How Regionalization-Based Recommendations Can Improve Your Operations

Rodrigo Alves

Jul 16, 2025

From ancient trade routes to modern urban planning, geography has consistently shaped human decisions and opportunities. Today, in the world of online business and personalized recommendations, geography remains equally influential, even though the effects aren’t always immediately obvious. At Recombee Research, we believe in the potential of location-based recommendation methods and prioritize their development. In this post, we will discuss how identifying and understanding geographic regions using your recommender system can significantly enhance your operational strategies.

Whether you’re running a streaming platform, curating local events or tailoring a marketing campaign, location-tailored content can be a game-changer. Audiences in different areas often exhibit distinct tastes (what captures attention in one city might not be the same in another) so integrating regional insights into your recommendation engine can boost relevance and engagement. For instance, by suggesting recipes that resonate with local culinary traditions, region-aware recommendations let you meet users (customers) where they are (literally) and offer them what they truly want. Note that, however, if the recommender isn’t calibrated correctly, globally popular items can drown out local content, depriving regional providers of the visibility they deserve and users of the locally relevant choices they expect.

Our recent research introduces a practical and efficient method for incorporating geographical data into recommendations. Using regionalization (spatial clustering) techniques, this approach effectively uncovers meaningful geographic patterns in user preferences and behaviors. These insights can then be applied to personalize content curation, recommend local events that truly resonate with each community, tailor marketing campaigns to regional trends, and deepen the overall understanding of how geography shapes user engagement.

Why Does Location Matter?

Consider a streaming platform looking to refine its content strategy. Regionalized recommendation analysis may reveal that one micro-region is embracing short-form cooking videos, something not yet picked up in global trends, while another area shows growing enthusiasm for interactive live streams and e-sports content. By detecting these nascent trends in your recommender system’s interaction logs, you can, for instance, boost engagement by offering the most relevant titles and deliver a more personalized experience that feels tailor-made for local audiences.

The same principle applies, for instance, when curating and promoting local events. Geographic recommendation techniques can uncover neighborhoods where demand for indie music nights, pop-up art shows, or wellness workshops is on the rise. By surfacing these findings to the right user clusters you drive higher attendance, strengthen community bonds, and give event organizers the insights they need to allocate, for instance, marketing budgets most effectively.

Common Challenges in Identifying and Understanding Regions in Recommenders

Recommendation systems often face two main challenges:

• Sparse Data: In many cases, there is insufficient data from individual regions to clearly identify user preferences.
• Effective Region Grouping: Unsupervised learning methods (such as clustering) are inherently difficult to evaluate. It can be challenging to define geographical clusters that truly reflect customer behavior, rather than relying on arbitrary or misleading divisions.

Our Solution

Imagine a city where restaurants in different neighborhoods have distinct tastes: e.g., downtown users favor trendy vegan spots, while suburban customers lean toward hearty, family-style restaurants. Because these groups rarely overlap in their restaurant interactions, there’s no direct data linking their preferences, and many smaller or emerging neighborhoods simply don’t generate enough feedback to learn reliable local signals. Our solution tackles this by identifying macro-regions, spatial clusters of areas with similar latent user affinities, and pooling their interaction data. By aggregating sparse signals across each macro-region, even the least active zones inherit robust, region-aware insights, yielding far more reliable, personalized recommendations for every user.

Our method was engineered to exactly explore this scenario. First we use Inductive Matrix Factorization (IMF) to combine two sources of data:

1. Customer interaction data: the matrix of what people, for instance, have rated, ordered, or clicked on.
2. Geographic information: a mapping from items (e.g. restaurants) to regions (e.g. neighborhoods or delivery zones).

At its core, a recommender system often relies on matrix factorization: learning two low-dimensional matrices (one for users, one for items) whose product approximates the user-item interaction matrix X.

