Product Matching Using Image Similarity

PriceRunner is an online shopping comparison company. To maintain up-todate prices, PriceRunner has to process large amounts of data every day. The processing of the data includes matching unknown products, referred to as offers, to known products. Offer data includes information about the product such as: title, description, price and often one image of the product. PriceRunner has previously implemented a textual-based machine learning (ML) model, but is also looking for new approaches to complement the current product matching system. The objective of this master’s thesis is to investigate the potential of using an image-based ML model for product matching. Our method uses a similarity learning approach where the network learns to recognise the similarity between images. To achieve this, a siamese neural network was trained with the triplet loss function. The network is trained to map similar images closer together and dissimilar images further apart in a vector space. This approach is often used for face recognition, where there is an extensive amount of classes and a limited amount of images per class, and new classes are frequently added. This is also the case for the image data used in this thesis project. A general model was trained on images from the Clothing and Accessories hierarchy, one of the 16 toplevel hierarchies at PriceRunner, consisting of 17 product categories. The results varied between each product category. Some categories proved to be less suitable for image-based classification while others excelled. The model handles new classes relatively well without any, or with briefer, retraining. It was concluded that there is potential in using images to complement the current product matching system at PriceRunner

Product Matching Using Image Similarity

PriceRunner is an online shopping comparison company. To maintain up-todate prices, PriceRunner has to process large amounts of data every day. The processing of the data includes matching unknown products, referred to as offers, to known products. Offer data includes information about the product such as: title, description, price and often one image of the product. PriceRunner has previously implemented a textual-based machine learning (ML) model, but is also looking for new approaches to complement the current product matching system. The objective of this master’s thesis is to investigate the potential of using an image-based ML model for product matching. Our method uses a similarity learning approach where the network learns to recognise the similarity between images. To achieve this, a siamese neural network was trained with the triplet loss function. The network is trained to map similar images closer together and dissimilar images further apart in a vector space. This approach is often used for face recognition, where there is an extensive amount of classes and a limited amount of images per class, and new classes are frequently added. This is also the case for the image data used in this thesis project. A general model was trained on images from the Clothing and Accessories hierarchy, one of the 16 toplevel hierarchies at PriceRunner, consisting of 17 product categories. The results varied between each product category. Some categories proved to be less suitable for image-based classification while others excelled. The model handles new classes relatively well without any, or with briefer, retraining. It was concluded that there is potential in using images to complement the current product matching system at PriceRunner

Uppsala Parkering

Heavy traffic in cities is an increasing problem that causes congestion. An inadequate infrastructure unable to scale to satisfy the growing amount of traffic is the main cause. Traffic congestion results in ineffective overhead traffic patterns. The overhead time spent in traffic is consuming valuable time from drivers and it also contributes to a heavier impact on the environment. A relevant portion of the traffic in cities, is search traffic: traffic with the sole intent of finding an available parking spot. Reducing the search traffic in cities would effectively hamper congestion. In a collaboration with Uppsala Parkering AB, a project was commenced with the aim of developing a solution that could contribute to reduce the search traffic in Uppsala. This aim was accomplished by the creation of a mobile application. The applications main feature is an interactive map that visualizes parking areas within a confined test area in Uppsala. The application is scalable and can be extended to cover a larger area. With the application, a user can find the closest suitable parking spot depending on custom preferences. The project did not have access to actual traffic data with real-time information of currently parked vehicles in Uppsala. To mimic the real-time data, a simulation was created and used in the application. The simulated data was used to demonstrate the varying availability of free parking spots in the different parking areas. By scaling up the application with additional parking areas and replacing the simulated data with actual traffic data, the application can be used as an aid to find suitable parking spots. Then the search traffic in Uppsala could potentially be decreased.Hög trafikbelastning i storstäder är ett ökande problem som resulterar i ineffektiva trafikmönster. Tid spenderad i köer är överflödig tid som direkt minskar trafikanters dagliga effektivitet. Längre tid i trafiken bidrar dessutom till en högre miljöpåverkan.En relativt stor andel av trafik i storstäder är söktrafik: trafik med mål att hitta en tillgänglig parkeringsplats. Att reducera söktrafiken i städer skulle även effektivt minska den totala trafiken.I samarbete med Uppsala Parkerings AB inleddes ett projekt med mål att utveckla en lösning med syfte att reducera söktrafiken i Uppsala.Detta uppnåddes genom att skapa en mobilapplikation vars huvudfunktionalitet är en interaktiv karta där användarna kan se parkeringsområden inom ett testområde i Uppsala. Applikationen är fullt skalbar och kan utökas för att täcka ett större område. I applikationen kan användare ta reda var närmaste parkeringsplats finns och kan anpassa kartan till att filtrera parkeringsområden efter egna preferenser. Projektet saknade tillgång till trafikdata med realtidsinformation om parkerade fordon i Uppsala. En simulation skapades för att imitera den saknade realtidsdatan. Den simulerade realtidsdatan användes för att demonstrera den varierande tillgängligheten av lediga platser i olika parkeringsområden.Genom att skala upp applikationen med ytterligare parkeringsområden och ersätta den simulerade datan med riktig trafikdata, kan den skapade applikationen användas som ett hjälpmedel för att hitta lämpliga parkeringsplatser. Då kan applikationen användas för att potentiellt minska söktrafiken i Uppsala

