How to Create a Recommendation System Effortlessly

Why Smart Recommendations Matter ?

Imagine opening your favourite events app and seeing a list of local concerts, festivals or workshops that perfectly match your interests, thanks to a smart Recommendation System. No more endless scrolling. No more irrelevant suggestions. Just events you’d actually want to attend. That’s the power of smart recommendations.

In a world flooded with information, real challenge isn’t access—it’s relevance. Whether it’s a music gig or a coding bootcamp, users want to know what’s happening around them. And when you’re running a platform, helping users find what they care about isn’t just a nice feature—it’s a must have for keeping them engaged and coming back.

The Challenge: Helping Users Find the Right Events at the Right Time

The problem we faced was simple but tricky: How do we recommend only the upcoming events that matter most to each user?

We couldn’t just throw everything into a list and hope for the best. Showing users past events is useless. Recommending things outside their interest zone? Even worse !

What we needed was a way to:

Filter out irrelevant or outdated events,
Understand each user’s preferences (even if they’re brand new),
And make sure the recommendations stay fresh, engaging, and useful.

The Tech Behind the Magic: Gorse + SQL + Multi-Source Strategy

To solve this, we turned to Gorse, an open-source recommendation system built for speed, flexibility, and intelligence. It supports various recommendation sources like:

Items liked by similar users (user-based),
Similar items based on what users liked (item-based),
Popular events,
Newly added events.

We used SQL to manage the backbone—our users, items (i.e., events), and the feedback data (likes, interests, etc.). Then we designed a strategy that pulls recommendations from different angles:

For newcomers: Popular + Latest events.
For returning users: Events that match their preferences.
And for users giving feedback: A fine-tuned mix based on their actions.

To ensure users only see upcoming events, we used Gorse’s powerful filtering feature: isHidden, allowing us to keep the experience clean and timely.

This was just the beginning. What followed was a journey through user scenarios, feedback loops, and creative problem-solving to make the experience even better. Let’s dive into how we made it work.

Choosing the Right Recommendation Strategy

When you’re building a recommendation system for events, there’s no one-size-fits-all solution. People behave differently—some explore, some follow trends, and others know exactly what they want. That’s why the first big decision we had to make was:
What kind of recommendations actually make sense for event discovery?

We settled on a blend of three tried-and-tested approaches:

👥 User-Based Recommendations

This method suggests events that people with similar interests have interacted with. It’s like getting a nudge from someone with the same vibe as you—”Hey, you might like this too.” Perfect for users who already have a pattern of preferences.

🔥 Popular Events

Some events just attract attention. They’re trending, widely attended, or have broad appeal. We decided to use popularity as a signal for what might be worth promoting, especially for users who haven’t given us much to go on yet.

🆕 Latest Events

Freshness matters. Events come and go quickly, so we included newly added events in the mix. This helps keep the recommendations dynamic and ensures users don’t miss something that just went live.

🚀 Why Gorse? The Engine Behind the Curtain

To bring this multi-source strategy to life, we chose Gorse, a powerful open-source recommendation engine that supports hybrid recommendation models right out of the box.

What made Gorse a perfect fit?

It supports multiple recommendation sources (user similarity, item similarity, popularity, recency).
It allows us to customize how data is stored and used.
It’s designed to scale and perform well even with growing data and user activity.

With Gorse, we didn’t have to reinvent the wheel—we just had to teach it to spin the way we needed.

This strategy gave us a solid foundation. But the real magic started when we tested how users interacted with it. Let’s explore what happened when different kinds of users entered the system.

Setting Up Gorse for Upcoming Event Recommendations

Event Recommendation System-Setting Up Gorse

Once we had our recommendation strategy figured out, the next step was bringing it to life using Gorse. Thankfully, Gorse is designed to do exactly what we needed—it pulls recommendations from multiple sources and gives us the flexibility to tweak the logic to fit our use case.

Let’s break down how we wired everything up.

How Gorse Picks What to Recommend

Gorse combines several intelligent methods to figure out what to recommend. Here are the four key sources it uses:

👥 Neighbor User Items
Think of this as social-proof-driven recommendations. If users similar to you liked certain events, chances are, you’ll like them too.
🔄 Neighbor Items
This one focuses on the events themselves. If you interacted with Event A, and others who liked A also liked Event B, Gorse connects the dots and shows you B.
🔥 Popular Items
These are the crowd favorites. Gorse keeps track of what’s getting the most attention across the board and surfaces those events, especially helpful for users with no history yet.
🆕 Newly Added Items
This ensures freshness. It highlights the latest events added to the system, which keeps things dynamic and prevents the feed from feeling stale.

By blending these sources, Gorse gives each user a mix of familiar, trendy, and fresh suggestions—all in real-time.

