Recommendation Systems Playbook

Step-by-Step User Event Recommendation Execution Flow

A progressive disclosure blueprint for delivering personalized event nudges with traceable logging, vector-based matching, and configurable guardrails. Use it to stand up a production-ready engine that balances intelligence, transparency, and psychological resonance.

Updated: October 22, 2025 Reading Time: 11 minutes Author: Christopher Manuel Cruz-Guzman

Open the Execution Map Jump to Code References

Why this flow matters

Push notifications succeed when the orchestration is as intentional as the copy. This walkthrough breaks the recommendation lifecycle into discrete checkpoints—from validating inputs to closing the loop on logging—so product and engineering teams can adopt it without guesswork.

Each stage below opens as you need it, keeping strategic intent visible while hiding implementation detail until you are ready. That is progressive disclosure applied to system design: clarity up front, depth on demand.

Vector Personalization Quality Control Psychology-Driven Nudges Full-Funnel Logging

Execution Blueprint

Progressive Disclosure Timeline

Expand each checkpoint to understand its goals, implementation notes, and fallback paths. Collapse when finished to keep your focus on the stages ahead.

01. Input Validation & Initial Logging ➜

Receive user and event identifiers, record the timestamp, and confirm both IDs match expected formats before any downstream work.

Capture structured logs with function name, parameters, and start status.
Reject early if IDs are malformed to preserve compute and maintain traceability.

Input: user_id, event_id, request_metadata

Output: Sanitized identifiers plus a log reference that traces the execution journey.

payload = {"user_id": user_id, "event_id": event_id}
validate_uuid(payload["user_id"])
validate_uuid(payload["event_id"])

log_ref = audit_logger.start(
    "user_event_reco",
    context={**payload, "request_metadata": request_metadata},
)
return payload, log_ref

02. User Profile Lookup ➜

Pull the full behavioral, preference, and social graph to fuel personalization.

Load profile basics, historic events, interaction patterns, and notification preferences.
Gracefully handle missing users by raising a UserNotFoundError.

Input: Sanitized user_id plus the requested profile_fields from configuration.

Output: Complete user profile document hydrated with behavioral and preference signals.

profile_fields = [
    "basics",
    "interaction_history",
    "preferences",
    "notification_settings",
]
user_profile = user_store.fetch(user_id=payload["user_id"], fields=profile_fields)

if user_profile is None:
    raise UserNotFoundError(payload["user_id"])

return user_profile

03. User Vectorization Check ➜

Confirm an embedding exists before continuing. If absent, pivot into vector generation.

Refresh the profile after vectorization so downstream logic uses the latest data.

Input: Hydrated user_profile plus vector freshness policy (vector_ttl).

Output: Tuple showing the current embedding (if any) and whether regeneration is required.

existing_vector = user_profile.get("vector")
vector_is_stale = vector_ttl.is_expired(user_profile.get("vector_timestamp"))

needs_regeneration = existing_vector is None or vector_is_stale
if needs_regeneration:
    audit_logger.info("vectorization.required", {"user_id": user_profile["id"]})

return existing_vector, needs_regeneration

04. User Vectorization Process ➜

Create a narrative that blends demographics, interests, spending signals, and history to feed the embedding model.

Generate the vector with text-embedding-3-large (or your selected provider).
Persist the vector back into the behavioral profile and log dimensionality.

Input: user_profile, regeneration flag, and embedding model configuration.

Output: Fresh user_embedding saved to persistence with dimensionality metadata.

profile_narrative = render_profile_narrative(user_profile)
embedding_response = embedding_client.embed(
    model="text-embedding-3-large",
    input=profile_narrative,
)
user_embedding = embedding_response["vector"]

user_store.save_vector(user_profile["id"], user_embedding)
audit_logger.info(
    "vectorization.completed",
    {"user_id": user_profile["id"], "dimensions": len(user_embedding)},
)

return user_embedding

05. Event Data Retrieval ➜

Load venue insights, participant counts, and existing event vectors to compare against user intent.

Short-circuit with EventNotFoundError when details are missing.

Input: Validated event_id plus requested event_fields.

Output: Event record enriched with vector data, venue insights, and logistics metadata.

event_fields = ["details", "schedule", "pricing", "vector", "host_reputation"]
event_record = event_store.fetch(event_id=payload["event_id"], fields=event_fields)

if event_record is None:
    raise EventNotFoundError(payload["event_id"])

return event_record

06. Vector Similarity Calculation ➜

Compute a compatibility score between user and event vectors. Normalize results to a 0–1 scale.

Log the raw score for analytics and A/B tuning.

Input: user_embedding, event_vector, and a normalization_strategy.

