In this master thesis work we design and analyze a data driven suit jacket fit recommendation system which aim to guide shoppers in the process of assessing garment fit over the web. The system is divided into two stages. In the first stage we analyze labelled customer data, train supervised learning models as to be able to predict optimal suit jacket dimensions of unseen shoppers and determine appropriate models for each suit jacket dimension. In stage two the recommendation system uses the results from stage one and sorts a garment collection from best fit to least fit. The sorted collection is what the fit recommendation system is to return. In this thesis work we propose a particular design of stage two that aim to reduce the complexity of the system but at a cost of reduced quality of the results. The trade-offs are identified and weighed against each other. The results in stage one show that simple supervised learning models with linear regression functions suffice when the independent and dependent variables align at particular landmarks on the body. If style preferences are also to be incorporated into the supervised learning models, non-linear regression functions should be considered as to account for increased complexity. The results in stage two show that the complexity of the recommendation system can be made independent from the complexity of how fit is assessed. And as technology is enabling for more advanced ways of assessing garment fit, such as 3D body scanning techniques, the proposed design of reducing the complexity of the recommendation system enables for highly complex techniques to be utilized without affecting the responsiveness of the system in run-time. / I detta masterexamensarbete designar och analyserar vi ett datadrivet rekommendationssystem för kavajer med mål att vägleda nät-handlare i deras process i att bedöma passform över internet. Systemet är uppdelat i två steg. I det första steget analyserar vi märkt data och tränar modeller i att lära sig att framställa prognoser av optimala kavajmått för shoppare som inte systemet har tidigare exponeras för. I steg två tar rekommendationssystemet resultatet ifrån steg ett och sorterar plaggkollektionen från bästa till sämsta passform. Den sorterade kollektionen är vad systemet är tänkt att retunera. I detta arbete föreslåar vi en specifik utformning gällande steg två med mål att reducera komplexiteten av systemet men till en kostnad i noggrannhet vad det gäller resultat. För- och nackdelar identifieras och vägs mot varandra. Resultatet i steg två visar att enkla modeller med linjära regressionsfunktioner räcker när de obereoende och beroende variabler sammanfaller på specifika punkter på kroppen. Om stil-preferenser också vill inkorpereras i dessa modeller bör icke-linjära regressionsfunktioner betraktas för att redogöra för den ökade komplexitet som medföljer. Resultaten i steg två visar att komplexiteten av rekommendationssystemet kan göras obereoende av komplexiteten för hur passform bedöms. Och då teknologin möjliggör för allt mer avancerade sätt att bedöma passform, såsom 3D-scannings tekniker, kan mer komplexa tekniker utnyttjas utan att påverka responstiden för systemet under körtid.

<p>Internet is one of the major achievements of 21 century by human kind. Retailers have moved their business towards a global market through internet. Human computer interaction is getting enhanced in different contexts. Consumers can now access the global markets online for the sake of shopping. Fashion industry is getting tremendous popularity in an online environment. Fashion is transformed into digital fashion where people from all over the world have easy access to the world of fashion and can interact and get hands on every piece of art. They can simply buy any fashion product anywhere in the world. The most important part in this domain is the interaction of the consumer with the media for the sake of spending money for goods having a fashion designer at the back end. So here the interaction needs to be precise and specific, especially when it comes to the sizing phase of the activity called shopping garments online. This paper investigates the current sizing trends offered by online fashion retailers and proposes the preferred state interface in order to discover the facts that enables consumers to finalize perfect size for them. The focus is on female users. The results will lead to understanding of the cause of confusion that consumer faces in selecting right size while shopping garments online.</p>

Internet is one of the major achievements of 21 century by human kind. Retailers have moved their business towards a global market through internet. Human computer interaction is getting enhanced in different contexts. Consumers can now access the global markets online for the sake of shopping. Fashion industry is getting tremendous popularity in an online environment. Fashion is transformed into digital fashion where people from all over the world have easy access to the world of fashion and can interact and get hands on every piece of art. They can simply buy any fashion product anywhere in the world. The most important part in this domain is the interaction of the consumer with the media for the sake of spending money for goods having a fashion designer at the back end. So here the interaction needs to be precise and specific, especially when it comes to the sizing phase of the activity called shopping garments online. This paper investigates the current sizing trends offered by online fashion retailers and proposes the preferred state interface in order to discover the facts that enables consumers to finalize perfect size for them. The focus is on female users. The results will lead to understanding of the cause of confusion that consumer faces in selecting right size while shopping garments online.