An Integrated Recommender System Using Semantic Web With Social Tagging System

Abstract

Social tagging systems (STSs) allow collaborative users to share and annotate many types of resources with descriptive and semantically meaningful information in freely chosen text labels. STS provides three recommendations such as tag, item and user recommendations. Existing recommendation algorithms transform the three dimensional space of user, resource, and tag into two dimensions using pair relations in order to apply existing techniques. However, users may have different interests for an item, and items may have multiple facets. To circumvent this, a new system that models three types of entities user, tag and item in a STS as a 3-order tensor is proposed. The sparsity is reduced using stemming and predictions are made by applying latent semantic indexing using randomized singular value decomposition (RSVD). The proposal provides all the three recommendations using semantic web and shows notable improvements in terms of effectiveness through indices such as recall, precision, time and space.