DIRECT: Dual Interpretable Recommendation with Multi-aspect Word Attribution Article Swipe
Xuansheng Wu
,
Hanqin Wan
,
Qiaoyu Tan
,
Wenlin Yao
,
Ninghao Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3663483
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3663483
Recommending products to users with intuitive explanations helps improve the system in transparency, persuasiveness, and satisfaction. Existing interpretation techniques include post hoc methods and interpretable modeling. The former category could quantitatively analyze input contribution to model prediction but has limited interpretation faithfulness, while the latter could explain model internal mechanisms but may not directly attribute model predictions to input features. In this study, we propose a novel Dual Interpretable Recommendation model called DIRECT, which integrates ideas of the two interpretation categories to inherit their advantages and avoid limitations. Specifically, DIRECT makes use of item descriptions as explainable evidence for recommendation. First, similar to the post hoc interpretation, DIRECT could attribute the prediction of a user preference score to textual words of the item descriptions. The attribution of each word is related to its sentiment polarity and word importance, where a word is important if it corresponds to an item aspect that the user is interested in. Second, to improve the interpretability of embedding space, we propose to extract high-level concepts from embeddings, where each concept corresponds to an item aspect. To learn discriminative concepts, we employ a concept bottleneck layer and maximize the coding rate reduction on word-aspect embeddings by leveraging a word–word affinity graph extracted from a pre-trained language model. In this way, DIRECT simultaneously achieves faithful attribution and usable interpretation of embedding space. We also show that DIRECT achieves linear inference time complexity regarding the length of item reviews. We conduct experiments including ablation studies on five real-world datasets. Quantitative analysis, visualizations, and case studies verify the interpretability of DIRECT. Our code is available at: https://github.com/JacksonWuxs/DIRECT .
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3663483
- https://dl.acm.org/doi/pdf/10.1145/3663483
- OA Status
- hybrid
- Cited By
- 1
- References
- 54
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396669082
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396669082Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3663483Digital Object Identifier
- Title
-
DIRECT: Dual Interpretable Recommendation with Multi-aspect Word AttributionWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-06Full publication date if available
- Authors
-
Xuansheng Wu, Hanqin Wan, Qiaoyu Tan, Wenlin Yao, Ninghao LiuList of authors in order
- Landing page
-
https://doi.org/10.1145/3663483Publisher landing page
- PDF URL
-
https://dl.acm.org/doi/pdf/10.1145/3663483Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://dl.acm.org/doi/pdf/10.1145/3663483Direct OA link when available
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Computer science, Dual (grammatical number), Word (group theory), Natural language processing, Artificial intelligence, Attribution, Authorship attribution, Information retrieval, Linguistics, Philosophy, Social psychology, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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54Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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