Corpus ID: 15960491

Pandora ’ s Music Recommender

  title={Pandora ’ s Music Recommender},
  author={M. Howe},
One of the great promises of the internet and Web 2.0 is the opportunity to expose people to new types of content. Companies like Amazon and Netflix provide customers with ideas for new items to purchase based on current or previous selections. For instance, someone who rented “Star Wars” at Netflix might be presented with “The Matrix” as another movie to rent. The challenge in this strategy is to make suggestions in a reasonable amount of time that the user will mostly like based on the known… Expand
A Novel Video Game Recommender System using Content Based Filtering -Vidya
This work is aimed at building an application that takes input from the user in the form of a few initial games and then proceeds to give recommendations to the user which they will tend to like.Expand
Predicting Yelp User ’ s Rating Based on Previous Reviews
  • Yue Li
  • 2016
Currently, the interactions between the user and the Yelp application is mainly initiated by the user searching for some keywords, and then go through a list of the matches, potentially ranked byExpand
Collaborative Filtering Based on a New Matrix Factorization Algorithm
  • Yongjie Yan, H. Xie, Li Ma, H. Jiang
  • Computer Science
  • 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA)
  • 2020
This paper proposes a new algorithm based on matrix decomposition, which takes the reviews among users as auxiliary information, and its performance is significantly better than the existing related algorithms. Expand
Recommender Systems: From Achievements to Requirements
A brief overview of recommender systems including their applications, limitations that still need to be worked on and some ideas that can be used to overcome some of those limitations are presented. Expand
Content-Based Music Recommendation using Deep Learning
Music streaming services use recommendation systems to improve the customer experience by generating favorable playlists and by fostering the discovery of new music. State of the art recommendationExpand
MPlist: Context aware music playlist
  • Maake Benard Magara, S. Ojo, S. Ngwira, T. Zuva
  • Computer Science
  • 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech)
  • 2016
A personalized context-aware music recommendation system, called MPlist, that dynamically and automatically creates a music playlist for music lovers based on their context, which has the advantage of solving the well-known cold start problem yet giving music lovers a personalized anywhere anytime music listening experience. Expand
GuideMe: service for consulting, publication and recommendation of touristic locations
Trabalho de Projeto realizado para obtencao do grau de Mestre em Engenharia Informatica e de Computadores


Item-based collaborative filtering recommendation algorithms
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. Expand
Application of Dimensionality Reduction in Recommender System - A Case Study
This paper presents two different experiments where one technology called Singular Value Decomposition (SVD) is explored to reduce the dimensionality of recommender system databases and suggests that SVD has the potential to meet many of the challenges ofRecommender systems, under certain conditions. Expand
Learning with General Proximity Measures
Proximity is the basic quality which identifies and characterizes groups of objects in various domains and contexts. When objects are compared to a set of chosen prototype examples, proximity can beExpand
Available: westergren.html (Accessed 5
  • Available: westergren.html (Accessed 5
  • 2007
How Pandora's matching service cuts the chaos of digital music
  • Available:
  • 2007
Tim Westergren
  • Available:
  • 2007
Tim Westergren of Pandora Media (radio interview)
  • Available:
  • 2007
Tim Westergren of Pandora Media (radio interview) Available:
  • Inside the Net
  • 2007
Item-based Collaborative Filtering Recommender Algorithms Accepted for publication at the WWW10 Conference
  • Item-based Collaborative Filtering Recommender Algorithms Accepted for publication at the WWW10 Conference
  • 2001