But when items are sparsely rated, the system struggles. To overcome it, we replace the learned item matrix with known features (like a one-hot encoding of the item’s region). That is, instead of learning all features of an item from data, we inject side information. The result is:

X ≈ M Y

• X is the observed interaction matrix (users × items),
• Y is the binary matrix that maps items to regions (the item geo-location),
• M is what we learn: a matrix of users × regions.

Each row of M represents a user’s latent affinity for each region. It's like building a profile of how likely a person is to enjoy products from any given area, even if they haven’t rated any items in that region directly. However, even in this case, some regions often remain too sparse to provide useful collaborative filtering signals.

From Preferences to Patterns: Clustering Regions

As discussed above, once we’ve built M, each column represents a region. This means we can:

1. Visualize how customer preferences vary geographically.
2. Cluster similar regions based on their latent profiles.

In practice, this led us to discover surprising groupings, such as:

• Inner-city districts with university campuses clustered with young, tech-savvy suburbs.
• Affluent residential areas aligned with certain rural pockets (both preferring premium and organic goods).
• Tourist-heavy zones and central business districts formed a “fast-paced” macro-region.

Therefore, by clustering regions (using M) into these broader macro-regions, fast-paced urban cores, student-centric districts, premium-oriented suburbs, or tourist hubs, we gain two key advantages. First, we surface stronger, more reliable patterns of local preference by pooling data across similar areas. Second, we overcome sparsity in any one neighborhood: even if a particular zone has few direct interactions, its membership in a well-defined macro-region lets us borrow insights from fellow regions with richer feedback. The result is a recommendation engine that delivers highly relevant, geographically aware suggestions for every user, no matter where they live. In technical terms, we construct an updated Y_Macro matrix that encodes each item by its macro-region membership rather than its original fine-grained zone. This macro-region-based Y_Macro (items × macro-regions) both reduces dimensionality and amplifies sparse signals across similar areas. We then refactorize X as

X ≈ M_macro × Y_macro

where M_macro (users × macro-regions) captures each user’s latent affinity profile at the macro level. By leveraging these aggregated region features, your recommender naturally delivers robust, geographically aware suggestions in both dense urban cores and sparsely populated zones.

For more details on Inductive Matrix Factorization, see our detailed post. For regionalization in recommenders, check out our paper, and feel free to reach out for a deeper conversation.

What Did We Find?

Our extensive experiments using both simulated data and real-world datasets (from Google Locals dataset), particularly in the context of restaurants, yielded compelling results:

• Accurate Region Identification: We successfully identified distinct geographic clusters, clearly separating items and behaviors critical for strategic product placement and marketing.
• Robust Predictions: Recommendations based on regional clusters significantly outperformed traditional methods, demonstrating reliable accuracy even when data was limited or incomplete.
• Actionable Insights: The method effectively revealed regional trends and preferences, enabling businesses to make informed decisions.

…and what are the real-world benefits?

Adopting our regionalization method can help your business:

• Enhance Customer Satisfaction: Deliver faster and more relevant recommendations by aligning product offerings with local interests and upcoming trends.
• Personalize Content Curation: Deliver region-tailored articles, videos, or playlists that resonate with local tastes, boosting click-through and engagement rates.
• Increase Event Attendance: Surface concerts, meetups, and festivals most likely to appeal to each community, driving higher ticket sales and word-of-mouth.
• Enhance User Retention: Keep audiences coming back by consistently suggesting experiences and products that feel made for their region.

Final Thoughts

Integrating geography into your recommendation and business strategy is both practical and impactful. Employing regionalization can not only improve operational efficiency but also provide valuable insights into customer preferences, helping businesses spot trends early and adapt accordingly.

Recombee Research is committed to advancing geography-based recommendation methods, incorporating interaction data while exploring additional data sources.

Stay updated by following us on social media; exciting developments are on the way! If you're interested in our research or wish to collaborate, please reach out to us.

References

Alves, Rodrigo. "Regionalization-based Collaborative Filtering: Harnessing Geographical Information in Recommenders." ACM Transactions on Spatial Algorithms and Systems 10.2 (2024): 1-23.