Recommender System

• Cold-start and Ramp-Up problems

  Everyone knows folksonomy is (or was) cool and useful, however, when it is applied in real life, then problem arises. The idea of blogging this came while I am struggling to get my literature review report done (been doing it for months, I am being so ridiculous, I know). As a matter of fact, as I am dying to get it done, there are a couple of things that I found to be blog-worthy. So, I will be publishing a couple of brief overview to some of the topics involved in the coming days in a really casual (read: lazy, and full of personal speculations) way to this very humble little blog of mine.

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• Fuzzy C-Means and Content Recommendation

  This post is purely based on my own speculation as there’s no experiment on real-life data to actually back the arguments. I am currently trying to document down a plan for my experiment(s) on recommender system (this reminds me that I have not release the Flickr data collection tool :/) and my supervisor advised to write a paragraph or two on some of the key things. Since he is not going to read it, so I might as well just post it here as a note.

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• Recommender System Classification

  Had a discussion with my secondary supervisor and it turned out pretty bad because I wasn’t fully prepared and he was rushing to somewhere else for a meeting. So I am jotting down a brief summary (read: highly based on personal/subjective feelings/opinions) of my readings here to help organize things before the followup meeting that is taking place next week.

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Random Posts

• Evil evil update

  This update took me quite a bit more time than I initially expected. Anyway, I have done some refactoring work to the original code, and thought it would be nice to document the changes. Overall, most of the changes involved the refactoring of function names. I am not sure if this would stick, but I am quite satisfied for now.

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• Understanding potree hierarchy file

  One of my recent tasks involving crawling a lot of geo-tagged data from a given service. The most recent one is crawling files containing a point cloud for a given location. So I began by observing the behavior in the browser. After exporting the list of HTTP requests involved in loading the application, I noticed there are a lot of requests fetching resources with a common rXXX pattern.

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• Processing JSON with dask.bag

  Often times, I am dealing with JSONL files, though panda’s DataFrame is great (and blaze to certain extend), however it is offering too much for the job. Most of the received data is in the form of structured text and I do all sorts of work with them. For example checking for consistency, doing replace based on values of other columns, stripping whitespace etc.

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• Hitting a nail with the wrong hammer (my writing tools)

  I just failed a programming assessment test miserably yesterday and thought I should at least document it down. However, the problem with this is that the questions are copyrighted, so I guess I would write it from another point of view. So the main reason I failed was because I chose the wrong strategy to the problem, thinking it should be solution but as I put in time to that I ended up creating more problems.

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• Adding a new settings applet for my daemon

  This is basically a small incremental update to my script published here. For some reason, the previous version of the script didn’t really work, so this release should fix the problem. Besides fixing the problem where the daemon did not actually launched at start up, I have added a settings applet for this script as well.

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