![]() In: Proceedings of 14th Conference Uncertainty in Artificial Intelligence (UAI 1998), pp. 3, 993–1022 (2003)īreese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the International Biometrics Society Annual Meeting, pp. 33–40 (2009)Īiroldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P., Jaakkola, T.: Mixed membership stochastic block models for relational data with application to protein-protein interactions. In: Advances in Neural Information Processing Systems, pp. KeywordsĪiroldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P.: Mixed membership stochastic blockmodels. We also find that the Grouped Hetero-RTM is effective for various textual data such as item reviews and movie descriptions. This model, furthermore, performs as effectively as the stochastic block model in the link prediction for existing nodes. Through intensive experiments that simulate real recommendation problems, the Grouped Hetero-RTM outperforms baseline methods at predicting links for isolated nodes. We present a new model called the Grouped Hetero-RTM that has both latent topics and latent clusterings. However, this simple extension degrades performance in a link prediction for existing nodes. In this study, we first naturally expand the relational topic model (RTM) to a heterogeneous network (Hetero-RTM). This method makes it difficult to predict links for isolated nodes, which happens when new items are recommended. ![]() In conventional PGMs, a link between two nodes is predicted on the basis of the nodes’ other existing links. This paper presents a new probabilistic generative model (PGM) that predicts links for isolated nodes in a heterogeneous network using textual data.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
June 2023
Categories |