Just a girl in Tech

Great lecture on Recommender Systems by   Xavier Amatriain,  Researcher on Netflix.  https://www.youtube.com/watch?v=bLhq63ygoU8 https://www.youtube.com/watch?v=mRToFXlNBpQ

I received emails from users asking which recommender system algorithm they should use . Usually people start looking for articles on which approach has a better performance, and once they find something convincing they start to implement it. I believe that the best recommender system depends on the data and the problem you have to deal with. With that in mind, I decided to publish here some pros and cons for each recommender type (collaborative, content and hybrid), so people can decide for their own what algoritms better suit their needs. I've already presented these approaches here , so if you know nothing about recommender systems, you can read it there first. Collaborative Filtering Pros Recommends diverse items to users, being innovative; Good practical results (read Amazon's article ); It is widely used, and you can find several OpenSource  implementations of it ( Apache Mahout ); It can be used on ratings from users on items; It can deal with video and...

I already talked about Coursera's great courses here . There is a new course on Recommender Systems starting in September: https://www.coursera.org/course/recsys I don't know how it is going to be, but based on the courses I've done so far, it looks good.

Duine   is a open source Recommender System . It is a collection of software libraries developed by Telematica Instituut/Novay that intends to predict how interesting is an information to a user. It provides the collaborative filtering and content based recommender and other features, such as an Explanation API (explanations to why such recommendations has been made). Its result recommendations are quantified by a number, ranging from -1 to +1, being that the greater the result, the more interesting the item should be to the user. One of the main advantages of Duine is its well formed architecture. When it performs a recommendation, it can incorporate the user feedback to its systems. Also, it possess a switching engine, being able to analyse which method (content or collaborative) is better in the data situation, and dynamically change it. a . Architecture The following picture describes the main concept of Duine framework. b. Installation To ins...

Here follows a survey I did back in 2010 when I was studying Recommender Systems. Hope it is useful. The growth of web content and the expansion of e-commerce has deeply increased the interest on recommender systems. This fact has led to the development of some open source projects in the area. Among the recommender systems algorithms available in the web, we can distinguish the following:   Duine , Apache Mahout , OpenSlopeOne , Cofi , SUGGEST and Vogoo . All of these projects offers collaborative-filtering implementations, in different programming languages. The Duine Framework supplies also an hybrid implementation. It is a Java software that presents the content-based and collaborative filtering in a switching engine: it dynamically switches between each prediction given the current state of the data. For example if there aren't many ratings available, it uses the content-based approach, and switches to the collaborative when the scenario changes. ...

Hello, I have just posted an implementation of the SlopeOne Recommender System in Java. I designed it to work with Movielens Dataset (a bunch of ratings on movies). I hope that helps everybody that is starting to develop their own recommender system. https://github.com/renataghisloti/SlopeOne-with-Movielens-Dataset

Seeing that Recommender Systems has drawn a lot of attention in this past year, I would like to recommend further reading to those who want to obtain greater knowledge in the subject. I will indicate some articles that have helped me study the matter: G. Adomavicius and A. Tuzhilin Towards the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. 2001 This article written by Adomavicius introduces Recommender Systems very well. It explains the main three types of these systems (Content-Based, Collaborative Filtering and Hybrid Recommendation). I also gives a formal mathematical definition of a Recommender Systems, which for some people can be great. I greatly recommend any other article you may find of Adomavicius. Laurent Candillier , Frank Meyer , Kris Jack, Françoise Fessant.A State-of-the-Art Recommender Systems. This paper also provides great overview of Recommender Systems and a very interesting comparison between...

Slope One is a simple and efficient type of recommender system. Introduced by Daniel Lemire and Anna Maclachlan in 2005, it involves a simpler idea than the majority of other collaborative filtering implementations. While these usually calculate the similarity between vectors of items using the cosine or the Pearson methods, the Slope One approach recommends items to users based on the average difference in preferences of items.  The main idea of the algorithm is to create a linear relation between items preferences such as the relation F(x) = x + b. The name "Slope One" cames from the fact that here the "x" is multiplied by "1". It basically calculates the difference between the ratings of items for each user (for every item the user has rated). Then, it creates and average difference (diff) for every pair of items. To make a prediction of the Item A for an User 1 for example, it would get the ratings that User 1 has given to other items and a...

“Scalable machine learning library” Mahout is a solid Java framework in the Artificial Intelligence area. It is a machine learning project by the Apache Software Foundation that tries to build intelligent algorithms that learn from some data input. What is special about Mahout is that  it is a scalable library, prepared to deal with huge datasets. Its algorithms are built on top of the Apache Hadoop project and, so, they work with distributed computing. Mahout offers algorithms in three major  areas: Clustering, Categorization and Recommender Systems. This lats part was incoporated in April 4 th 2008, from the previous Taste Recommender System project. Mahout currently implements  a collaborative filtering engine that supports the user-based, item-based and Slope-one recommender systems. Other algorithms available in the package are  the k-means, fuzzy k-Means clustering, Canopy, Dirichlet and Mean-Shift. They also have The Naive Bayes, Complement...

"If an user A has liked the movies "Matrix " and "The Lord of the Rings" and many other users that have liked these two movies also liked "Memento", then it is likely that "Memento" will be recommended to user A." Collaborative Filtering is a type of recommender system widely implemented, and it is known for giving more accurated predictions than other approaches. The basic idea of the algorithms in the collaborative filtering area is to provide recommendations based on what people with similar taste have liked in the past. These people, the neighbors, are selected by comparing the user's past preferences (usually presented as ratings on items). So, by measuring the ratings similarity its possible to recommend items liked by the neighborhood. There are two major techniques to compare ratings. User-Based Let us consider a user as an N-dimensional vector of ratings, where each cell represents the rating...

"Suggest new items that fit the user’s preference."   Introduction The increasing amount of information in the web has promoted the advance of the recommender systems research area.  These systems help users by offering useful suggestions to them . The aim of Recommender Systems is to provide personalized recommendations, representing a fundamental role on e-commerce (widely used by companies such as Amazon , Netflix and Google ). They highlight items that the users have not yet seen and may appreciate. Such items include books, restaurants, webpages or even lifestyles. A suggestion is usually made based on the user's historical preferences. These preferences may be collected implicitly or explicitly . When a user is buying an item, or entering a web-page, for example, he is giving an implicit preference feedback. In the case of a user giving a rating to an article, he is providing an explicit feedback. A substantial challenge in this ar...