems, or How to compare pears with apples / Marcel Kurovski.

Format:

Conference/Event

Video

Author/Creator:

Kurovski, Marcel, on-screen presenter.

Conference Name:

O'Reilly Artificial Intelligence Conference (2019 : New York, New York)., issuing body.

Language:

English

Subjects (All):

Recommender systems (Information filtering).

Machine learning.

Customer relations--Management.

Customer relations.

Electronic commerce.

Physical Description:

1 online resource (1 streaming video file (41 min., 51 sec.)) : digital, sound, color

Place of Publication:

[Place of publication not identified] : O'Reilly, 2019.

Summary:

"Recommender systems support the decision making processes of customers with personalized suggestions. These widely used systems influence the daily life of almost everyone across domains like ecommerce, social media, and entertainment. However, the efficient generation of relevant recommendations in large-scale systems is a very complex task. In order to provide personalization, engines and algorithms need to capture users' varying tastes and find mostly nonlinear dependencies between them and a multitude of items. Enormous data sparsity and ambitious real-time requirements further complicate this challenge. At the same time, deep learning has been proven to solve complex tasks like object or speech recognition where traditional machine learning failed or showed mediocre performance. Join Marcel Kurovski (inovex) to explore a use case for vehicle recommendations at mobile.de, Germany's biggest online vehicle market. Marcel shares a novel regularization technique for the optimization criterion and evaluates it against various baselines. To achieve high scalability, he combines this method with strategies for efficient candidate generation based on user and item embeddings--providing a holistic solution for candidate generation and ranking. The proposed approach outperforms collaborative filtering and hybrid collaborative-content-based filtering by 73% and 143% for MAP@5. It also scales well for millions of items and users returning recommendations in tens of milliseconds."--Resource description page.

Participant:

Presenter, Marcel Kurovski.

Notes:

Title from title screen (viewed November 14, 2019).

Recorded April 18, 2019 at the O'Reilly Artificial Intelligence Conference in New York.

OCLC:

1127651205