Exploiting both vertical and horizontal dimensions of feature hierarchy for effective recommendation

Zhu Sun, Jie Yang, Jie Zhang, Alessandro Bozzon

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

15 Citations (Scopus)


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)
Number of pages7
ISBN (Print)978-1577357803
Publication statusPublished - 2017
Event31st AAAI Conference on Artificial Intelligence: AAAI 2017 - Hilton San Francisco Union Square, San Francisco, United States
Duration: 4 Feb 201710 Feb 2017
Conference number: 31


Conference31st AAAI Conference on Artificial Intelligence
Abbreviated title AAAI Conference on Artificial Intelligence
Country/TerritoryUnited States
CitySan Francisco
Internet address

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