Name
Abstract | ||
|---|---|---|
Many users of e-commerce portals commonly use customer reviews for making purchase decisions. But a product may have tens or hundreds of diverse reviews leading to information overload on the customer. The main objective of our work is to develop a recommendation system to recommend a subset of reviews that have high content score and good coverage over different aspects of the product along with their associated sentiments. We address the challenge which arises due to the fact that similar aspects are mentioned in different reviews using different natural language expressions. We use vector representations to identify mentions of similar aspects and map them with aspects mentioned in product features specifications. Review helpfulness score may act as a proxy for the quality of reviews, but new reviews do not have any helpfulness score. We address the cold start problem by using a dynamic convolutional neural network to estimate the quality score from review content. The system is evaluated on datasets from Amazon and Flipkart and is found to be more effective than the competing methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3109859.3109901 | RecSys |
Keywords | Field | DocType |
Review Recommendation, Content scoring, Product aspect, E-commerce | Recommender system,Data mining,Quality Score,Information overload,Helpfulness,Cold start,Expression (mathematics),Computer science,Natural language,E-commerce | Conference |
ISBN | Citations | PageRank |
978-1-4503-4652-8 | 2 | 0.37 |
References | Authors | |
19 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Debanjan Paul | 1 | 2 | 0.37 |
| Sudeshna Sarkar | 2 | 423 | 210.58 |
| Muthusamy Chelliah | 3 | 7 | 2.86 |
| Chetan Kalyan | 4 | 2 | 0.37 |
| Prajit Prashant Sinai Nadkarni | 5 | 2 | 0.37 |