Abstract :
We propose an algorithm for recommending both well-watched old movies and unwatched new ones. To recommend both old favourites and new releases, hybrids of collaborative and content-based filtering are the most suitable methods. However, hybrid movie recommenders have two issues. First, it is necessary to acquire content-descriptive metadata, which is not always easily available. Second, the metadata, once acquired, may be noisy, which can damage recommendation accuracy. In our algorithm, we address the first issue by automatically drawing movie metadata from Linked Open Data, and the second by modeling the relevance of the collected metadata to the transaction history before using the relationship between them to make recommendations. We experimentally demonstrate that our method can effectively collect metadata from LOD, and that our method outperforms conventional hybrid methods found in the literature in both well-watched and unwatched movie recommendation using the noisy collected movie metadata.
