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Journal ArticleDOI

The MovieLens Datasets: History and Context

TLDR
The history of MovieLens and the MovieLens datasets is documents, including a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization, and best practices and limitations of using the Movie Lens datasets in new research are documented.
Abstract
The MovieLens datasets are widely used in education, research, and industry. They are downloaded hundreds of thousands of times each year, reflecting their use in popular press programming books, traditional and online courses, and software. These datasets are a product of member activity in the MovieLens movie recommendation system, an active research platform that has hosted many experiments since its launch in 1997. This article documents the history of MovieLens and the MovieLens datasets. We include a discussion of lessons learned from running a long-standing, live research platform from the perspective of a research organization. We document best practices and limitations of using the MovieLens datasets in new research.

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Proceedings ArticleDOI

Deep Interest Network for Click-Through Rate Prediction

TL;DR: A novel model: Deep Interest Network (DIN) is proposed which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad.
Proceedings ArticleDOI

Neural Factorization Machines for Sparse Predictive Analytics

TL;DR: Neural Factorization Machines (NFM) as discussed by the authors is a special case of NFM without hidden layers, which combines the linearity of FM in modelling second-order feature interactions and the non-linearity of neural network in modelling higher-order features.

The Lean Startup:How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses

TL;DR: Ries was one of the pioneers of the Lean Startup philosophy as discussed by the authors, based on the Japanese Philosophy of Lean Manufacturing, and he pioneered the philosophy of Lean Startup based on his experience with multiple startups.
Posted Content

Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

TL;DR: DGL distills the computational patterns of GNNs into a few generalized sparse tensor operations suitable for extensive parallelization and allows users to easily port and leverage the existing components across multiple deep learning frameworks.
Journal ArticleDOI

Fastai: A Layered API for Deep Learning

TL;DR: This paper has used this library to successfully create a complete deep learning course, which was able to write more quickly than using previous approaches, and the code was more clear.
References
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Proceedings ArticleDOI

Item-based collaborative filtering recommendation algorithms

TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Proceedings ArticleDOI

GroupLens: an open architecture for collaborative filtering of netnews

TL;DR: GroupLens is a system for collaborative filtering of netnews, to help people find articles they will like in the huge stream of available articles, and protect their privacy by entering ratings under pseudonyms, without reducing the effectiveness of the score prediction.
Journal ArticleDOI

GroupLens: applying collaborative filtering to Usenet news

TL;DR: The combination of high volume and personal taste made Usenet news a promising candidate for collaborative filtering and the potential predictive utility for Usenets news was very high.
Journal ArticleDOI

Item-based top-N recommendation algorithms

TL;DR: This article presents one class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended, and shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.
Proceedings ArticleDOI

Image-Based Recommendations on Styles and Substitutes

TL;DR: The approach is not based on fine-grained modeling of user annotations but rather on capturing the largest dataset possible and developing a scalable method for uncovering human notions of the visual relationships within.
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