We all know the possible consequences of global warming, rising temperatures, flooded cities and destroyed ecosystems. One of the causes is the emission of gases, predominantly CO2, which is increased by the growing E-commerce market. E-commerce companies rely on recommender systems to stimulate users to purchase products. We are convinced that we can use the core strength of recommender systems, influencing decision making, to steer users towards eco-friendly choices. Therefore, in this thesis, we research how greenness can be integrated into recommender systems. We present the first recommender system dataset that includes greenness, we benchmark several recommendation algorithms and we propose a strategy to increase recommendation greennness. To create the dataset, we annotate an existing recipe recommendation dataset with recipe greenness. For our benchmarking experiment, we propose metrics to measure recommendation greenness, which we use to show that no recommendation algorithm is fundamentally greener than others. Lastly, we propose a re-ranking method for improving the greenness of recommendation rankings. We use the method to explore the trade-off between accuracy and greenness and we show that it is possible improve the greenness of recommender systems significantly with little loss of accuracy.

The department of Urbanism at the TU Delft, our clients, research the sentiment in different places, times, ages or genders and compare them to each other. This report describes the purpose, design, implementation and accuracy of a web tool created to get insights into the sentiment people have, de- ducted from social media. The aim of our project was to make research easy and extracting innovative insights from social media.

In our tool, we analysed tweets and their location to collect information about the sentiment different people have towards places. The implementation of our tool consisted of five main components: (1) Twitter-Kafka, processing the tweets from the tweet data stream to our database, (2) face recognition, used for determining whether a tweet comes from a person instead of a company or organisation and for age and gender inference, (3) sentiment analysis, using machine learning to determine whether a tweet is neutral, negative or positive, (4) REST API, for the connection between the front-end and the back-end and (5) the user interface, in the form of an interactive dashboard.

At the beginning of the project, we set up a pipeline that checks the code on multiple things. The testing of the back-end is based on a Python unit test suit. For the build to succeed, all tests must pass and the total branch coverage must be at least 80%. We used Flake8 and ESlint in our build to ensure code quality at all times.

All of the above-mentioned components are up and running. The clients are now able to research the sentiments of people towards places.