Notes:
A recommender system, or a recommendation system, is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item. Recommender systems are utilized in a variety of areas, including movies, music, news, books, research articles, search queries, social tags, and products in general. In the context of virtual beings, a recommender system could be used to suggest potential responses or actions for the virtual being to take based on past interactions with users. For example, if a virtual being has consistently received positive feedback for making jokes in the past, a recommender system might suggest that the virtual being make a joke in a current interaction in order to increase the likelihood of a positive response from the user.
Resources:
- muricoca.github.io/crab .. recommender systems framework (python)
- easyrec.org .. open source recommendation engine (java)
- graphlab.org/collaborative_filtering .. tools for computing a linear model of data and predicting missing values (c++)
- lenskit.org .. recommender toolkit (java)
- mahout.apache.org .. scalable machine learning and data mining (java)
- mymedialite.net .. recommender system library
- r-forge.r-project.org .. central platform for the development of r packages
- code.richrelevance.com/reclab .. test recommender system models on synthetic and live data
- recommender101 .. lightweight and easy-to-use framework written (java)
- recommenderlab .. lab for developing and testing recommender algorithms (r)
- waffles.sourceforge.net .. machine learning toolkit (c++)
Wikipedia:
References:
- Recommender Systems for the Social Web (2012)
- Collaborative Filtering Recommender Systems (2011)
See also:
Conversational Recommender Systems 2014 | Recommender Dialog Systems 2013 | Recommender Dialog Systems 2014 | Recommender Dialog Systems 2015
- 075 Introduction to Spark Recommendation Systems
- 6.9 – Lesson 6.9: Recommender Systems: Collaborative Filtering – Part 3 [Text Retrieval and Searc…
- 6.8 – Lesson 6.8: Recommender Systems: Collaborative Filtering – Part 2 [Text Retrieval and Searc…
- 6.5 – Lesson 6.5: Recommender Systems: Content-Based Filtering – Part 1 [Text Retrieval and Searc…
- 6.7 – Lesson 6.7: Recommender Systems: Collaborative Filtering – Part 1 [Text Retrieval and Searc…
- 6.6 – Lesson 6.6: Recommender Systems: Content-Based Filtering – Part 2 [Text Retrieval and Searc…
- 4.3 – Assignment Intro Video [Recommender Systems: Evaluation and Metrics]
- 3.1 – Introduction to Online Evaluation and User Studies [Recommender Systems: Evaluation and Met…
- 2.6 – Programming Assignment Introduction [Recommender Systems: Evaluation and Metrics]
- 1.7 – Assignment Intro Video [Recommender Systems: Evaluation and Metrics]
- 1.1 – Introduction to Evaluation and Metrics [Recommender Systems: Evaluation and Metrics]
- 4.3 – Unified Mathematical Model [Introduction to Recommender Systems: Non-Personalized and Conte…
- 3.8 – Beyond TFIDF — Interview with Pasquale Lops [Introduction to Recommender Systems: Non-Pers…
- 4.2 – Honors: Intro to programming assignment [Introduction to Recommender Systems: Non-Personali…
- 3.6 – Dialog-Based Recommenders — Interview with Pearl Pu [Introduction to Recommender Systems: …
- 3.3 – Content-Based Filtering: Deeper Dive [Introduction to Recommender Systems: Non-Personalized…
- 3.5 – Case-Based Reasoning — Interview with Barry Smyth [Introduction to Recommender Systems: No…
- 3.2 – TFIDF and Content Filtering [Introduction to Recommender Systems: Non-Personalized and Cont…
- 3.4 – Entree Style Recommenders — Robin Burke Interview [Introduction to Recommender Systems: No…
- 3.1 – Introduction to Content-Based Recommenders [Introduction to Recommender Systems: Non-Person…
- 2.5 – Product Association Recommenders [Introduction to Recommender Systems: Non-Personalized and…
- 2.6 – Assignment #1 Intro Video [Introduction to Recommender Systems: Non-Personalized and Conten…
