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Slope One. Slope One is a family of algorithms used for collaborative filtering, introduced in a 2005 paper by Daniel Lemire and Anna Maclachlan.[1] Arguably, it is the simplest form of non-trivial item-based collaborative filtering based on ratings. Their simplicity makes it especially easy to implement them efficiently while their accuracy is often on par with more complicated and computationally expensive algorithms.[1][2] They have also been used as building blocks to improve other algorithms.[3][4][5][6][7][8][9] They are part of major open-source libraries such as Apache Mahout and Easyrec. Item-based collaborative filtering of rated resources and overfitting[edit] When ratings of items are available, such as is the case when people are given the option of ratings resources (between 1 and 5, for example), collaborative filtering aims to predict the ratings of one individual based on his past ratings and on a (large) database of ratings contributed by other users. ). ). Example: [edit]

Deploying a massively scalable recommender system with Apache Mahout | “I for one welcome our new computer overlords” Introduction The purpose of this post is to explain how to use Apache Mahout to deploy a massively scalable, high throughput recommender system for a certain class of usecases. I’ll describe the shape of usecases covered and give a step-by-step guide to setting up such a recommender system.

Be aware that this is a guide intended for readers already familiar with Collaborative Filtering and recommender systems that are evaluating Mahout as a choice for building their production systems on. The focus is on making the right engineering decisions rather than on explaining algorithms here. If you only want to learn about recommendation mining and try out Mahout, having a look at Mahout in Action might be a more suitable starting point. Anatomy of covered usecases If your usecase matches these conditions, then the techniques described in this article will be a good basis for your recommender system: Which scenarios or business models do meet these conditions?

Some numbers Algorithmic approach. A Graph-Based Movie Recommender Engine « Marko A. Rodriguez. The MovieRatings Dataset The GroupLens research group has made available a corpus of movie ratings. There are 3 versions of this dataset: 100 thousand, 1 million, and 10 million ratings. This post makes use of the 1 million ratings version of the dataset. The dataset can be downloaded from the MovieRatings website (~6 megs in size).

Getting Started with Gremlin All of the code examples can be cut and pasted into the Gremlin console or into a Groovy/Java class within a larger application. Generating a MovieRatings Graph Before getting recommendations of which movies to watch, it is important to first parse the raw MovieLens data according to the graph schema defined above. The data will be inserted into the graph database Neo4j. Parsing Movie Data The file movie.dat contains a list of movies. The code to parse this data into Neo4j and according to the diagrammed schema is presented below. Parsing User Data The file users.dat contains a list of users. Parsing Ratings Data Conclusion Like this: Libro: Trust Networks for Recommender Systems - library.nu.

Libro: Recommender Systems: An Introduction - library.nu.