![]() ![]() # ARIN WHOIS data and services are subject to the Terms of Use # available at: # If you see inaccuracies in the results, please report at # Copyright 1997-2018, American Registry for Internet Numbers, Ltd. Relying on Google Mobile-Friendly test is well optimized for mobile and tablet devices. In accordance with Google Safe Browsing, Google Safe Search, Symantec and Web of Trust is pretty a safe domain. COM zone.ĭuring the last check (June 16, 2019) has an expired SSL certificate (expired on November 19, 2019), please click the “Refresh” button for SSL Information at the Safety Information section. See the list of other websites hosted by UNIFIEDLAYER-AS-1 - Unified Layer, US. It’s good for that their hosting company UNIFIEDLAYER-AS-1 - Unified Layer, US is located in United States, as that provides the majority of their visitors to benefit from the faster page load time. Every unique visitor makes about 1 pageviews on average.Īlexa Traffic Rank estimates that is ranked number 83,047 in the world, while most of its traffic comes from United States, where it occupies as high as 592,713 place. How we arrive at what’s relevant / irrelevant is itself a complicated topic, and I recommend my previous article if you’re traffic estimate is about 757 unique visitors and 757 pageviews per day. A judgment list, is just a term of art for the documents labeled as relevant/irrelevant for each query. This holds the judgment list used as the ground truth of MSMarco. You can find the datasets here.įor exploring MRR, for now we really just care about one file for MSMarco, the qrels. MSMarco is a question-answering dataset used in competitions and to prototype new/interesting ideas. Of course, we do this over possibly many thousands of queries! Which is where Pandas comes in. An MRR close to 1 means relevant results tend to be towards the top of relevance ranking. The mean of these two reciprocal ranks is 1/2 + 1/3 = 0.4167. We see in the tables above the reciprocal rank of each query’s first relevant search result - in other words 1 / rank of that result. Then, similarly, we search for “Who is PM of Canada?” we get back: Query We can then compute a reciprocal rank or just 1 / rank in the examples below Query If we search for “How far away is Mars?” and our result listing is the following, note how we know the rank of the correct answer. In question answering, everything else is presumed irrelevant. Such as in the two questions below: QueryĮach question here has one labeled, relevant answer. This occurs in applications such as question answering, where one result is labeled relevant. If MRR is close to 1, it means relevant results are close to the top of search results - what we want! Lower MRRs indicate poorer search quality, with the right answer farther down in the search results.Īs MRR really just cares about the ranking of the first relevant document, it’s usually used when we have ‘one relevant result’ to our query. Mean Reciprocal Rank or MRR measures how far down the ranking the first relevant document is. For this reason, I want to look at how Pandas can be used to rapidly compute one such statistic: Mean Reciprocal Rank. As you experiment, you’ll want to compute such a statistic over thousands of queries. If we’re building a search app, we often want to ask ‘How good is its relevance?’ As users will try millions of unique search queries, we can’t just try 2-3 searches, and get a gut feeling! We need to put a robust number on search quality. ![]()
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