AI for networking: myths and reality

Indeed, many networking applications claim AI matchmaking or smart networking, but behind this there can be very different levels of real intelligence — from basic tag-based sorting to truly adaptive recommendations with elements of machine learning.

Let’s break it down by levels — from simple to advanced, with examples of possible formulas and logic.

🧩 1. "AI" at the level of filtering and tag intersection (90% of real cases)

How it works:

Participants specify interests, industry, position, goals (for example, "looking for partners in fintech").
The algorithm simply counts the number of matches by tags or categories.

The formula may be:

$$\text{score}(A,B) = \frac{|T_A \cap T_B|}{|T_A \cup T_B|}$$

where $T_A,\,T_B$ are the sets of interests of participants $A$ and $B$.

Next, sorting by score and presenting the top 5 recommendations

What is called "AI":
– "Semantic matching" (but in reality just synonymization of tags via Word2Vec or GloVe)
– "Interest similarity" (based on embeddings of interest words)

👉 Reality: the algorithm works reliably, but not “intelligently” — it simply intersects fields.

🤖 2. "Smart" algorithms based on embeddings (Embedding-based similarity)

How it is built:

Each participant is represented as a feature vector (skills, goals, profile description, company, topics).
The model (for example, Sentence-BERT) converts profile text into a numerical space.
Then the closest ones are found by cosine similarity.

Formula:

$$\text{similarity}(A,B) = \frac{v_A \cdot v_B}{\|v_A\| \, \|v_B\|}$$

(cosine similarity between embedding vectors)

Plus: You can find “semantically similar” people even if they do not use the same words.
Minus: Requires good-quality texts and computational resources.

🧠 3. "Behavioral AI-matching" (less common)

How it works:

The system collects user behavior: who they interacted with, who they liked, who they skipped, which meetings they confirmed.
Then it trains a recommendation model (like Netflix):
- Collaborative filtering (similar users — similar recommendations)
- Or Reinforcement learning (optimization based on response metrics)

Formula (example of matrix factorization):

$$\hat{r}_{ij} = p_i^{T} q_j$$

where $p_i$ is the preference vector of user $i$, and $q_j$ is the feature vector of another participant.

The higher $\hat{r}_{ij}$, the higher the “compatibility”.

Reality: A very rare case, because a lot of data is needed for this to work.

🎩 4. "Marketing magic"

Some platforms simply do:

A random selection from those who haven’t met you yet;
Or filter by city/industry;
And add “AI powered” to the feature description.

The check is simple:
– If there is no ability in the interface to specify goals/interests or no interaction data is collected — then the “AI” is fake.
– If recommendations are always generic and repetitive — most likely a regular tag-based filter.

💡 Examples of real platforms:

Платформа	Что заявляют	Что реально
Brella	AI matchmaking	Tag overlap + Semantic matching
Grip	AI-powered networking	Embeddings + Interest weighting
Swapcard	Smart recommendations	Tag matching + Simple ML weighting
MeetingMojo / Whova	Smart search	Фильтрация и сортировка
Zerista / Bizzabo	Intelligent matches	Смешанная система (теги + текстовый анализ)

AI-matching for conferences — technical documentation

Compiled from architecture, algorithm, and API descriptions demonstrating a realistic implementation of smart-matching for a conference application.

Summary

Levels of “AI” implementation: from simple tags to behavioral models.
Hybrid scoring formula and specific signals (tag, embedding, role, goal, behavior).
Implementation pseudocode and a numerical example of score calculation.
Database schema (PostgreSQL), tables and API endpoints.
Architecture: Matching Service, AI Layer (embeddings + FAISS) and caching.

1. Levels of “AI” in networking applications

Below is a summary of realistic levels found among vendors:

1. Tag filtering / intersection

One of the most common approaches: participants specify tags/interests; the platform counts matches (for example, Jaccard) and sorts by this metric.

Formula (Jaccard): S_tag = |T_A ∩ T_B| / |T_A ∪ T_B|

2. Embeddings and cosine similarity

Profiles are converted into a vector space (Sentence-BERT, USE) and nearest neighbors are found by cosine measure.

Formula: S_embed = (v_A · v_B) / (||v_A|| ||v_B||)

3. Behavioral models

They use interaction data (likes, meetings, views) and build collaborative filtering, matrix factorization, or graph embeddings.

Requires a sufficient volume of data, so it is less common.

4. Marketing “AI magic”

Some platforms simplify — they use randomization or pure filtering, but label the feature as “AI”. Verify via UI and the presence of text signals/behavior data.

2. Hybrid scoring formula (concept)

Combine signals with weights. General formula:

$$ \text{score}(A, B) = \sigma \big( w_1 \cdot S_{\text{tag}} + w_2 \cdot S_{\text{embed}} + w_3 \cdot S_{\text{role}} + w_4 \cdot S_{\text{goal}} + w_5 \cdot S_{\text{behavior}} - w_6 \cdot C_{\text{conflict}} \big) $$

Here σ is a normalization function (for example, sigmoid) to bring the result into (0,1).

