Feature hierarchy (FH) has proven to be effective to improve recommendation accuracy. Prior work mainly focuses on the influence of vertically affiliated features (i.e. child-parent) on user-item interactions. The relationships of horizontally organized features (i.e. siblings and cousins) in the hierarchy, however, has only been little investigated. We show in real-world datasets that feature relationships in horizontal dimension can help explain and further model user-item interactions. To fully exploit FH, we propose a unified recommendation framework that seamlessly incorporates both vertical and horizontal dimensions for effective recommendation. Our model further considers two types of semanti-cally rich feature relationships in horizontal dimension, i.e. complementary and alternative relationships. Extensive validation on four real-world datasets demonstrates the superiority of our approach against the state of the art. An additional benefit of our model is to provide better interpretations of the generated recommendations.
|Title of host publication||The 31st AAAI Conference on Artificial Intelligence, AAAI 2017|
|Number of pages||7|
|Publication status||Published - 2017|
|Event||31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States|
Duration: 4 Feb 2017 → 10 Feb 2017
|Conference||31st AAAI Conference on Artificial Intelligence, AAAI 2017|
|Period||4/02/17 → 10/02/17|