Uppsala Parkering

Heavy traffic in cities is an increasing problem that causes congestion. An inadequate infrastructure unable to scale to satisfy the growing amount of traffic is the main cause. Traffic congestion results in ineffective overhead traffic patterns. The overhead time spent in traffic is consuming valuable time from drivers and it also contributes to a heavier impact on the environment. A relevant portion of the traffic in cities, is search traffic: traffic with the sole intent of finding an available parking spot. Reducing the search traffic in cities would effectively hamper congestion. In a collaboration with Uppsala Parkering AB, a project was commenced with the aim of developing a solution that could contribute to reduce the search traffic in Uppsala. This aim was accomplished by the creation of a mobile application. The applications main feature is an interactive map that visualizes parking areas within a confined test area in Uppsala. The application is scalable and can be extended to cover a larger area. With the application, a user can find the closest suitable parking spot depending on custom preferences. The project did not have access to actual traffic data with real-time information of currently parked vehicles in Uppsala. To mimic the real-time data, a simulation was created and used in the application. The simulated data was used to demonstrate the varying availability of free parking spots in the different parking areas. By scaling up the application with additional parking areas and replacing the simulated data with actual traffic data, the application can be used as an aid to find suitable parking spots. Then the search traffic in Uppsala could potentially be decreased.Hög trafikbelastning i storstäder är ett ökande problem som resulterar i ineffektiva trafikmönster. Tid spenderad i köer är överflödig tid som direkt minskar trafikanters dagliga effektivitet. Längre tid i trafiken bidrar dessutom till en högre miljöpåverkan.En relativt stor andel av trafik i storstäder är söktrafik: trafik med mål att hitta en tillgänglig parkeringsplats. Att reducera söktrafiken i städer skulle även effektivt minska den totala trafiken.I samarbete med Uppsala Parkerings AB inleddes ett projekt med mål att utveckla en lösning med syfte att reducera söktrafiken i Uppsala.Detta uppnåddes genom att skapa en mobilapplikation vars huvudfunktionalitet är en interaktiv karta där användarna kan se parkeringsområden inom ett testområde i Uppsala. Applikationen är fullt skalbar och kan utökas för att täcka ett större område. I applikationen kan användare ta reda var närmaste parkeringsplats finns och kan anpassa kartan till att filtrera parkeringsområden efter egna preferenser. Projektet saknade tillgång till trafikdata med realtidsinformation om parkerade fordon i Uppsala. En simulation skapades för att imitera den saknade realtidsdatan. Den simulerade realtidsdatan användes för att demonstrera den varierande tillgängligheten av lediga platser i olika parkeringsområden.Genom att skala upp applikationen med ytterligare parkeringsområden och ersätta den simulerade datan med riktig trafikdata, kan den skapade applikationen användas som ett hjälpmedel för att hitta lämpliga parkeringsplatser. Då kan applikationen användas för att potentiellt minska söktrafiken i Uppsala

Uppsala Parkering

Heavy traffic in cities is an increasing problem that causes congestion. An inadequate infrastructure unable to scale to satisfy the growing amount of traffic is the main cause. Traffic congestion results in ineffective overhead traffic patterns. The overhead time spent in traffic is consuming valuable time from drivers and it also contributes to a heavier impact on the environment. A relevant portion of the traffic in cities, is search traffic: traffic with the sole intent of finding an available parking spot. Reducing the search traffic in cities would effectively hamper congestion. In a collaboration with Uppsala Parkering AB, a project was commenced with the aim of developing a solution that could contribute to reduce the search traffic in Uppsala. This aim was accomplished by the creation of a mobile application. The applications main feature is an interactive map that visualizes parking areas within a confined test area in Uppsala. The application is scalable and can be extended to cover a larger area. With the application, a user can find the closest suitable parking spot depending on custom preferences. The project did not have access to actual traffic data with real-time information of currently parked vehicles in Uppsala. To mimic the real-time data, a simulation was created and used in the application. The simulated data was used to demonstrate the varying availability of free parking spots in the different parking areas. By scaling up the application with additional parking areas and replacing the simulated data with actual traffic data, the application can be used as an aid to find suitable parking spots. Then the search traffic in Uppsala could potentially be decreased.Hög trafikbelastning i storstäder är ett ökande problem som resulterar i ineffektiva trafikmönster. Tid spenderad i köer är överflödig tid som direkt minskar trafikanters dagliga effektivitet. Längre tid i trafiken bidrar dessutom till en högre miljöpåverkan.En relativt stor andel av trafik i storstäder är söktrafik: trafik med mål att hitta en tillgänglig parkeringsplats. Att reducera söktrafiken i städer skulle även effektivt minska den totala trafiken.I samarbete med Uppsala Parkerings AB inleddes ett projekt med mål att utveckla en lösning med syfte att reducera söktrafiken i Uppsala.Detta uppnåddes genom att skapa en mobilapplikation vars huvudfunktionalitet är en interaktiv karta där användarna kan se parkeringsområden inom ett testområde i Uppsala. Applikationen är fullt skalbar och kan utökas för att täcka ett större område. I applikationen kan användare ta reda var närmaste parkeringsplats finns och kan anpassa kartan till att filtrera parkeringsområden efter egna preferenser. Projektet saknade tillgång till trafikdata med realtidsinformation om parkerade fordon i Uppsala. En simulation skapades för att imitera den saknade realtidsdatan. Den simulerade realtidsdatan användes för att demonstrera den varierande tillgängligheten av lediga platser i olika parkeringsområden.Genom att skala upp applikationen med ytterligare parkeringsområden och ersätta den simulerade datan med riktig trafikdata, kan den skapade applikationen användas som ett hjälpmedel för att hitta lämpliga parkeringsplatser. Då kan applikationen användas för att potentiellt minska söktrafiken i Uppsala