Should I Build It Should I Build It
11 Apr 2025
Food & Drink

Personalized restaurant recommendations on a new location based on my ...

...preferences and mood

Confidence
Engagement
Net use signal
Net buy signal

Idea type: Freemium

People love using similar products but resist paying. You’ll need to either find who will pay or create additional value that’s worth paying for.

Should You Build It?

Build but think about differentiation and monetization.

Your are here

Your idea for personalized restaurant recommendations based on preferences and mood falls into the 'Freemium' category, which means users generally like these types of services, but are often hesitant to pay for them. We found about 10 similar products, indicating a good amount of interest and validation for this space, but also a moderate level of competition. The engagement with these similar products is medium, averaging around 5 comments per launch. Since you're in a crowded space, differentiation is key. Think about what makes your recommendation engine stand out from the rest. You'll likely need to identify specific features or a niche audience that would be willing to pay for a premium version of your service.

Recommendations

  1. Start by deeply understanding your potential users. Focus on identifying which users get the most value from free versions of similar restaurant recommendation services. Analyze user behavior and feedback to pinpoint specific needs and pain points that a premium offering could address.
  2. Based on your understanding of user needs, develop premium features that offer substantial value beyond the free version. This could include more granular personalization options (e.g., dietary restrictions, ambiance preferences), advanced filtering capabilities, exclusive deals, or priority access to reservations. Consider adding a mood component, such as 'romantic' or 'business' settings.
  3. Explore the possibility of charging teams or groups instead of individuals. For example, businesses planning team lunches or event organizers seeking venue recommendations could be a lucrative target market. Consider offering collaborative planning features and group discounts.
  4. Consider offering personalized help or consulting services to complement your automated recommendations. This could involve pairing users with a dedicated concierge who provides tailored suggestions, assists with booking reservations, and offers insider tips. Monetize this service through a subscription or per-use fee.
  5. Test different pricing approaches with small groups of users to determine the optimal price point and packaging for your premium features. A/B test various subscription tiers, one-time purchases, and freemium models to identify the most effective monetization strategy. Be sure to get detailed feedback from users regarding price sensitivity and feature preferences.
  6. Address the common criticism found in similar products regarding trust in recommendations. Actively solicit and highlight user reviews and ratings to build credibility and transparency. Consider incorporating a verification system to ensure the authenticity of reviews.
  7. Based on the similar product 'Recce', focus on facilitating the sharing of recommendations within trusted circles. Implement social features that allow users to easily share their favorite restaurants with friends and family, fostering a sense of community and personalized discovery.
  8. Incorporate detailed restaurant discovery features. Let users search restaurants by notes or tags in Google Maps to overcome the limitations of only searching within one's social circle (as was the case with Recce). This can address specific needs beyond general searches and the current location, as reported in Find Me a Restaurant criticisms.

Questions

  1. Given the criticism that some similar products' search results lack personalization or provide irrelevant suggestions, how will you ensure that your recommendations are truly tailored to individual preferences and moods?
  2. Considering that 'Find Me a Restaurant' was criticized for lacking features like restaurant exclusion and manual location modification, what specific features will you include to allow users more control over the recommendation process and cater to travel planning needs?
  3. How will you balance the desire for free access to your service with the need to generate revenue and sustain long-term growth, especially given the prevalence of the 'Freemium' model in this space and the fact that people resist paying for similar products?

Your are here

Your idea for personalized restaurant recommendations based on preferences and mood falls into the 'Freemium' category, which means users generally like these types of services, but are often hesitant to pay for them. We found about 10 similar products, indicating a good amount of interest and validation for this space, but also a moderate level of competition. The engagement with these similar products is medium, averaging around 5 comments per launch. Since you're in a crowded space, differentiation is key. Think about what makes your recommendation engine stand out from the rest. You'll likely need to identify specific features or a niche audience that would be willing to pay for a premium version of your service.

