Improving Product Review Search Experiences in General Search Engines.

Proceedings of the International Conference on Electronic Commerce – EC2009. 2009
Improving Product Review Search Experiences in General Search Engines.
Shen Huang, Dan Shen, Wei Feng, Catherine Baudin, Yongzheng Zhang, Shen Huang, Dan Shen, Wei Feng, Catherine Baudin, Yongzheng Zhang

In the Web 2.0 era, internet users contribute a large amount of online content. Product review is a good example. Since these phenomena are distributed all over shopping sites, weblogs, forums etc., most people have to rely on general search engines to discover and digest others' comments. While conventional search engines work well in many situations, it's not sufficient for users to gather such information.

The reasons include but are not limited to: 1) the ranking strategy does not incorporate product reviews' inherent characteristics, e.g., sentiment orientation; 2) the snippets are neither indicative nor descriptive of user opinions. In this paper, we propose a feasible solution to enhance the experience of product review search.

Based on this approach, a system named "Improved Product Review Search (IPRS)" is implemented on the ground of a general search engine. Given a query on a product, our system is capable of: 1) automatically identifying user opinion segments in a whole article; 2) ranking opinions by incorporating both the sentiment orientation and the topics expressed in reviews; 3) generating readable review snippets to indicate user sentiment orientations; 4) easily comparing products based on a visualization of opinions.

Both results of a usability study and an automatic evaluation show that our system is able to assist users quickly understand the product reviews within limited time.

Another publication from the same category: Machine Learning and Data Science

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Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.