access_time September 05, 2019 at 02:00PM until September 05, 2019 at 04:00PM
place Anfiteatro PA-3 (Piso -1, Pavilhão de Matemática), IST, Alameda
Recommender Systems have as goal providing valuable recommendations to its users. Most research on Recommender Systems aims to improve the recommendation quality exclusively, overlooking the computational efficiency of such solutions. Although Recommender Systems, based on collaborative filtering, do not have many ratings available in this work by strategically removing redundant ratings is able to offer a similarity metric with an improved the computational efficiency, for Recommender Systems. This work focus on improving the computational efficiency of similarity metrics and enhance quality, using two different approaches. The first relies on Collaborative Filtering i.e. exclusively on ratings, to produce a computationally efficient similarity metric for Recommender Systems. The second approach uses contextual information regarding users and items to further improve recommendation quality, while still maintaining the same computational efficiency. The solutions here proposed can be readily deployed on real Recommender Systems. The first approach methodology intends to improve similarity metrics for Memory-based Collaborative Filtering using Fuzzy Models. Memory-based Collaborative Filtering relies heavily on similarity metrics to produce recommendations. Fuzzy Fingerprints are used as a framework to create a novel similarity metric, providing a fast and effective solution. The second approach also uses Fuzzy Fingerprints, and it combines contextual information with ratings into a single Fuzzy Fingerprint, or create a multi-context Fuzzy Fingerprint where each contextual information has its own Fuzzy Fingerprint. Each contextual Fuzzy Fingerprint allows a ranking fusion algorithm to produce similarity values. This work is validated using four well-known datasets which are ML-1M, HetRec 2011, Netflix and Jester. The application of the Fuzzy Fingerprint similarity improves recommendation quality but, more importantly, requires four times less computational resources than current solutions on large datasets. When using contextual information the recommendation quality improves either by combining contextual information and ratings into a single Fuzzy Fingerprint or by using multi-context Fuzzy Fingerprints. The solutions using contextual information achieve further recommendation quality improvements while maintaining a comparable computational efficiency in comparison well-known similarity metrics.
local_offer Doctoral exam
person Candidate: André Filipe Caldaça da Silva Carvalho
supervisor_account Advisor: Prof. Pável Pereira Calado / Prof. João Paulo Baptista de Carvalho