Dual Attention Network for Product Compatibility and Function Satisfiability Analysis

By Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu. 2017

13 citations recorded by the source corpus.

Product compatibility and functionality are of utmost importance to customers when they purchase products, and to sellers and manufacturers when they sell products. Due to the huge number of products available online, it is infeasible to enumerate and test the compatibility and functionality of every product. In this paper, we address two closely related problems: product compatibility analysis and function satisfiability analysis, where the second problem is a generalization of the first problem (e.g., whether a product works with another product can be considered as a special function). We first identify a novel question and answering corpus that is up-to-date regarding product compatibility and functionality information. To allow automatic discovery product compatibility and functionality, we then propose a deep learning model called Dual Attention Network (DAN). Given a QA pair for a to-be-purchased product, DAN learns to 1) discover complementary products (or functions), and 2) accurately predict the actual compatibility (or satisfiability) of the discovered products (or functions). The challenges addressed by the model include the briefness of QAs, linguistic patterns indicating compatibility, and the appropriate fusion of questions and answers. We conduct experiments to quantitatively and qualitatively show that the identified products and functions have both high coverage and accuracy, compared with a wide spectrum of baselines.

Subfields: Artificial Intelligence, Management of Technology and Innovation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bing Liu
2. Hu Xu
3. Zixuan Ke
4. Lei Shu
5. B. Liu
6. Philip S. Yu
7. Nianzu Ma
8. Sahisnu Mazumder

In Management of Technology and Innovation

1. Bing Liu
2. Zixuan Ke
3. Hu Xu
4. Lei Shu

Citations in the corpus

• Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning (2021, methodology): “; Adapter-BERT adapts the BERT as in [16], where only the adapter blocks are trainable; W2V uses embeddings trained on the Amazon review data in [66] using FastText [11].”
• Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks (2021, methodology): “3); Adapter-BERT adapts the BERT as in (Houlsby et al., 2019), where only the adapter blocks are trainable; W2V uses embeddings trained on the Amazon review data in (Xu et al., 2018) using Fast-Text (Grave et al., 2018).”
• BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis (2019, neutral): “tions (McAuley and Yang, 2016; Xu et al., 2018b).”
• CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (2021, methodology): “, 2019) and (4) W2V (word2vec embeddings trained with the Amazon review data in (Xu et al., 2018) using FastText (Grave et al.”
• Lifelong Domain Word Embedding via Meta-Learning (2018, methodology): “Although some embeddings trained using large scale corpora are available [ Pen-nington et al. , 2014; Bojanowski et al. , 2016], they are often sub-optimal for domain-specific tasks [Bollegala et al. , 2015; Yang et al. , 2017; Xu et al. , 2018a; Xu et al. , 2018b].”