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.

Original languageEnglish
Title of host publicationProceedings of the 31st Conference on Artificial Intelligence, AAAI 2017
PublisherAmerican Association for Artificial Intelligence (AAAI)
Pages189-195
Number of pages7
ISBN (Print)978-1577357803
StatePublished - 2017
Event31st AAAI Conference on Artificial Intelligence - San Francisco, United States

Conference

Conference31st AAAI Conference on Artificial Intelligence
Abbreviated title AAAI Conference on Artificial Intelligence
CountryUnited States
CitySan Francisco
Period4/02/1710/02/17
Internet address

ID: 33898204