Recommendations

  1. Start by deeply understanding your potential users. Focus on identifying which users get the most value from free versions of similar restaurant recommendation services. Analyze user behavior and feedback to pinpoint specific needs and pain points that a premium offering could address.
  2. Based on your understanding of user needs, develop premium features that offer substantial value beyond the free version. This could include more granular personalization options (e.g., dietary restrictions, ambiance preferences), advanced filtering capabilities, exclusive deals, or priority access to reservations. Consider adding a mood component, such as 'romantic' or 'business' settings.
  3. Explore the possibility of charging teams or groups instead of individuals. For example, businesses planning team lunches or event organizers seeking venue recommendations could be a lucrative target market. Consider offering collaborative planning features and group discounts.
  4. Consider offering personalized help or consulting services to complement your automated recommendations. This could involve pairing users with a dedicated concierge who provides tailored suggestions, assists with booking reservations, and offers insider tips. Monetize this service through a subscription or per-use fee.
  5. Test different pricing approaches with small groups of users to determine the optimal price point and packaging for your premium features. A/B test various subscription tiers, one-time purchases, and freemium models to identify the most effective monetization strategy. Be sure to get detailed feedback from users regarding price sensitivity and feature preferences.
  6. Address the common criticism found in similar products regarding trust in recommendations. Actively solicit and highlight user reviews and ratings to build credibility and transparency. Consider incorporating a verification system to ensure the authenticity of reviews.
  7. Based on the similar product 'Recce', focus on facilitating the sharing of recommendations within trusted circles. Implement social features that allow users to easily share their favorite restaurants with friends and family, fostering a sense of community and personalized discovery.
  8. Incorporate detailed restaurant discovery features. Let users search restaurants by notes or tags in Google Maps to overcome the limitations of only searching within one's social circle (as was the case with Recce). This can address specific needs beyond general searches and the current location, as reported in Find Me a Restaurant criticisms.

Questions

  1. Given the criticism that some similar products' search results lack personalization or provide irrelevant suggestions, how will you ensure that your recommendations are truly tailored to individual preferences and moods?
  2. Considering that 'Find Me a Restaurant' was criticized for lacking features like restaurant exclusion and manual location modification, what specific features will you include to allow users more control over the recommendation process and cater to travel planning needs?
  3. How will you balance the desire for free access to your service with the need to generate revenue and sustain long-term growth, especially given the prevalence of the 'Freemium' model in this space and the fact that people resist paying for similar products?

  • Confidence: High
    • Number of similar products: 10
  • Engagement: Medium
    • Average number of comments: 5
  • Net use signal: 18.9%
    • Positive use signal: 22.2%
    • Negative use signal: 3.3%
  • Net buy signal: 0.0%
    • Positive buy signal: 0.0%
    • Negative buy signal: 0.0%

This chart summarizes all the similar products we found for your idea in a single plot.

The x-axis represents the overall feedback each product received. This is calculated from the net use and buy signals that were expressed in the comments. The maximum is +1, which means all comments (across all similar products) were positive, expressed a willingness to use & buy said product. The minimum is -1 and it means the exact opposite.

The y-axis captures the strength of the signal, i.e. how many people commented and how does this rank against other products in this category. The maximum is +1, which means these products were the most liked, upvoted and talked about launches recently. The minimum is 0, meaning zero engagement or feedback was received.

The sizes of the product dots are determined by the relevance to your idea, where 10 is the maximum.

Your idea is the big blueish dot, which should lie somewhere in the polygon defined by these products. It can be off-center because we use custom weighting to summarize these metrics.

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Users commented on a Nutri-Matic machine that produces a beverage resembling tea. Some users have posted similar content and suggested sharing insights. There are issues with registration, as it requires a Google account, and a feedback button error was reported. The computer voice used by the machine is reminiscent of a 'design is very human' personality. There are mixed feelings about the automated culinary appropriation, with some users upset about sharing food culture. Additionally, there are concerns about the potential for food poisoning from the machine's recipes.

Users criticized the product for producing a tea-like liquid instead of the expected beverage, requiring a Google account for use, and having a faulty feedback button. There were also concerns about cultural insensitivity regarding automated culinary appropriation and unease about sharing food culture. Additionally, there were worries about the safety of the food produced.

