A context aware web recommendation system
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Infuse is the implementation of a context-aware web recommendation system. It is decoupled into two parts: a firefox plugin which retrieve the browsing data and a server side script which expose an API and provide tools to extract data, analyse them, cluster them and provide recommendations.

This is the server side.


A web API is intended to record users interaction with their browser. Users need to be authentified to do so. This API only exposes ways to record data, not to read it. Internally, the data is stored into a mongodb database.

User management

Because users need to authenticate to send data through the API, there is a need for user management. Users are abble to:

  • create an account
  • delete an account (along with all their data)
  • access the recorded data about them
  • modify their credentials

Data extraction

Once the data have been recorded into the mongodb database, there is a need to extract this information. This means:

  • Gather the text of the HTML resources
  • Extract text metrics from texts, idealy subjects / tag of words
  • Transform opening / closing information to viewing sequences.

Those steps are done in the following scripts:

  • Converting the events that fired up on the browser is done in the infuse.convert module. Views are created from Events. It is possible to run the conversion by doing python infuse/convert.py
  • Gathering the text of the HTML resources + extracting metrics is done in the infuse.download module. It uses a python/java bridge to use a java tool able to transform HTML content into text content. It is possible to run the download of resources by doing python infuse/download.py N where N is the number of threads you want to run.

Profile extraction

Extract profiles from the different information gathered at this point: text subjects, browsing trees, geolocalisation. For each user, determine the different possible profiles, using clustering techniques.

One of the techniques used is to compute the TF/IDF (Term Frequency, Inverse Document Frequency matrix for each url) and to split the resources in groups.

Then, each user is attached to multiple profiles

What is defining an user profile ?

In order to extract different profiles from users, we can use, for each resource:

  • The period of the day the resource have been viewed
  • The location the view have been made
  • The topic of the visited webpage (using TF/IDF measures)?

Links ranking

Given a number of heuristics, rank the visited items. An interface provides a way for users to give explicit feedback. As users are not forced to give feedback, this step is not mendatory, but will be proposed to them.

It is possible to provide feedback by going through http://infuse.notmyidea.org/feedback/

When no feedback is given, it is directly infered from the viewing information, using simple heuristincs.

Ranking prediction

Uses collaborative filtering techniques to predict the rankings of unknown items in the profiles clusters.


Most of the dependencies can be installed automatically using the following command:

$ pip install -r requirements.txt

However, it will be needed to install manually mongodb (the server) and jpype (a python/java bridge). Similarly, you will need to install numpy before running pip install as scikits.learn depends on it.