- 2.3 – Summary Statistics II [Introduction to Recommender Systems: Non-Personalized and Content-Ba…
- 2.2 – Summary Statistics I [Introduction to Recommender Systems: Non-Personalized and Content-Based]
- 2.4 – Demographics and Related Approaches [Introduction to Recommender Systems: Non-Personalized …
- 1.9 – Recommender Systems: Past, Present and Future [Introduction to Recommender Systems: Non-Per…
- 1.10 – Introducing the Honors Track [Introduction to Recommender Systems: Non-Personalized and Co…
- 1.8 – Tour of Amazon.com [Introduction to Recommender Systems: Non-Personalized and Content-Based]
- 1.7 – Taxonomy of Recommenders II [Introduction to Recommender Systems: Non-Personalized and Cont…
- 1.5 – Predictions and Recommendations [Introduction to Recommender Systems: Non-Personalized and …
- 2.1 – Non-Personalized and Stereotype-Based Recommenders [Introduction to Recommender Systems: No…
- 1.6 – Taxonomy of Recommenders I [Introduction to Recommender Systems: Non-Personalized and Conte…
- 1.11 – Honors: Setting up the development environment [Introduction to Recommender Systems: Non-P…
- 1.4 – Preferences and Ratings [Introduction to Recommender Systems: Non-Personalized and Content-…
- 1.1 – Intro to Recommender Systems [Introduction to Recommender Systems: Non-Personalized and Con…
- 1.2 – Intro to Course and Specialization [Introduction to Recommender Systems: Non-Personalized a…
- 1.3 – Movielens Tour [Introduction to Recommender Systems: Non-Personalized and Content-Based]
- 1.1 – Intro to Recommender Systems [Introduction to Recommender Systems: Non-Personalized and Con…
- Alexandre Hubert: How to Improve your Recommender System with Deep Learning
- How Does Netflix Recommend Movies || Netflix and Recommender Systems
- #bbuzz 17: Raam Rosh Hai – How to build a recommendation system overnight
- Introduction Serendipity in Recommender Systems
- How Big Data Is Used In Amazon Recommendation Systems To Change Our Lives
- Rubber Soil Information System -RubSIS-Online Fertilizer Recommendation in Rubber-How to use
- RecSys 2016 – Tutorial: Lessons Learned from Building Real-life Recommender Systems
- RecSys 2016: Tutorial on Group Recommender Systems
- RecSys 2016: Tutorial on Lessons Learned from Building Real-life Recommender Systems
- How Recommendation Systems Work On Amazon & Netflix | Simplilearn Webinar
- How Big Data Is Used In Amazon Recommendation Systems To Change Our Lives
- How to build a machine learning recommender systems and how to sell one to your boss
- [Chapter 1] Introduction to Recommender Systems
- Big Data Course Spring Unit 16 Lesson 2 Recommender Systems Introduction 720p
- Clustering of Recommender System Example Spring 2014 Unit 20 Lesson 3 MOOC Unit 16 Lesson 2 720p
- Big Data Course Spring Unit 16 Lesson 1 Recommender Systems as an Optimization Problem 720p
- Writing Recommender Systems with Java: An Introduction
- Science Café: How Did They Know I Wanted That? Recommendation Systems and Social Influence
- Introduction to Recommender Systems with Joseph A Konstan and Michael D Ekstrand
- Introduction to Recommendation Systems | Apache Mahout | Edureka
- HOW TO USE THE D2F RECOMMENDATION SYSTEM
- Big Data Course-Spring Unit 16 Lesson 2: Recommender Systems Introduction
- Big Data Course-Spring Unit 16 Lesson 1: Recommender Systems as an Optimization Problem
- Introduction to Recommender Systems with Joseph A Konstan and Michael D Ekstrand
- Recommendation system Intro
- Clustering of Recommender System Example Spring 2014: Unit 20 Lesson 3 / MOOC Unit 16 Lesson 2
- Big Data Course-Mooc Unit 13: Lesson 1: Recap of Recommender Systems I
- IU X-Informatics Unit 12: Lesson 2: Recommender Systems Introduction
- IU X-Informatics Unit 12: Lesson 1: Recommender Systems as an Optimization Problem
- Introduction: Recommender Systems