Ankita Sinha
ABSTRACT
The recent boom in e-commerce has created active electronic communities where consumers share their thoughts about the product and the company. These reviews play a very important part in building customer opinion about the said item. For a popular product or service, there might be thousands of reviews, making it difficult for the customer to make an informed decision about the product. In this paper, we present a way to surface only those reviews that contain information relevant to the user. To address this problem, we try to surface out the reviews that are outliers to the general cluster of reviews during a particular time period.We are leveraging anomaly detection algorithms to achieve this.
- Markus Breunig, Hans-Peter Kriegel, Raymond Ng, and Joerg Sander. 2000. LOF: Identifying Density-Based Local Outliers. ACM Sigmod Record 29, 93--104. https://doi.org/10.1145/342009.335388Google Scholar
Digital Library
- Zhangyu Cheng, Chengming Zou, and Jianwei Dong. 2019. Outlier detection using isolation forest and local outlier factor. 161--168. https://doi.org/10.1145/3338840.3355641Google ScholarDigital Library
- Jesse Davis and Mark Goadrich. 2006. The Relationship Between Precision-Recall and ROC Curves. Proceedings of the 23rd International Conference on Machine Learning, ACM 06. https://doi.org/10.1145/1143844.1143874Google ScholarDigital Library
- A. Ghose and Panos Ipeirotis. 2007. Designing Novel Review Ranking Systems: Predicting Usefulness and Impact of Reviews. ACM International Conference Proceeding Series 258. https://doi.org/10.1145/1282100.1282158Google ScholarDigital Library
- Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168--177. https://doi.org/10.1145/1014052.1014073Google ScholarDigital Library
- Minqing Hu and Bing Liu. 2004. Mining Opinion Features in Customer Reviews. Proceedings of AAAI.Google ScholarDigital Library
- Fei Tony Liu, Kai Ting, and Zhi-Hua Zhou. 2009. Isolation Forest. 413--422. https://doi.org/10.1109/ICDM.2008.17Google ScholarDigital Library
Index Terms
- Cerebro: Novelty Detection in Product Reviews
Recommendations
Deep Learning for Anomaly Detection: A Review
Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced ...
A flexible framework for anomaly Detection via dimensionality reduction
AbstractAnomaly detection is challenging, especially for large datasets in high dimensions. Here, we explore a general anomaly detection framework based on dimensionality reduction and unsupervised clustering. DRAMA is released as a general python package ...
Comments