Tech Term Decoded: Similarity Learning

Definition

Similarity learning is a field of machine learning that has to do with training models to identify the similarity or dissimilarity between data points. Unlike traditional tasks, it is all about determining how alike or different two things are. It powers technologies like custom recommendation, image recognition, anomaly detection, etc. [1]

For instance, lets take a look at the following scenario;

A student in Nsukka suddenly starts using 15GB of data daily, as against the usual 2GB monthly usage.

An AI similarity learning system detects the following during its pattern analysis:

A massive data spike (750% increase)
The student’s campus location (university area)
Identifies the student as a previous light data user

The AI then compares its analysis to students with similar:

Sudden extreme data increases
Same campus location
Similar usage history
Same time period patterns

The AI then recognizes the following pattern in the similar cases:

70% were SIM card sharing/selling
20% were compromised accounts
10% were legitimate (project work, streaming)
Most occurred during exam periods

The AI system automatically gives the following smart responses:
Sends usage alert SMS
Temporarily applies fair usage policy
Offers data plan upgrade options
Flags account for verification call

The students confirms to the monitoring system that they were sharing SIM with roommates. The network offers family plan discount, converting to higher-value customers while preventing policy violations. In summary, the AI similarity learning system helped the telecom network distinguish between fraud, policy violations, and genuine usage growth among the students.

Similarity learning: identifying similarities and differences [2].

Origin

Similarity learning came into light as the result of the pursuit of infusing AI systems with human-like cognitive abilities, as well as the natural ability to see patterns and draw correlations between different datasets. Its origins can be traced back to the fundamental efforts to fill the gap between raw data and actionable intelligence, shaping the trajectory of AI advancements in diverse domains.

As AI technologies continued to develop, the integration of similarity learning was on the rise, propelling the refinement of algorithms and models. The integration of similarity learning with AI marked a significant transformation, enabling systems to understand and process data with increased precision and contextual relevance, thus amplifying their practical utility [3].

Context and Usage

The benefits of similarity learning are manifold; it enables machines to understand patterns, relationships, and structures within data, which is crucial for tasks like recommendation systems, face recognition, and anomaly detection.

Face recognition: Facial recognition is very important for security and authentication processes. Similarity learning plays a role in many security systems for face recognition by comparing the features of a face in an image with a database of known faces.

Product recommendations:  Similarity learning is used in e-commerce for product recommendations. With the help of similarity learning, when a user views a product they like, the system can suggest alternatives or products with similar characteristics that may interest them.

Anomaly detection: In the field of cyber security and finance, similarity learning is used to detect anomalies or extreme values in order to prevent incidents of fraud or breaches. Furthermore, in the field of medicine, similarity learning can be used for medical imaging to detect anomalies. Through the comparison of medical images, medical conditions and health problems can be detected early, and thus patients may have a better course of their disease [4].

Why it Matters

It matters because it enhances machine learning algorithm performance, efficiency, and interpretability while mitigating overfitting risks. Thanks to similarity learning, machines can better understand various patterns, relationships, and structures in data, enabling effective anomaly detection.

In Practice

A real-life case study of a company practicing similarity learning in AI can be seen in the case of Replika. Replika is an AI companion chatbot founded by Eugenia Kuyda with the sole purpose to helping you express yourself through conversations through which you safely share your thoughts, feelings, beliefs, experiences, memories, dreams, your private perceptual world. It listens and learns from your conversations, explores your personality and starts to replicate you.  And by so doing you see the chatbot as a friend.

See Also

Related Learning Approaches:

• Singularity: Hypothetical point when AI surpasses human intelligence across all domains
• Strong AI: Theoretical AI with human-level general intelligence across all domains
• Supervised Learning: Learning from labeled examples with known outcomes
• Symbolic AI: Using explicit knowledge representation and logical rules
• Zero-shot Learning (ZSL): Making predictions without training examples

References

1. Hankare, O. (2023). Exploring Similarity Learning.
2. Chakraborty, M. (2023). Spot The Difference: Can you spot 5 differences between the two images in 13 seconds?
3. Lark Editorial Team. (2023). Similarity Learning in Ai.
4. Big blue. (2023). Similarity Learning.
5. Replika. (2025). The AI companion who cares.