The effects of implementing domain knowledge in a recommender system

Abstract: This thesis presents a domain knowledge based similarity measure for recommender systems, using Systembolaget's open API with product information as input data. The project includes the development of the similarity measure, implementing it in a content based recommender engine as well as evaluating the model and comparing it to an existing model which uses a bag-of-words based approach. The developed similarity measure uses domain knowledge to calculate the similarity of three feature, grapes, wine regions and production year, to attempt to improve the quality of recommendations. The result shows that the bag-of-words based model performs slightly better than the domain knowledge based model, in terms of coverage, diversity and correctness. However, the results are not conclusive enough to discourage from more investigation into using domain knowledge in recommender systems.