Deep learning for recommender systems: A Netflix case study

H Steck, L Baltrunas, E Elahi, D Liang, Y Raimond… - AI Magazine, 2021 - ojs.aaai.org
Deep learning has profoundly impacted many areas of machine learning. However, it took a
while for its impact to be felt in the field of recommender systems. In this article, we outline …

[图书][B] Recommender systems

CC Aggarwal - 2016 - Springer
“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …

When recurrent neural networks meet the neighborhood for session-based recommendation

D Jannach, M Ludewig - Proceedings of the eleventh ACM conference …, 2017 - dl.acm.org
Deep learning methods have led to substantial progress in various application fields of AI,
and in recent years a number of proposals were made to improve recommender systems …

Evaluation of session-based recommendation algorithms

M Ludewig, D Jannach - User Modeling and User-Adapted Interaction, 2018 - Springer
Recommender systems help users find relevant items of interest, for example on e-
commerce or media streaming sites. Most academic research is concerned with approaches …

Embarrassingly shallow autoencoders for sparse data

H Steck - The World Wide Web Conference, 2019 - dl.acm.org
Combining simple elements from the literature, we define a linear model that is geared
toward sparse data, in particular implicit feedback data for recommender systems. We show …

Collaborative filtering bandits

S Li, A Karatzoglou, C Gentile - … of the 39th International ACM SIGIR …, 2016 - dl.acm.org
Classical collaborative filtering, and content-based filtering methods try to learn a static
recommendation model given training data. These approaches are far from ideal in highly …

How powerful is graph convolution for recommendation?

Y Shen, Y Wu, Y Zhang, C Shan, J Zhang… - Proceedings of the 30th …, 2021 - dl.acm.org
Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms
for collaborative filtering (CF). Nevertheless, the theoretical underpinnings of their empirical …

The influence of organizational structure on software quality: an empirical case study

N Nagappan, B Murphy, V Basili - … of the 30th international conference on …, 2008 - dl.acm.org
Often software systems are developed by organizations consisting of many teams of
individuals working together. Brooks states in the Mythical Man Month book that product …

Neighborhood-based collaborative filtering

CC Aggarwal, CC Aggarwal - Recommender Systems: The Textbook, 2016 - Springer
Neighborhood-based collaborative filtering algorithms, also referred to as memory-based
algorithms, were among the earliest algorithms developed for collaborative filtering. These …

Movie recommendation system using collaborative filtering

CSM Wu, D Garg, U Bhandary - 2018 IEEE 9th International …, 2018 - ieeexplore.ieee.org
As the business needs are accelerating, there is an increased dependence on extracting
meaningful information from humongous amount of raw data to drive business solutions …