SiliconFilter

Google Now Personalizes Its Maps with Your Rated Places and Recommendations

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Google today announced that it will start personalizing your Google Maps experience with your ratings and personalized recommendations. For now, Google is keeping these new features very subtle. Indeed, unless you look very closely, you may just overlook the new symbols. Places you have already rated will now appear with a number of dots underneath their respective symbols, corresponding to the star rating you gave them. Recommended places now feature a slight orange glow around their symbols.

Here is what the new symbols look like:

delicatessen_small_ratings_google_maps

recommendations_in_google_maps

For Google, of course, this is yet another way to get people to actually rate restaurants, shops and other local businesses in the first place. Unlike services like Yelp, few people explicitly come to Google Maps to leave reviews. The company’s place pages have increased the emphasis of reviewing businesses over time, though, and while most reviews on Google Maps and Place Pages are still aggregated from third parties, the number of native reviews seems to be going up now.

With Google Places, the company tried to get its users to leaving more reviews (and hence feed Google’s algorithms with more data), though I doubt most consumers are even aware of this service.

In an effort to bolster its recommendation services, Google also acquired Zagat earlier this year, though we haven’t seen any integration of Zagat’s ratings into Google’s own products yet.



6:15 pm


Google’s +1 Buttons for Websites Have Arrived – But Will You Use Them?

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Google today launched it’s +1 button for third-party sites. Until now, these buttons were only available on Google’s own search results page, but now, website owners will be able to integrate +1 into their own sites as well. Among today’s launch partners are major tech blogs like TechCrunch and Mashable, as well as Best Buy, The Washington Post, Reuters and Bloomberg. The question, though, is if users will actually want to press these buttons.

+1 Button = Delayed Gratification

In its current form, the +1 button is likely the least interesting button to press. The recommendations you make through +1 will only appear on Google’s search results pages (and your Google profile – but the reality is that nobody ever looks at those). There is no immediate gratification from using the button. Your recommendation won’t appear on your Facebook wall or in your Twitter feed. It may, at some point, appear on somebody’s search results page – but only if your friends end up using a query that would bring this site up anyway. Then, no doubt, this recommendation would be useful for your friends to decide to visit a site, but given that you can never know if that will ever happen, you’re probably better of ‘liking’ a story on Facebook than +1ing it.

Given that most users are likely just clicking one button per page they visit, chances are they will choose the one that’s most likely to get them an immediate reaction from their friends – and when it comes to that, the +1 simply doesn’t cut it against competitors like the Facebook and Twitter.



6:08 pm


Improving Social Search by Getting Opinions About Opinions

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When it comes to social search and social recommendations, there is a lot of hype around the concept, but given that a user’s social graph is – almost by default – limited, there are major gaps in both accuracy and coverage when it comes to putting this concept into reality. While Google +1 and Bing’s implementation of Facebook ‘like’ data are trying to find ways around this, Microsoft researcher Mohammad Raza argues (PDF) that we need a smarter recommendation system that is based on the idea that “your friends know you and can best predict your taste” and that social search can be greatly improved upon with the help of prediction extraction.

Note: you can download Raza’s paper here.

Flaws in Today’s Social Search Schemes

To understand why this matters, let’s look at how most of today’s social search and recommendation systems work. As Raza points out, the two main ideas behind social search today are that “your friends are like you” and “people we agree on certain things also agree on others.” In reality, though, individuals and communities are far more complex than this. Users may agree with friends on some things (pizza), but disagree on others (politics). While Razza doesn’t go into this, it’s also worth noting that the idea of “friendship” on social networks has become so diluted that many of you “friends” today have little to none in common with you.

Then, there’s the problem of coverage. Users generally only talk about and rate items when they have a strong positive or negative opinion about something. “Part of the difficulty,” writes Raza, “is to motivate people to give more feedback on more mundane items, or items that may be important to different people under different circumstances.”

How to Fix This?

Raza argues that we can past these problems by getting users’ opinions about others’ opinions. Even if your friends don’t agree with you about everything, they are, says Raza, “actually the people who know you best” (his emphasis). The idea the, is to “elicit predictions about the target user’s opinion of a certain item from the user’s friends who have experienced the item, and aggregate these predictions to construct an estimation of the target user’s opinion of the item before he has experienced it.”

Raza proposes to use a Facebook app that allows users to rate items they have experiences (movies, news, events, food, YouTube videos etc.). The unique twist here is that this app will also ask users to predict how one or more of their friends would rate this item.

Once a user then actually experiences this item (say a YouTube video) and rates it, this score will be used to train the algorithm and the software can learn which of your friends know you best and take their ratings into account when presenting you search results or other recommendations. Of course, the algorithm will also learn if your friends are good at predicting anybody’s reaction in general or if they are just good at predicting your reaction in certain areas.

Raza also proposes to push this system even further by allowing users to give reasons why they think a friend would like an item and elaborate on their opinions. Say your friend thinks you will like the movie “Inception” because it has Leonardo DiCaprio in it or because large parts of it play in Paris. The algorithm will then know that these are things you like (assuming your friend has been classified as trustworthy) and can tweak its recommendations accordingly.

For now, of course, there are no public implementations of this idea, but it does sound intriguing. In my experience, I find myself drawn more to purely algorithmic recommendation systems like my6sense and Zite than social apps like Flipboard and News.me because they have come to know my tastes better than my wide-flung group of friends on Facebook and Twitter. Chances are, though, if these social recommendation algorithms knew which friends to trust and who knows me best, this hybrid system that pulls in a far wider range of signals could present me with better recommendations than either system alone ever could.



9:27 am