![]() ![]() Our systems are designed to recognize terms and phrases that might be violent, sexually-explicit, hateful, disparaging or dangerous. Secondly, if our automated systems don’t catch predictions that violate our policies, we have enforcement teams that remove predictions in accordance with those policies. First and foremost, we have systems designed to prevent potentially unhelpful and policy-violating predictions from appearing. We deal with these potential issues in two ways. We also recognize that some queries are less likely to lead to reliable content. It’s also possible that people might take predictions as assertions of facts or opinions. There’s the potential to show unexpected or shocking predictions. But like anything, predictions aren’t perfect. Predictions, as explained, are meant to be helpful ways for you to more quickly finish completing something you were about to type. That’s also why it differs from and shouldn’t be compared against Google Trends, which is a tool for journalists and anyone else who’s interested to research the popularity of searches and search topics over time. ![]() Overall, Autocomplete is a complex time-saving feature that’s not simply displaying the most common queries on a given topic. Predictions will reflect the queries that are unique and relevant to a particular topic. In contrast, “trip to San Francisco” may show a prediction of “trip to San Francisco and Yosemite.” Even if two topics seem to be similar or fall into similar categories, you won’t always see the same predictions if you try to compare them. For example, someone searching for “trip to New York” might see a prediction of “trip to New York for Christmas,” as that’s a popular time to visit that city. People, places and things all have different attributes that people are interested in. Predictions also will vary, of course, depending on the specific topic that someone is searching for. However, if that team just won a big face-off against a rival, timely game-related predictions may be more useful for those seeking information that’s relevant in that moment. ![]() For example, searches for a basketball team are probably more common than individual games. If our automated systems detect there’s rising interest in a topic, they might show a trending prediction even if it isn’t typically the most common of all related predictions that we know about. We also take freshness into account when displaying predictions. For instance, if you were to type in “best star trek…”, we’d look for the common completions that would follow, such as “best star trek series” or “best star trek episodes.” To determine what predictions to show, our systems begin by looking at common and trending queries that match what someone starts to enter into the search box. ![]() Where predictions come fromĪutocomplete predictions reflect searches that have been done on Google. We’ll also look at why not all predictions are helpful, and what we do in those cases. In this post, we’ll explore how Autocomplete’s predictions are automatically generated based on real searches and how this feature helps you finish typing the query you already had in mind. These time-saving predictions are from a feature called Autocomplete, which we covered previously in this How Search Works series. As soon as you start typing, predictions appear in the search box to help you finish what you’re typing. You come to Google with an idea of what you’d like to search for. ![]()
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