Signals (description)

$S_{\text{tag}}$ — similarity by tags (Jaccard or weighted overlap);
$S_{\text{embed}}$ — cosine similarity of embeddings (normalized to [0,1]);
$S_{\text{role}}$ — role compatibility matrix (for example, founder ↔ investor = 0.9);
$S_{\text{goal}}$ — goal match (looking for / offering);
$S_{\text{behavior}}$ — collaborative signal (who liked/scheduled meetings with similar users);
$C_{\text{conflict}}$ — penalties (for example, same email/company → exclusion or strong penalty);

3. Specific signal formulas

3.1 Tag similarity (Jaccard)

$$S_{\text{tag}}(A, B) = \frac{|T_A \cap T_B|}{|T_A \cup T_B|}$$

3.2 Embedding similarity (cosine)

$$S_{\text{embed}}(A, B) = \frac{v_A \cdot v_B}{\|v_A\| \, \|v_B\|}$$

Value in [-1,1] → normalized to [0,1] as (x+1)/2.

3.3 Role compatibility

Role compatibility matrix: a predefined table of values in the range [0,1].

3.4 Goal match

Simple logic for goal matching (for example, “looking for partners” vs “offering partnership” → 1.0).

3.5 Behavioral signal

Example: using information about which profiles similar users connected with.

Simple signature: $$S_{\text{behavior}}(A, B) = \frac{\#\,\text{users similar to } A \text{ who connected to } B}{\#\,\text{users similar to } A}$$

4. Normalization and combination

Each signal is normalized to [0,1]. Then we combine them with weights and apply sigmoid normalization:

$$ \text{raw} = \sum_i w_i S_i - w_6 C $$

$$ \text{score} = \text{sigmoid}\left( \alpha \cdot (\text{raw} - \beta) \right) $$

5. Numerical example

Let:

$T_A = \{\text{fintech}, \text{payments}, \text{API}\}$
$T_B = \{\text{payments}, \text{banking}\}$
$S_{\text{tag}} = \frac{|T_A \cap T_B|}{|T_A \cup T_B|} = \frac{1}{4} = 0.25$
$\text{cosine}(v_A, v_B) = 0.6 \Rightarrow S_{\text{embed}} = \frac{0.6 + 1}{2} = 0.8$
$S_{\text{role}} = 0.7,\; S_{\text{goal}} = 1.0,\; S_{\text{behavior}} = 0.0,\; C_{\text{conflict}} = 0$

Weights: $w_{\text{tag}}=0.15,\; w_{\text{embed}}=0.35,\; w_{\text{role}}=0.15,\; w_{\text{goal}}=0.25,\; w_{\text{behavior}}=0.05$

\[ \text{raw} = 0.15 \cdot 0.25 + 0.35 \cdot 0.8 + 0.15 \cdot 0.7 + 0.25 \cdot 1.0 + 0.05 \cdot 0 = 0.6725 \] \[ \text{score} = \sigma(8 \cdot (0.6725 - 0.5)) \approx \sigma(1.38) \approx 0.8 \]

Result: B is an excellent candidate ($\text{score} \approx 0.8$).

6. Practical tips and improvements

Cold start: popular profiles + content-only signals.
Weight training: A/B testing and supervised learning (logistic regression, LightGBM) on usefulness labels.
Speed: precompute embeddings, ANN (FAISS/pgvector) → candidate generation + re-rank.
Interpretability: show match reason in UI (common tags, goal alignment).
Privacy: options to “opt-out”, explicit indication of which data is used.

7. API endpoints (REST)

GET /api/users/{id} — profile and tags.
POST /api/users — create/update profile (triggers embedding generation).
GET /api/matching/{user_id} — returns top‑N recommended matches (refresh parameter for recalculation).
GET /api/matching/search — search candidates by filters (tags, roles, location).
POST /api/interactions — register like/skip/meeting_confirmed (feedback to the model).

8. Architecture and data flow

In short: Frontend ↔ API Gateway ↔ Matching Service ↔ Database / AI Layer / Cache.

Architecture diagram

Updated flowchart: Matching Service, AI Layer (embeddings + ML), DB, Cache.

9. Background ML pipeline

daily_batch.py — collects interactions, updates latent vectors.
retrain_embeddings.py — regenerates embeddings after model changes.
generate_faiss_index.py — builds ANN index for fast top‑N queries.

10. MVP and scaling recommendations

Start with tags + Jaccard → add embeddings (Sentence-BERT) → ANN (FAISS) + re-rank.
Cache matches for 1–3 hours to reduce load.
For 50k users — pgvector + Redis; >100k — FAISS/Milvus.

And the format if we were a regular service for marketers and general users:

data collection

Collecting participant interests and preferences

To generate relevant profile recommendations, a large volume of data is required. Participants naturally provide this data by filling out their profiles and simply using the platform.

Participants visit each other's profiles and connect via messages and meeting requests
This leaves a trail of connections and creates a complex network of participant interactions
Along with their profile data, this information is continuously fed into our machine learning algorithm in near real-time

data analysis

Understanding participant needs with machine learning

As it collects data, the algorithm processes it simultaneously. This continuous cycle allows it to determine what interests each participant.

The algorithm constantly analyzes participant behavior and their profile information
This is the interpretation of data to understand the interests and goals of each participant
Once understood, it can predict which profiles may be interesting to the participant

recommended profiles

Providing relevant profile recommendations

Once recommendations are generated, they can be presented to participants. This stimulates engagement and interaction within your networking event.

Each participant sees a different set of profile recommendations based on their interests
As the algorithm continues to process data, the recommendations improve
Participants can mark some recommendations as irrelevant, which in turn helps the algorithm
The more active the participant, the better the recommendations

Preparing for the event

Ticket sales

On the day of the event

After the event