Towards Controllable Explanation Generation for Recommender Systems via Neural Template

Lei Li, Li CHEN, Yongfeng Zhang

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

It has been commonly agreed that the explanation associated with recommendation can be effective in increasing the recommender systems (RS)'s transparency and thus users' satisfaction and acceptance. Among the various types of explanation in RS, the commonly used textual explanation can be roughly classified into two categories, i.e., template-based and generation-based. As for the former, the fixed template may lose flexibility, while, though the latter may enrich the explanation, it may produce less useful content due to the lack of controllability. In this work, we combine the advantages of the two types of method by developing a neural generation approach named Neural Template (NETE) whose explanations are not only flexible but also controllable and useful. Our human evaluation results confirm that the explanations from our model are perceived helpful by users. Furthermore, our case study illustrates that the explanation generation process is controllable. To demonstrate the controllability of our model, we present a demo that can be easily viewed on a Web browser.

Original languageEnglish
Title of host publicationThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PublisherAssociation for Computing Machinery
Pages198-202
Number of pages5
ISBN (Electronic)9781450370240
DOIs
Publication statusPublished - 20 Apr 2020
Event29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan, Province of China
Duration: 20 Apr 202024 Apr 2020

Publication series

NameThe Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020

Conference

Conference29th International World Wide Web Conference, WWW 2020
Country/TerritoryTaiwan, Province of China
CityTaipei
Period20/04/2024/04/20

Scopus Subject Areas

  • Computer Networks and Communications
  • Software

User-Defined Keywords

  • Explainable recommendation
  • natural language generation
  • neural networks

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