Recommendation systems become more and more necessary due to the growing supply chain. Therefore, scientists are developing models that can serve different recommendation needs faster than before, and it is getting more complicated to choose the model for a specific case. In this thesis, there are three neural collaborative filtering methods compared regarding dataset fit. This research shows that there is no one-fits-all method. There is much space for improvement in all the areas: dataset selection and aggregation, method development and operation, and selective approaches for the analysis of the results. In the thesis, three contrasting datasets are chosen (Chess, Library, and LastFM), and three novel approaches are tested: recently released Dynamic Graph Collaborative Filtering (DGCF) and Dynamic Embeddings for Interaction Prediction (DeePRed) are compared to the Joint Dynamic User- Item Embeddings (JODIE) as the baseline. Results show DeePRed being a state-of-the-art model that outperforms other methods. It runs an epoch for a small dataset in less than a minute, shows great prediction accuracy in an average of 98% for small datasets. However, DGCF does not show accuracy improvement over JODIE but is significantly faster for an extensive dataset. / Rekommendationssystem blir mer och mer nödvändiga på grund av den växande försörjningskedjan. Därför utvecklar forskare modeller som kan tjäna olika rekommendationsbehov snabbare än tidigare och det blir mer och mer komplicerat att välja modell för ett specifikt fall. I denna avhandling finns det tre neurologiska samarbetsfiltreringsmetoder som jämförs avseende deras gran för olika datamängder. Denna forskning visar att det inte finns någon metod som passar alla och det finns mycket utrymme för förbättring inom alla områden: datasatsval och aggregering, metodutveckling och drift och selektiva metoder för analys av resultaten. I avhandlingen väljs tre kontrasterande datamängder (Chess, Library och LastFM) och tre nya metoder testas: nyligen släppt Dynamic Graph Collaborativefiltering (DGCF) och Dynamic Embedding for Interaction Prediction (DeePRed) jämförs med Joint Dynamic User-Item. Inbäddning (JODIE) som baslinje. Resultaten visar att (DeePRed) är en avancerad modell som överträffar andra metoder som snabba genom att köra en epok för liten dataset på mindre än en minut, vilket visar stor förutsägelsesnoggrannhet i genomsnitt 98% för små datamängder. Men (DGCF) visar inte förbättring av noggrannhet jämfört med (JODIE), men är betydligt snabbare för en stor dataset.

The abundance of information available on the internet necessitates means of quickly finding what is relevant for the individual user. To this end, there has been much research concerning recommender systems and lately specifically methods using deep learning for such systems. This work proposes a Multi-Armed Bandit as a recommender for suggested sites on a browser start page. The system is compared to a pre-existing baseline and does not manage to outperform it in the setting used in controlled experiments. A Neural Collaborative Filtering system is then constructed using a stacked autoencoder and is used to produce user preference vectors that are inserted in the bandit in the hope of improving its performance. Analysis indicates that the bandit solution works better as the number of items grows. The user-informed initialisation used in this work shows a trend of improving over a randomly-initialised bandit, but results are inconclusive. This work also contributes an analysis of the problem domain including which factors impact the performance on the model training for preference vectors, and the performance of the bandit algorithms.

Modelos baseados em fatoração de matrizes estão entre as implementações mais bem sucedidas de Sistemas de Recomendação. Neste projeto, estudamos as possibilidades de incorporação de informações provindas de redes sociais, para melhorar a qualidade das predições do modelo tanto em modelos tradicionais de Filtragem Colaborativa, quanto em Filtragem Colaborativa Neural. / Models based on matrix factorization are among the most successful implementations of Recommender Systems. In this project, we study the possibilities of incorporating the information from social networks to improve the quality of predictions of the model both in traditional Collaborative Filtering and in Neural Collaborative Filtering.