Package es.uam.eps.ir.relison.links.recommendation.algorithms.standalone.foaf

Classes and definitions of algorithms based on friends of friends link prediction approaches.
  • Class Summary 
    Class Description
    AdamicAdar<U>
    Recommender that uses the Adamic-Adar coefficient of the neighbours.
    Cosine<U>
    Recommender using the cosine similarity to produce recommendations.
    Dist2Popularity<U>
    Recommender that sorts users at distance 2 by popularity.
    HubDepressedIndex<U>
    Recommender that uses the hub depressed index of the neighbors: given the number of common neighbors between two users, the recommendation score is divided by the size of either the target user or the candidate user: the user with a larger number of them.
    HubPromotedIndex<U>
    Recommender that uses the hub depressed index of the neighbors: given the number of common neighbors between two users, the recommendation score is divided by the size of either the target user or the candidate user: the user with a smaller number of them.
    Jaccard<U>
    Recommended based on the Jaccard similarity.
    LocalLHNIndex<U>
    Recommender that uses the local Leicht-Holme-Newman index.
    MostCommonNeighbors<U>
    Recommended that sorts candidate users according to the number of neighbors in common with the target one.
    NonReciprocalPreferentialAttachment<U>
    Non Reciprocal Preferential Attachment recommender.
    PreferentialAttachment<U>
    Recommender based on the preferential attachment phenomena.
    ReciprocalLinks<U>
    Recommends reciprocal links.
    ResourceAllocation<U>
    Recommender that uses the resource allocation principle to recommend.
    Sorensen<U>
    Recommender based on Sorensen similarity.
    TotalNeighbors<U>
    Recommends people according to the total number of neighbors between the two users.