Using SQL to Organize Our Data

Behind the scenes, Gorse depends on a structured data pipeline. We used SQL to manage and store everything it needs to function effectively:

Users:
Each user had a unique ID and could optionally have labels like categories of interest, preferred districts, or even budget range.
Items (Events):
Every event was stored as an item, with metadata like category, district, ticket price, and—most importantly—the event date.
Feedback (Interactions):
Whenever a user liked, bookmarked, or viewed an event, that interaction was saved as feedback. This feedback is gold—it teaches Gorse what works and what doesn’t.

This SQL-driven setup allowed us to easily push data into Gorse, update it regularly, and keep everything clean and queryable for further analysis.

Hiding the Past: Making Recommendations Time-Sensitive

Here’s the thing: recommending a concert that happened last weekend isn’t just irrelevant—it actively hurts the user experience.

So how did we make sure users only saw upcoming events?

We used one of Gorse’s hidden gems: the isHidden flag.

We set this flag to true for any event that had already taken place. Gorse then automatically excluded those events from its recommendation pool. That one feature saved us a lot of custom logic and made the system smarter by default.

By combining smart filtering with structured data and multi-source recommendation logic, we created a system that was not only powerful—but also context aware.

In short, setting up Gorse was like plugging in a recommendation brain—but one that listened to us and adapted to our needs. Next up, let’s see what happened when different types of users started interacting with it.

Testing Recommendation Scenarios

Once the system was set up, it was time for the real test: Would it behave the way we expected when different users interacted with it?

To answer that, we created a few controlled test cases that represented the different types of users our platform might see. The goal? Understand how Gorse responded in each case—and whether the results felt relevant and helpful.

Let’s break them down 👇

Case 1: New User with No Preferences

Imagine someone just signed up. They haven’t selected any preferences, categories, or districts. Gorse knows nothing about them yet.

Expected Behavior: Show them what’s trending and what’s fresh.
What Happened: As predicted, the user was shown a mix of:

🔥 Popular events that had strong engagement from other users
🆕 Newly added events, since freshness is always a safe bet

✅ Result: The recommendations made sense—relevant, engaging, and diverse. A great way to welcome new users.

Case 2: User with Selected Preferences

Now take a user who’s picked a few categories (say, “Music” and “Workshops”) or preferred districts.

Expected Behavior: Surface events that match those interests, with some popular events mixed in.
What Happened:
Gorse leaned into the preferences, showing:

🎵 Events that matched the user’s selected labels
🔥 Popular events, especially if they also matched preferences

✅ Result: The blend felt right. It struck a balance between personal taste and general popularity—perfect for a user still exploring.

Case 3: User with Preferences + Feedback

This user has gone a step further—not only have they selected preferences, but they’ve also interacted with events (liked/bookmarked).

Expected Behavior: Go deeper into personalization, favoring items similar to the ones they liked.
What Happened:
Gorse delivered:

🧠 Personalized recommendations based on feedback
🎯 Events that matched the user’s preferences
🔥 A few popular picks to round it out

✅ Result: This is where Gorse really started to shine. The recommendations felt tailored—less generic, more “for you.”

Case 4: User with Preferences Matching a Default Profile

In this case, we tested something clever. Suppose a new user chooses preferences that exactly match a default profile (like category:3, district:2, budget:1000). What happens?

Expected Behavior: Gorse should surface events that the default profile has interacted with.
What Happened:

🪄 The system recommended items the default user had liked
🔥 Popular events still appeared, maintaining a balanced feed

✅ Result: It worked! By assigning new users to default profiles temporarily, we successfully guided their early experience—even before they gave feedback of their own.

Final Takeaway: Predictable, Yet Personalized

Across all four test cases, Gorse stayed within the expected result buckets. This predictability is actually a strength—it means we could anticipate what users would see, and tweak the system accordingly.

Each user got a recommendation feed tailored to where they were in their journey:

Just joined? → Show what’s hot and new
Picked preferences? → Show relevant and trending
Gave feedback? → Go deep with personalization
Matched a default user? → Use the default’s taste to guide them

This layered, responsive behavior gave us confidence that we were on the right track.

The Cold Start Challenge and Our Solution

Event Recommendation System-The Cold Start Challenge

One of the toughest problems in any recommendation system is what’s called the cold start. Imagine trying to recommend something to a user you know nothing about—no preferences, no history, no clicks. It’s like trying to buy a birthday gift for a total stranger. Tricky, right?

While popular and latest events can keep things somewhat interesting, they still feel… impersonal. And if your goal is relevance, that just doesn’t cut it.

So, we rolled up our sleeves and built a clever workaround.

