Master's Thesis

I wrote my master’s thesis for Comordo Technologies during the spring of 2015 where I developed a first version of their recommendation system.

View the report at LiU or a direct link to the report. You can also view the source of the written report. Code produced during the thesis will not be published.

Here is the presentation I made using Prezi.


Recommendations are becoming more and more important in a world where there is an abundance of possible choices and e-commerce and content providers are featuring recommendations prominently. Recommendations based on explicit feedback, where user is giving feedback for example with ratings, has been a popular research subject. Implicit feedback recommender systems which passively collects information about the users is an area growing in interest. It makes it possible to generate recommendations based purely from a user’s interactions history without requiring any explicit input from the users, which is commercially useful for a wide area of businesses. This thesis builds a recommender system based on implicit feedback using the recommendation algorithms katz-eig and link-analysis and analyzes and implements strategies for learning optimized parameters for different datasets. The resulting system forms the foundation for Comordo Technologies’ commercial recommender system.