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

• Drafting my first progress report

  Just managed to migrate all my blog sites to one centralized multi-site, so no more half-baked solution and hopefully this brings better plugin compatibility. I have not check with other related services (like Google Webmaster Tools) whether this cause any breakage though. Well, the main purpose of this blog post is actually a draft of what I did for the past two months for my postgraduate programme. Yea, I should have posted more stuff to this blog (just realized that my last post here is already like half a year ago).

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• Approximate Neighbour Search in Multiple Dimensions

  In the previous post, I re-implemented Annoy in 2D with some linear algebra maths. Then I spent some time going through some tutorial on vectors, and expanded the script to handle data in 3D and more. So instead of finding gradient, the perpendicular line in the middle of two points, I construct a plane, and find the distance between it and points to construct the tree.

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• Learning asyncio by redoing my messaging bot

  I used to develop a bot, partly for work, that fetches current latest petrol retail price in Malaysia. The bot was really an experiment, but at the time it worked well. Then a few years later, out of boredom, I revisited the project after finding the telegram bot library is moving towards asyncio. It was great (at least a lot of people rave about it), but also at the same time intimidating, I learned about coroutines and used gevent in the past, but not asyncio itself.

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• Building librdf-storage-virtuoso Package

  I wanted to try using virtuoso as the storage engine for Redland but unfortunately there is no librdf-storage-virtuoso package for Ubuntu. After getting some help from @dajobe, I attempted to build the packages myself. Although it takes quite some time to build packages, but not too difficult it seems.

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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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