Exploiting user preference similarity transitivity in nearest neighbour recommender algorithms
This thesis explores the Nearest Neighbour recommender algorithm and a proposed way of recomputing the elements of a set, containing values from a similarity metric. In essence, this extends the neighbourhood used in the algorithm by making use of close neighbours’ neighbours. This proposal is motivated by the hypothesis that high user preference similarity is transitive. A programme was implement