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Eato – provides personalized restaurant and dish recommendations

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Eato is an app that curates restaurant AND dish recommendations based on the user's specific food taste.Finding new restaurants is time-consuming, especially for picky foodies who are new to a place. Personally, I only trust the recommendations of friends who share the taste in food as me. However, if I can't rely on personal recommendations (from friends, family, locals), I have to experiment a lot in order to find a good food spot, wasting both time and money. The goal of this app is to recommend places you are guaranteed to like by taking into account the type of food you grew up eating, type of flavors you gravitate towards, etc.The beta app is now live (for Seattle, WA only) and we're looking for some feedback.Backend logic: The app requires the user to answer an initial survey about specific food habits. Eato then matches you with users with similar responses and displays those users’ fav restaurants and dishes. This does, however, require the user to do some work. If you're a foodie and have faced this problem, I would love to hear your feedback!Check out our Instagram eato.app if you want to hear about app updates and information :)

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Perplexity-insp. Restaurant Search:Ask Anything,from Cuisine to Reviews

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Butler, inspired by Perplexity, is an AI-powered and conversational search engine that assists users in finding restaurants by answering queries such as "a cosy restaurant that serves italian food and has a price range of $100-$200" and querying reviews to determine whether the restaurant meets their needs, such as "what do people think of pasta? "What do people dislike about this location?”We have published the MVP, which includes basic functionality such as restaurant search and reviews, including a list of some of the top restaurants in London, Paris, New York, San Francisco and Barcelona.

Users appreciate the AI-powered search engine for restaurant queries and reviews. It has been tested successfully in London, identifying the best pizza places. There is interest in expanding the service to include restaurant reviews in Germany. Overall, the idea is well-received and considered cool.

Users criticized the product for having search results that are too fast and for lacking German restaurant reviews.

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Travel Guide Tool – Personalized Recommendations and Itineraries

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Built a simple tool for travelers. Input your city and preferences, get back a list of recommendations or a detailed itinerary. Includes non-touristy spots, practical tips, and Google Maps links for every place mentioned. No signups required.Recommendations: Includes attractions, rentals, local tips, hidden gems. Example: Café La Biela.Itineraries: Detailed plans considering your preferences.Give it a try. Also live on Product Hunt: https://www.producthunt.com/posts/trip-bespokeLooking for feedback or any thoughts on how to improve.

Users find the product impressive, especially with verbose descriptions. However, there are concerns about the personalization aspect and language inconsistency in search results, with some results appearing in Spanish. While some users were already familiar with many NYC recommendations, they appreciated learning about new places like Prospect Park live music.

Users criticized the itineraries for having long distances, making them unsuitable for kids. They also mentioned a lack of personalization and that the results were in Spanish, especially for unpunctuated searches. Additionally, some users felt that the NYC recommendations were not new or unique.

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recce - Share Restaurant Recommendations With Friends

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Discover and share restaurant recommendations with friends and family.

Recce's Product Hunt launch garnered positive feedback for its focus on trusted, personal restaurant recommendations, with users praising the app's usefulness, ease of use, and vibrant location section. The concept of sharing and discovering recommendations from trusted circles resonated strongly. Some users suggested expanding the app's functionality beyond restaurants to include cafes and other establishments. There was also a desire for improved restaurant discovery features beyond social circles and the ability to search restaurants by notes.

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Find Me a Restaurant - Your daily recommendation is a tap away

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Unsure of where to eat 🤷? Craving a cuisine 🍜? On a budget 💰? Find Me a Restaurant basically finds you a restaurant by offering one daily recommendation. Perfect for those indecisive moments or outings with friends. Oh, and it's free to use.

The Product Hunt launch received positive feedback, with users congratulating the team and praising the app's simplicity and usefulness in solving the 'where to eat' problem. Users are interested in finding quality restaurants and appreciate the app's potential for discovering new dining options. There are requests for expanded city availability, location-based features, personalization options, and the ability to manually set locations for travel planning. Some users suggested re-rolling recommendations and pre-trip city selection, also integration with other location-based apps. There are also questions about data sources.

Users criticize the product for lacking features like restaurant exclusion and manual location modification, relying solely on current location services. A key concern is the inability to pre-select a city for trip planning, resulting in recommendations of restaurants that are too far away. Users have also expressed a need to re-roll recommendations.

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Users congratulate the Product Hunt launch and highlight the app's functionality and recommendation capabilities. However, feedback indicates a need for a UI revamp or improvement. There is also interest in future plans for expansion.

Users criticize the product's UI, indicating a need for a revamp. There's a request for information regarding specific plans or timelines for UI improvements.

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