In the previous 2 parts of this article, I reviewed the user experience of 4 restaurant discovery sites. 

User Experience of Restaurant Discovery - Part One

User Experience of Restaurant Discovery - Part Two

In this part, I will discuss only two questions: 

  1. Can we trust the content on those sites and how much can we trust them? 
  2. What is the desired restaurant discovery interaction and future? 

How can we trust the content?  

I don't think this article is the first one to raise this question. You can easily find many discussion online on this topic:

Yelp

A professor at Boston University commented on Yelp: 

Yelp Reviews: Can You Trust Them?

CNN talked about it too: 

Yelp: You can trust our reviews

A restaurant was so fed up by Yelp, they even offered 1-star review for discount.

Bistro turns the tables on Yelp, offers discounts to customers for 1-star reviews

Chef Marcus Guiliano talked about Yelp on his channel

Yelp Sucks and Here is Why...More Court Cases

And then, you can find a Facebook group about it:

Yelp is a Fraud 

There are more, you can easily find them online. I just listed a few above only. 

TripAdvisor 

TripAdvisor is not doing any better either. Here are a few discussion: 

IS TRIPADVISOR RELIABLE? HOW MUCH CAN WE TRUST ITS REVIEWS?

TripAdvisor and the issue of trust

Why I Don’t Trust TripAdvisor – and You Should Neither

Finally, check the complaints about them: 

Consumer Complaints​

Zomato

They are pretty new, but it still has some negative comments online about them already. 

After Reading this Story You will Never Trust Zomato!!

 

To me, the first fundamental cause is actually crowd-sourcing. Crowd-source is a great way to accumulate content, but someone still needs to monitor who is contributing to the content. Here let me quote a statement from a famous person: 

if it is on internet, it must be true

Obviously the statement was a joke. There are more and more fake content on internet today. We can blame the providers (i.e. Yelp, TripAdvisor, etc.) controlling the content. However, without monitoring, how do we know what we are reading are not fake? Of course it could go both ways: we are reading a fake hateful review and we are reading a fake positive review. 

The second cause is the providers need to generate income. As long as the content is linked with money (including advertising and ranking), trust cannot be established. 

To answer my own question: How can we trust the content? In the context of restaurant discovery, my answers will be the following:

  1. The service provider should not benefit in any way from the content contributed.
    If the provider can use the content to charge businesses, the content could be manipulated, thereby it will not trust-able. 

  2. The content should belong to the visitors, i.e. users.  
    The goal of restaurant discovery service should be serving visitors, therefore, the content should really belong to all users. 

  3. In an ideal situation, the content should be generated by machine not manually entered by a user.
    If any Joe Doe can go to a website and enter content, it is understandably not trust-able. Therefore, content, including review and popularity for a specific business should be generated by machine not by human input. See my next section for elaboration 

In sum, based on my answers, there is no restaurant discovery provider can be trusted, at least now. 


Where is restaurant discovery going in the future? 

Before I can discuss that question, can I first ask: what is the best search engine in today's world?

Google is a fine search engine 

Well, at least I did not make any statement. But how Google achieves this reputation?

Because most of us know Google displays results in an order determined by algorithm instead of human (hopefully), the results become most trustful. Most people also believe the results are accurate. In fact, more and more people realize the results displayed on the first page pretty much match their expectation, they become more and more faithful to Google search. 

The best are on the first page of Google search results

How can restaurant discovery apply the same principle and metaphor, as well as building up trustful content? Here is my bold predication: 

Content Source

  • The content should be machine-generated and hence has no bias; 
  • The content should come from various sources; 

Here are a few possible sources:

surveillance  Most businesses have surveillance, restaurant discovery can benefit from some data such as: crowd at specific time range to provide some attributes: popularity and recommendation on reservation or when to head over without starving in the line. 
social media It is not new to data mine from social media, which will be very useful to derive a list of recommendations based on discussion, reputation, and trending. 
menu To generate better content for discovery, restaurants will all need to digitize their menu eventually. These data will be associated with the data collected from social media to create personalized results. 
geocaching Similar to the game, dining out is a treasure hunt as well. Foursquare has been trying to do something like this with the metaphor of checking in; even though it was interesting at the beginning, it was pretty soon forgotten by many later. Restaurant caching, in my opinion, should happen automatically not manually. For example, using Beacon or GPS on smart devices. People can also choose to share the caching privately, publicly, or in a selected group. 

Here I am not interested in discussing privacy. I believe that there are number of ways to achieve good knowledge/information sharing without releasing any confidential data. 

Content Presentation 

Once we have algorithm to collect content from those potential sources, we will need a "search" interface to present the results.

Of course we can design a big text box on a responsive web page, or a SIRI-like smartphone app. Ultimately, the interface would probably follow this work flow:

 Restaurant Discovery Algorithm work flow

The boxes in the center can go on and on. The bottom line is, the algorithm should gather ambient information to apply filters, hereby provide better recommendations and reduce cluster in the interface. Ideally, there is no filter control required for users to manipulate. As one of my favorite books says: No Interface is the Best Interface

 

 If there is no interface, is it a web page, or is it a phone app? It could be either. No interface does not necessarily mean there is no media to interact with. Since we are talking about future, I can go as further as I want. For example, if I go in an elevator, which has 3 faces are interactive screens and one face detects I am nearby, show a piece of information of 3 nearby restaurants I can go to. I bring up my phones, those options already populated on my phone. I select one and get the direction. 

There are a few key notes here: (1) Any one else in the same elevator doesn't really know those information presented for me. (2) We do not need too many recommendations. Seriously, with thousands of restaurants in the city, who will browse each of them and then make a decision. Chance is, give me a few options, I will probably go to one I haven't tried or offer something I want to eat at the moment. (3) There is no interaction/interface to trigger the search. Why do I have to enter a text in a specific location? Assuming I give you permission to know what I like to eat, and since it is near lunch time, just show me some options. If in fact, I am not going for lunch, I can simply disregard the information. 

Is there any other design, for sure I believe so, but I will leave it for you to think about. 


In this article, I discussed content on restaurant discovery and how restaurant discovery could be in the future. This is the final part of user experience of restaurant discovery. Thanks for reading.