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Title: Graph-based Extractive Explainer for Recommendations
Explanations in a recommender system assist users make informed decisions among a set of recommended items. Extensive research attention has been devoted to generate natural language explanations to depict how the recommendations are generated and why the users should pay attention to them. However, due to different limitations of those solutions, e.g., template-based or generation-based, it is hard to make the explanations easily perceivable, reliable, and personalized at the same time. In this work, we develop a graph attentive neural network model that seamlessly integrates user, item, attributes and sentences for extraction-based explanation. The attributes of items are selected as the intermediary to facilitate message passing for user-item specific evaluation of sentence relevance. And to balance individual sentence relevance, overall attribute coverage and content redundancy, we solve an integer linear programming problem to make the final selection of sentences. Extensive empirical evaluations against a set of state-of-the-art baseline methods on two benchmark review datasets demonstrated the generation quality of proposed solution.  more » « less
Award ID(s):
2007492 1718216 1553568
NSF-PAR ID:
10381233
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the ACM Web Conference 2022
Page Range / eLocation ID:
2163 to 2171
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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