Output: Normalized compatibility score ready for threshold evaluation.

user_vector = user_embedding
event_vector = event_record["vector"]

raw_score = cosine_similarity(user_vector, event_vector)
compatibility_score = normalization_strategy.to_unit_interval(raw_score)

metrics.log(
    "compatibility.score",
    compatibility_score,
    extra={
        "user_id": payload["user_id"],
        "event_id": event_record["id"],
        "raw_score": raw_score,
    },
)

return compatibility_score

07. Compatibility Threshold Check ➜

Compare the score to configurable modes (strict, moderate, relaxed, discovery) or custom thresholds.

Return a "no recommendation" response if the match misses the bar and document the reason.

Input: compatibility_score, user segment data, and threshold configuration.

Output: Boolean gate indicating if the recommendation proceeds, plus the applied threshold metadata.

threshold = threshold_resolver.resolve(
    mode=config.threshold_mode,
    user_segment=user_profile.get("segment"),
)

meets_bar = compatibility_score >= threshold.min_score

if not meets_bar:
    audit_logger.info(
        "recommendation.skipped",
        {
            "reason": "score_below_threshold",
            "score": compatibility_score,
            "min_score": threshold.min_score,
        },
    )

return meets_bar, threshold

08. User Data Sufficiency Assessment ➜

Score profile completeness to decide between high, moderate, or generic personalization paths.

Fallback to discovery templates when the data score falls below the threshold.

Input: user_profile signals and configured completeness_weights.

Output: Personalization tier label plus the underlying completeness score.

data_score = completeness_scoring(user_profile, weights=completeness_weights)

if data_score >= 0.8:
    personalization_tier = "high"
elif data_score >= 0.5:
    personalization_tier = "moderate"
else:
    personalization_tier = "generic"

audit_logger.info(
    "personalization.tier",
    {"tier": personalization_tier, "score": round(data_score, 3)},
)

return personalization_tier, data_score

09. Nudge Type Determination ➜

Use social context, scarcity triggers, venue reputation, and compatibility strength to pick a psychology-backed nudge.

Available options: friend interest, scarcity alert, host reputation, perfect match, similar taste.

Input: personalization_tier, compatibility_score, user social graph, and event metadata.

Output: Selected nudge_type aligned to the most persuasive psychology angle.

signals = gather_nudge_signals(
    user_profile=user_profile,
    event=event_record,
    compatibility=compatibility_score,
    tier=personalization_tier,
)

nudge_type = select_nudge_template(signals)
audit_logger.info("nudge.selected", {"nudge_type": nudge_type, "signals": signals})

return nudge_type

10. Context Generation ➜

Assemble the narrative ingredients—preferences, event details, compatibility, personalization level—using configurable templates.

Input: nudge_type, personalization_tier, user_profile, event_record, and compatibility_score.

Output: Structured context payload feeding the copy generator.

context_payload = build_context_payload(
    nudge_type=nudge_type,
    user=user_profile,
    event=event_record,
    compatibility=compatibility_score,
    personalization_tier=personalization_tier,
)

context_payload["template"] = context_templates.resolve(nudge_type, personalization_tier)

return context_payload

11. Message Generation ➜

Create the notification with creativity and tone parameters while honoring length limits.

Regenerate with adjusted creativity when quality checks fall below minimum thresholds.

Input: context_payload, copywriting configuration, and channel constraints.

Output: Draft message text plus generation metadata for audits.

message, generation_metadata = notification_generator.render(
    template=context_payload["template"],
    context=context_payload,
    creativity=config.copy.creativity,
    tone=config.copy.tone,
    max_chars=config.copy.max_chars,
)

if generation_metadata["quality"] < config.copy.minimum_quality:
    message, generation_metadata = notification_generator.render(
        template=context_payload["template"],
        context=context_payload,
        creativity=config.copy.creativity - 0.1,
        tone=config.copy.tone,
        max_chars=config.copy.max_chars,
    )

return message, generation_metadata

12. Message Quality Validation ➜

Run grammar, relevance, and appropriateness checks. Only approve messages that meet configured scores.

Input: Draft message, context_payload, and quality guardrail configuration.

Output: Quality report with pass/fail status and scoring breakdown.

quality_report = quality_gate.evaluate(
    message=message,
    context=context_payload,
    rules=config.quality.rules,
)

if not quality_report.passed:
    raise MessageRejectedError(quality_report.reasons)

return quality_report

13. User Interaction History Update ➜

Persist notification content, nudge type, and metadata to both user behavior and centralized notification stores.

Track psychology principle usage for future targeting experiments.

Input: Final message, nudge_type, quality_report, and identifiers for the user and event.

