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Help needed on collaborative filtering

  Asked By: Adelina    Date: Nov 20    Category: Java    Views: 1785

I'm trying to start a project where I am in deep need of collaborative filtering.
(For those who are not familiar with the word, it is some recommendation system for users similar to what Amazon.com and Google News are using!!!)

Are there some standard platforms, frameworks or even workflows which I could start with?!
Really looking forward for your ideas.



5 Answers Found

Answer #1    Answered By: Joyce Edwards     Answered On: Nov 20

Fantastic subject, I guess you've noticed this link long before us, but I send it again, I found it a good step to realize the concept in practice.


Answer #2    Answered By: Adel Fischer     Answered On: Nov 20

Sure it was helpful, but I'm mostly trying to avoid things like Netflix and swim in Google News swim lane.
Of course I found some papers that discuss the Amazon recommendation  subject, but no one was precise.
Any idea in these matters is also appreciated. (Mostly on user clustering and co-visitation patterns).
Hereby I forward you a paper to let you know more about what I'm working on.

Answer #3    Answered By: Teresa Rogers     Answered On: Nov 20

I think these could help  you :
Cofi - The library is used as part of the RACOFI web site which is a live Music Recommender site.
CoFE - CoFE is short for "COllaborative filtering  Engine". CoFE was formerly known as CFEngine. CoFE will run as a server to generate recommendations for individual items, top-N recommendations over all items, or top-N recommendations limited to one item type. Recommendations are computed using a popular, well-tested nearest-neighbor algorithm (Pearson's algorithm). CoFE can be integrated with any system  that supports Java. User data is stored in MySQL.
Taste - Taste is a flexible, fast collaborative filtering engine for Java. Taste provides a rich set of components from which you can construct a customized recommender system from a selection of algorithms. Taste supports both memory-based and item-based recommender systems, slope one recommenders, and a couple other experimental implementations. It does not currently support model-based recommenders.
Alkini Meme - users  express their tastes by assigning ratings to products. To model users so that they can be grouped together, Alkindi represents them geometrically as vectors in a high-dimensional space. The coordinate axes of this space correspond to products; the coordinates of the point representing a user are that user’s ratings of those products. Alkindi partitions its existing user base into clusters using “K-means”, a statistical algorithm that maximizes the geometric tightness of the clusters. Alkindi has developed a novel metric that smoothly integrates all available data. This helps alleviate the sparse data problem.
RACOFI - RACOFI (Rule-Applying Collaborative Filtering) ia multidimensional rating system. It has been used where users rate contemporary music in the five dimensions of impression, lyrics, music, originality, and production. The collaborative filtering algorithms STI Pearson, STIN2, and the Per Item Average algorithms are employed together with RuleML-based rules to recommend music objects that best match user queries. The music rating system has been on-line since August 2003 at http://racofi.elg.ca.
iRate - iRATE radio is a collaborative filtering system for music. You rate the tracks it downloads and the server uses your ratings and other people's to guess what you'll like.
SWAMI - SWAMI is a framework for running collaborative filtering algorithms and evaluating the effectiveness of those algorithms. It uses the EachMovie dataset, generously provided by Compaq.
Apache Lucene Mahout - The Apache Lucene project  is pleased to announce the release of Apache Mahout 0.1. Apache Mahout is a subproject of Apache Lucene with the goal of delivering scalable machine learning algorithm implementations under the Apache license. The first public release includes implementations for clustering, classification, collaborative filtering and evolutionary programming.

Answer #4    Answered By: Tammy Sanders     Answered On: Nov 20

Great help.
I'll read them all, and maybe I am going to ask you more questions
Good to see you here.

Answer #5    Answered By: Mj Roxas     Answered On: Jan 25

I'm going to start with this subject too, and those papers will be awesome to get more ideas.
I was reading about it, and many patents are already registered by Google.
I would like to know your opinion, as you have more knowledge in this area.
Do you think there is an interesting future for Collaborative Filtering? Does Google will improve its filtering systems?

Thank you !

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