The Idea: Default User Profiles with Specific Tastes

Instead of leaving new users adrift in a sea of generic suggestions, we decided to create several default user profiles, each one simulating a real user with specific preferences.

Think of them like curated personas:

Profile:c1-d3-budget1000
➤ Category 1 events
➤ In District 3
➤ Budget up to 1000
Profile:c2-d2-budgetFree
➤ Category 2 events
➤ In District 2
➤ Free events only

Each profile had a mix of labels and interacted with events accordingly. This gave Gorse just enough feedback to build a meaningful recommendation trail for that profile.

Matching New Users with Curated Profiles

Now, when a new user signs up and selects preferences, we don’t leave things to chance. Instead, we:

Check their selected labels (e.g., categories, districts, budget range)
Match them to a default profile with similar preferences
Assign the same label (like profile:c1-d3-budget1000) to the new user
Let Gorse treat them as if they were part of that default profile

📌 Result? The user sees events that feel relevant right from the start—no waiting, no randomness.

Letting Users Grow Into Their Own Profiles

Of course, this default association isn’t forever. As soon as a user begins to:

Bookmark events
Give feedback
Interact more actively

We gradually remove the default profile label, and let Gorse build a unique recommendation path based on their own behavior.

This way, users move smoothly from cold start to personalized experience, without ever feeling like the platform doesn’t “get them.”

Managing Complexity

Yes, the number of profile combinations can grow fast. But we made it manageable by:

Limiting the number of categories, districts, and budget tiers
Keeping profile labels simple and structured (like profile:c2-d1-budgetFree)
Automating the matching logic based on preference filters

In the End…

This small trick had a big impact. Instead of letting new users bounce off generic recommendations, we gave them a guided, relevant entry point. And when users feel seen from the start, they’re far more likely to stick around, explore, and engage.

Controlling the Cold Start Experience

Show how default profiles help control the initial recommendation experience.
Importance of ensuring these profiles engage with items in a balanced ratio.
Mention the trade-off: Too many profiles can become hard to manage.

Keeping It Scalable and Simple

Event Recommendation System-Keeping It Scalable

One of the first things we realized while creating default user profiles was this:
personalization is great, but if you overdo it—you’ll drown in complexity.

We could have created thousands of profile combinations.
Category × District × Budget = Whoa… too many!
That would’ve quickly become a maintenance nightmare.

So we made a strategic choice: Keep the personalization smart but simple.

Simplifying with Controlled Combinations

We decided to limit the values for each preference filter:

Categories → 4 to 5 broad categories
Districts → Grouped into key regions
Budget → Just a few practical ranges (e.g., Free, 0–1000, 1000–3000, 3000+)

By fixing these ranges, we dramatically reduced the number of possible combinations, without sacrificing much in terms of personalization.

Instead of trying to cover every possible user scenario, we focused on the most common and meaningful ones.

Reusable Default Profiles

Each resulting profile wasn’t just a one-time solution—it became reusable.

For example:
profile:c1-d3-budget1000 could be shared among hundreds of users who selected similar preferences. And each time Gorse refreshed recommendations for that profile, all linked users benefited.

This approach gave us the best of both worlds: ✅ Scale – manageable number of profiles
✅ Relevance – tailored starting recommendations

Why This Works

You don’t need infinite combinations to make people feel understood.
You just need smart groupings that reflect real user intent.

By thinking in terms of personas rather than individuals at first, we were able to:

Speed up onboarding
Deliver context-aware suggestions
Keep the system light, fast, and scalable

And once users started engaging on their own?
Gorse took over with real feedback-based recommendations. 🧠

Scalable Today, Flexible Tomorrow

If our user base grows or user behavior shifts, we can always:

Add new profile personas
Retune our category/district/budget clusters
Refresh feedback linked to existing profiles

This structure is not only simple now—it’s ready for evolution.
And that’s exactly what we wanted.

Conclusion: A Smart, Flexible, and Practical Recommendation System

What began as a goal to recommend upcoming events turned into a smart blend of strategy and simplicity.

By setting up Gorse with SQL and using the isHidden feature, we ensured only relevant future events were shown. We tapped into Gorse’s core strengths—user-based filtering, popular items, and new additions—without overcomplicating the system.

To address the cold start problem, we introduced default user profiles based on common preferences. This gave new users meaningful recommendations from the start, before they’d even interacted with the system.

The power of open-source truly shined. With thoughtful defaults, label logic, and feedback loops, we built a system that’s not just accurate but also scalable and easy to maintain.

Looking ahead, we’re exploring real-time label learning, UI improvements, and smarter feedback handling. But even now, this setup proves that personalized experiences don’t need complex AI—just a bit of planning and care for the user journey.

Disclaimer

*The views are of the author and not necessarily endorsed by Madgical Techdom