Ebrahim Bagheri
Software / Data Engineering
Product reviews written by the crowd on e-commerce shopping websites have become an important information source for making purchasing decisions. Researchers have already extensively studied the impact of review sentiments and informativeness on product sales and success, finding a close relationship. An important challenge, however, is that the vast majority of products (e.g., >90% of products on amazon.com) do not receive enough attention and lack sufficient reviews by the users; hence, constitute the so-called cold products. A potential solution for addressing cold products is to curate expert reviews on such products as done by websites such as CNET.com. However, the multitude of these products is a major limiting factor. One solution to address cold products, which has already been studied in the literature, is to generate reviews for these products by sampling review sentences from closely related products.
The objective of this project is specifically on generating reviews for cold products by sampling review sentences from closely related warm products.
Students will learn about cutting edge techniques in Natural Language Processing and Information Retrieval.
The work will build upon and improve the work presented in https://dl.acm.org/citation.cfm?doid=2766462.2767748
The group will be responsible to replicate the work presented in the baseline paper, work with the FLC to further enhance the algorithm, implement the enhancements, perform experiments and report on the findings. All students will be responsible for understanding the baseline paper and participating in group brainstorming sessions.
Preparation of the dataset for the experiments and assist with the implementation of the enhancements to the baseline.
Implementation and Replication of the baseline code.
Leading the implementation of the enhanced code as well as running the experiments.
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EB03: Generating reviews for cold products | Ebrahim Bagheri | Not yet submitted at No time