A Cold Start Recommendation System Using Item Correlation and User Similarity

A Cold Start Recommendation System Using Item Correlation and User Similarity

Abstract— Conventional recommendation systems tend to focus on variations of well-known information retrieval techniques. We took a fresh approach, rather than to follow the traditional, commonly applied recommendation methodology of creating a user-item matrix, and then using them to make recommendations. Instead, we established and examined three types of relationships: user-user similarity, wine-wine similarity and user preference relationships, in the form of adjacency lists. Using this approach, we did not encounter the usual problems associated with large dimension matrices, such as sparsity [5] and synonymy, as well as the basic problems of storing the large matrix, and having to perform a large number of computations every single time. We attempted to address the synonymy problem by using the wine-wine similarity index we formed. Also, we developed a model that took care of the cold start[6] problem which is fairly common in recommendation systems. We attempted to address the grey sheep problem as well, by minimizing the effect of any one outlying element, and taking the overall cumulative effect of all the elements. < Final Year Projects 2016 >.