Developing a Recommender System for a Mobile E-commerce Application

This thesis describes the process of conceptualizing and developing a recommendersystem for a peer-to-peer commerce application. The application in question is calledPlick and is a vintage clothes marketplace where private persons and smaller vintageretailers buy and sell secondhand clothes from each other. Recommender systems is arelatively young field of research but has become more popular in recent years withthe advent of big data applications such as Netflix and Amazon. Examples ofrecommender systems being used in e-marketplace applications are however stillsparse and the main contribution of this thesis is insight into this sub-problem inrecommender system research. The three main families of recommender algorithmsare analyzed and two of them are deemed unfitting for the e-marketplace scenario.Out of the third family, collaborative filtering, three algorithms are described,implemented and tested on a large subset of data collected in Plick that consistsmainly of clicks made by users on items in the system. By using both traditional andnovel evaluation techniques it is further shown that a user-based collaborative filteringalgorithm yields the most accurate recommendations when compared to actual userbehavior. This represents a divergence from recommender systems commonly usedin e-commerce applications. The paper concludes with a discussion on the cause andsignificance of this difference and the impact of certain data-preprocessing techniqueson the results