@inproceedings{ed2df1e1098248a6bd96e82dde7ab743,
title = "SpecLDA: Modeling product reviews and specifications to generate augmented specifications",
abstract = "Product specifications are often available for a product on E-commerce websites. However, novice customers often do not have enough knowledge to understand all features of a product, especially advanced features. In order to provide useful knowledge to the customers, we propose to automatically generate augmented product specifications, which contains relevant opinions for product feature values, feature importance, and product-specific words. Specifically, we propose a novel Specification Latent Dirichlet Allocation (SpecLDA) that can enable us to effectively model product reviews and specifications at the same time. It mines review texts relevant to a feature value in order to inform customers what other customers have said about the feature value in reviews of the same product and also different products. SpecLDA can also infer importance of each feature and infer which words are special for each product so that customers quickly understand products. Experiment results show that SpecLDA can effectively model product reviews with specifications. The model can be used for any text collections with specification (key-value) type prior knowledge.",
author = "Park, {Dae Hoon} and Chengxiang Zhai and Lifan Guo",
year = "2015",
month = jan,
day = "1",
language = "English (US)",
series = "SIAM International Conference on Data Mining 2015, SDM 2015",
publisher = "Society for Industrial and Applied Mathematics Publications",
pages = "837--845",
editor = "Jieping Ye and Suresh Venkatasubramanian",
booktitle = "SIAM International Conference on Data Mining 2015, SDM 2015",
address = "United States",
note = "SIAM International Conference on Data Mining 2015, SDM 2015 ; Conference date: 30-04-2015 Through 02-05-2015",
}