Ranking mechanisms in twitter-like forums

WSDM 2010 (Invited to TIST Special Issue)
Ranking mechanisms in twitter-like forums
Anish DasSarma, Atish Das Sarma, Sreenivas Gollapudi, Rina Panigrahy, Anish DasSarma, Atish Das Sarma, Sreenivas Gollapudi, Rina Panigrahy
Abstract

We study the problem of designing a mechanism to rank items in forums by making use of the user reviews such as thumb and star ratings. We compare mechanisms where forum users rate individual posts and also mechanisms where the user is asked to perform a pairwise comparison and state which one is better.

The main metric used to evaluate a mechanism is the ranking accuracy vs the cost of reviews, where the cost is measured as the average number of reviews used per post. We show that for many reasonable probability models, there is no thumb (or star) based ranking mechanism that can produce approximately accurate rankings with bounded number of reviews per item.

On the other hand we provide a review mechanism based on pairwise comparisons which achieves approximate rankings with bounded cost. We have implemented a system, shoutvelocity, which is a twitter-like forum but items (i.e., tweets in Twitter) are rated by using comparisons. For each new item the user who posts the item is required to compare two previous entries.

This ensures that over a sequence of n posts, we get at least n comparisons requiring one review per item on average. Our mechanism uses this sequence of comparisons to obtain a ranking estimate. It ensures that every item is reviewed at least once and winning entries are reviewed more often to obtain better estimates of top items.

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

WWW '17 Perth Australia April 2017

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.

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