Hyperml and deep interest network to build a recommender system for modatta: targeting customers for campaign offers in a two-sided market

Han, QiweiMacedo, Patrícia Alexandra Cravo2022-06-172022-06-172022-01-212021-12-16http://hdl.handle.net/10362/140149This project studies two Deep Learning approaches, aiming to learn representations using embeddings, as well as get more insights about users, by deploying a Recommender System. After wards, it will allow Modatta to provide users with personalized offers based on their interests. Choosing the right users is critical for the success of a campaign offer. Therefore, it’s necessary to identify a user-base making sure that ,not only marketers will target their offer for those that are going to accept the campaign, but also users will get the offers they need and desire.engMachine learningDeep learningRecommender systemsHyperbolic embeddingsData monetizationCustomer targetingPersonalized offersBusiness analysisHyperml and deep interest network to build a recommender system for modatta: targeting customers for campaign offers in a two-sided marketmaster thesis202972097