Output: Durable interaction log entry synced across behavioral and notification systems.

interaction_record = {
    "user_id": payload["user_id"],
    "event_id": event_record["id"],
    "nudge_type": nudge_type,
    "message": message,
    "quality": quality_report.scores,
    "sent_at": utcnow(),
}

history_repo.append(interaction_record)
notification_log.store(interaction_record)

return interaction_record

14. Final Logging & Return ➜

Record execution completion, timing metrics, and outputs. Return structured results for downstream analytics.

Input: interaction_record, quality_report, log_ref, and collected execution metrics.

Output: Final response payload for API consumers alongside closed-out observability trails.

final_response = {
    "user_id": payload["user_id"],
    "event_id": event_record["id"],
    "nudge_type": nudge_type,
    "message": message,
    "quality": quality_report.scores,
    "personalization_tier": personalization_tier,
    "compatibility_score": compatibility_score,
    "context": context_payload,
}

audit_logger.complete(log_ref, outcome="success", result=final_response)
timing_metrics.record(execution_start, "recommendation_flow")

return final_response

Configuration Levers

What you can tune without code changes

Template and threshold controls make the engine adaptable across acquisition, retention, or discovery campaigns.

Threshold configuration with strict/moderate/relaxed/discovery modes or per-campaign overrides.

Template families aligned to nudge psychology (friend interest, scarcity, host reputation, perfect match, similar taste).

Quality control settings: minimum scores, regeneration attempts, and creativity/tone adjustments.

Logging toggles for inputs, outputs, execution timing, and sensitive data masking.

Alternative Protocols

Sometimes the smartest recommendation is knowing when to pause. Two resilient fallbacks keep the UX sharp:

Insufficient User Data: pivot to generic discovery templates, emphasize venue credibility, and invite profile completion.
Low Compatibility: return a no-recommendation response, log the cause, and optionally trigger a nudge to enrich profile data.

Reference Implementation

Core orchestration function

Use this async workflow as your baseline. It strings together the entire execution flow while emitting observability breadcrumbs.

async def generate_user_event_recommendation(user_id, event_id, config):
    execution_start = datetime.now()
    try:
        log_function_execution(..., {"status": "started", "timestamp": execution_start})
        user_profile = await fetch_user_profile_complete(user_id, [...])
        if not user_profile:
            raise UserNotFoundError(...)
        if not user_profile.behavioral_data.get('user_vector'):
            await vectorize_user(user_id, user_profile)
            user_profile = await fetch_user_profile_complete(user_id, ["behavioral_data"])
        event_data = await fetch_event_details_with_venue(event_id, include_participants=True)
        if not event_data:
            raise EventNotFoundError(...)
        compatibility_score = calculate_user_event_compatibility(
            user_profile.behavioral_data.user_vector,
            event_data.event_vector
        )
        min_threshold = config.get('compatibility_threshold', 0.7)
        if compatibility_score < min_threshold:
            return RecommendationResult(success=False, reason="compatibility_below_threshold", ...)
        nudge_type = determine_optimal_nudge_type(user_profile, event_data, compatibility_score)
        personalization_level = assess_user_data_sufficiency(user_profile)
        if personalization_level == "insufficient":
            nudge_type = "generic_discovery"
        context = await assemble_message_context(user_id, event_id, "recommendation")
        context.compatibility_score = compatibility_score
        context.personalization_level = personalization_level
        message_result = await generate_personalized_message_with_template(nudge_type, context, config)
        await update_user_message_history(user_id, event_id, nudge_type, message_result)
        result = RecommendationResult(success=True, message=message_result.content, ...)
        log_function_execution(..., result.to_dict(), {"status": "completed", "execution_time_ms": result.execution_time_ms})
        return result
    except Exception as e:
        log_function_execution(..., {"error": str(e)}, {"status": "failed", "error_type": type(e).__name__})
        raise

Supporting Components

Functions that power the engine

Keep these helpers modular so teams can swap models, logging providers, or persistence layers without rewriting orchestration.

Vectorize

vectorize_user crafts a natural-language narrative, requests embeddings, persists vectors, and logs the dimensionality.

Threshold

check_compatibility_threshold respects strict/moderate/relaxed/discovery presets or campaign overrides.

Nudge

determine_optimal_nudge_type picks the right psychology lever—social proof, scarcity, reputation, or perfect match—before defaulting to discovery.

Assess

assess_user_data_sufficiency scores profile richness across preferences, behavior, and social context to set personalization tiers.

Generate

generate_personalized_message_with_template pairs context-aware prompts with validation loops until quality benchmarks are met.

Log

update_user_message_history archives every interaction, nudge type, and psychology principle for future experimentation.

Error handling that preserves trust

Design guardrails for the inevitable outages and edge cases:

User Not Found: stop execution, log the failure, and surface an actionable error upstream.
Event Not Found: apply the same rigor—log, exit, and avoid phantom notifications.
Vectorization Failure: gracefully fall back to rule-based matching and simplified scoring while flagging the incident for retraining review.