Abstract in English:
With the interactive recommending approach we have re- cently proposed, users are given more control over model- based Collaborative Filtering while the results are perceived as more transparent. Integrating the latent factors derived by Matrix Factorization with tags users provided for the items has, however, even more advantages. In this paper, we show how general understanding of the abstract factor space, and of user and item positions inside it, can benet from the semantics introduced by considering additional in- formation. Moreover, our approach allows us to explain the user's (former latent) preference prole by means of tags.