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thesis.ljungblad.nu

thesis.ljungblad.nu

Since January 12, 2012 I’ve been slowly working on my master thesis called. Online recommendations at web-scale using matrix factorisation. Today I successfully defended it and can happily say I’m satisified with the results. What decisions led to progress and when did they not? Essentially it has been a tool for personal reflection on my learning process. After I have let the last few weeks sink in a bit, I will try to do a summary on my personal blog. So long and thanks for all the fish! 21 Jun 2012 in.

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thesis.ljungblad.nu | thesis.ljungblad.nu Reviews
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Since January 12, 2012 I’ve been slowly working on my master thesis called. Online recommendations at web-scale using matrix factorisation. Today I successfully defended it and can happily say I’m satisified with the results. What decisions led to progress and when did they not? Essentially it has been a tool for personal reflection on my learning process. After I have let the last few weeks sink in a bit, I will try to do a summary on my personal blog. So long and thanks for all the fish! 21 Jun 2012 in.
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1 thesis log
2 online recommendations
3 abstract
4 the presentation
5 view more presentations
6 from marcus ljungblad
7 older posts
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thesis.ljungblad.nu | thesis.ljungblad.nu Reviews

https://thesis.ljungblad.nu

Since January 12, 2012 I’ve been slowly working on my master thesis called. Online recommendations at web-scale using matrix factorisation. Today I successfully defended it and can happily say I’m satisified with the results. What decisions led to progress and when did they not? Essentially it has been a tool for personal reflection on my learning process. After I have let the last few weeks sink in a bit, I will try to do a summary on my personal blog. So long and thanks for all the fish! 21 Jun 2012 in.

INTERNAL PAGES

thesis.ljungblad.nu thesis.ljungblad.nu
1

Balancing the cluster

http://thesis.ljungblad.nu/2012/05/04/node_join

Yesterday I explained briefly what happens when the recommendation cluster is bootstrapped. Today I thought I would go into a little bit more detail, explaining how a new node figures out what to prioritise. First node to join. Read the file with itemsets’s signatures and assign internal id’s to each. This id is only used internally by the recommendation cluster. Load as many itemsets to memory as the node can handle (1 itemset = 1 worker) – this can either be manually restricted or determined from...

2

Weaving the fabric

http://thesis.ljungblad.nu/2012/05/12/weaving_the_fabric

After the last days dispair fighting with packaging jar files and configuration management in order to deploy the system on a set of servers, I’ve yesterday worked with something delightful: Fabric. I posed this question to a hacker-friend of mine: what is the easiest way to deploy and run a set of configuration commands on five machines. The only requirement? It shouldn’t take me forever to set-up. He immediately posted a snippet of python code using the Fabric library. And can be run by issuing. Btw, i...

3

How to evaluate a recommendation system?

http://thesis.ljungblad.nu/2012/04/24/background_on_evaluation

As I’m starting to look into the evaluation of the system I was curious to find related work in this area. Linas suggested two papers which form the backbone of recommender system evaluation. Both of them are written by well-known researchers within the field. Herlocker, Jonathan L., Joseph A. Konstan, Loren G. Terveen, John, and T. Riedl. Evaluating Collaborative Filtering Recommender Systems. Transactions on Information Systems 22 (2004): 5 53. Or are they strictly interested in. Items recommended may ...

4

Towards distributed evaluation

http://thesis.ljungblad.nu/2012/05/03/towards_distributed_evaluation

Today is big progress day. One of the things that I’ve set out to evaluate is the scalability of the system. Specifically I want to investigate two things:. How the system behaves as the quantity of data increases, and. The throughput and latency of requests that it supports. Before I had used the MultiJVM plugin. For Akka to test with multiple JVMs locally. Today I’ve refactored the code and enabled it to run from a scriptable number of machines. Contact me on Twitter. And their tech blog. And is a doub...

5

Illustrating matrix factorisation

http://thesis.ljungblad.nu/2012/05/02/illustrating_matrix_factorisation

As part of the report I’m including a section on the intuition behind matrix factorisation. Since I’m not the biggest fan of maths (it’s fascinating when it works but basically I suck at it) I want a more illustrative example. Prefering code over mathematical equations I decided to include some pseudo-code. However, I’m not every happy with it. Too many abbreviations and complicated syntax. Nick suggested making fluffy functions out of the. Alternatively I could just stick to the original python code.

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thesis.ljungblad.nu

Since January 12, 2012 I’ve been slowly working on my master thesis called. Online recommendations at web-scale using matrix factorisation. Today I successfully defended it and can happily say I’m satisified with the results. What decisions led to progress and when did they not? Essentially it has been a tool for personal reflection on my learning process. After I have let the last few weeks sink in a bit, I will try to do a summary on my personal blog. So long and thanks for all the fish! 21 Jun 2012 in.

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