invited-talk

Machine Learning @ Amazon

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Rajeev RastogiAuthors Info & Claims

CODS-IKDD '15: Proceedings of the 2nd IKDD Conference on Data Sciences

Article No.: 2, Page 1

https://doi.org/10.1145/2778865.2778867

Published: 20 March 2015 Publication History

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Abstract

In this talk, I will first provide an overview of the key Machine Learning (ML) applications we are developing at Amazon. I will then describe a matrix factorization model that we have developed for making product recommendations âĂŞ the salient characteristics of the model are: (1) It uses a Bayesian approach to handle data sparsity, (2) It leverages user and item features to handle the cold start problem, and (3) It introduces latent variables to handle multiple personas associated with a user account (e.g. family members). Our experimental results with synthetic and real-life datasets show that leveraging user and item features, and incorporating user personas enables our model to provide lower RMSE and perplexity compared to baselines.

Cited By

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Dhaouadi JAktas MKalipsiz OBalcik E(2019)On the Use of Hyperparameter Optimization in Big Data Processing Pipelines: A Case Study2019 Innovations in Intelligent Systems and Applications Conference (ASYU)10.1109/ASYU48272.2019.8946352(1-5)Online publication date: Oct-2019
https://doi.org/10.1109/ASYU48272.2019.8946352
Alghofaily BDing C(2019)Data mining service recommendation based on dataset featuresService Oriented Computing and Applications10.1007/s11761-019-00272-y13:3(261-277)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s11761-019-00272-y

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Machine Learning @ Amazon
1. Computing methodologies
  1. Machine learning

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Information

Published In

CODS-IKDD '15: Proceedings of the 2nd IKDD Conference on Data Sciences

March 2015

29 pages

ISBN:9781450336161

DOI:10.1145/2778865

General Chairs:
Manish Gupta
Xerox Research Center, India
,
Y. Narahari
Indian Institute of Science, Bangalore
,
Program Chairs:
V. S. Subrahmanian
University of Maryland, College Park
,
Indrajit Bhattacharya
IBM Research, India

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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ACM: Association for Computing Machinery

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 March 2015

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Machine Learning

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Invited-talk
Research
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CODS-IKDD '15

CODS-IKDD '15: 2nd IKDD Conference on Data Sciences

March 20, 2015

Bangalore, India

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Cited By

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Dhaouadi JAktas MKalipsiz OBalcik E(2019)On the Use of Hyperparameter Optimization in Big Data Processing Pipelines: A Case Study2019 Innovations in Intelligent Systems and Applications Conference (ASYU)10.1109/ASYU48272.2019.8946352(1-5)Online publication date: Oct-2019
https://doi.org/10.1109/ASYU48272.2019.8946352
Alghofaily BDing C(2019)Data mining service recommendation based on dataset featuresService Oriented Computing and Applications10.1007/s11761-019-00272-y13:3(261-277)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s11761-019-00272-y

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Machine learning-based book recommender system: a survey and new perspectives