TY - GEN
T1 - Towards Controllable Explanation Generation for Recommender Systems via Neural Template
AU - Li, Lei
AU - CHEN, Li
AU - Zhang, Yongfeng
N1 - Publisher Copyright:
© 2020 ACM.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/4/20
Y1 - 2020/4/20
N2 - 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.
AB - 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.
KW - Explainable recommendation
KW - natural language generation
KW - neural networks
UR - http://www.scopus.com/inward/record.url?scp=85091702053&partnerID=8YFLogxK
U2 - 10.1145/3366424.3383540
DO - 10.1145/3366424.3383540
M3 - Conference contribution
AN - SCOPUS:85091702053
T3 - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
SP - 198
EP - 202
BT - The Web Conference 2020 - Companion of the World Wide Web Conference, WWW 2020
PB - Association for Computing Machinery
T2 - 29th International World Wide Web Conference, WWW 2020
Y2 - 20 April 2020 through 24 April 2020
ER -