whoclickedit

Event-level behavior data for dynamic pricing research. This dataset captures how humans and automated agents browse, query prices, and move through the PHANTOM storefronts during controlled experiments.

What this dataset gives you

• A single flat file (whoclicked.csv) with both interaction and price-log events.
• Explicit labels for actor origin: actor_type and is_agent.
• Provenance fields from Kafka envelopes when available.
• Metadata flattened into feature-ready metadata_* columns.

Snapshot

Metric	Value
Rows	3874
Columns	42
Time range (UTC)	2025-12-05T09:43:31.301000+00:00 -> 2026-03-23T12:08:30.151000+00:00
Unique sessions	36

Composition

Rows by actor

Actor	Rows	Share
human	798	20.6%
agent	3076	79.4%

Rows by actor and record type

Actor	Record type	Rows
agent	interaction	197
agent	price_log	2879
human	interaction	328
human	price_log	470

Store mode coverage

Store mode	Rows
hotel	3628
airline	196
shop	50

Top interaction events

Interaction event	Count
page_view	246
learn_more_about_item	91
view_item_page	88
add_item_to_cart	47
hover_over_title	23
checkout_start	20
hover_over_paragraph	6
remove_item	4

Collection pipeline

Data is sourced from two roots inside PHANTOM:

• experiments/collected_data (human sessions)
• experiments/agents/collected_data (agent sessions)

Each session directory contains:

• int.json: user interaction events
• price.json: price quote observations

ETL behavior:

1. Accepts both Kafka-envelope records and flat payload records.
2. Flattens nested JSON to a tabular schema.
3. Preserves row-level provenance (source_session_dir, source_row_index, topic fields).
4. Adds modeling labels (actor_type, is_agent, record_type).

Schema highlights

Core modeling fields:

• actor_type, is_agent, record_type
• sessionId, experimentId, storeMode, ts
• eventName, page, productId, price, userAgent

Kafka provenance fields:

• kafka_partition_id, kafka_offset, kafka_timestamp_ms, kafka_compression
• kafka_is_transactional, kafka_headers, kafka_key_*, kafka_value_*

Metadata columns in this release

• metadata_cabinClass
• metadata_dateIndex
• metadata_dwellTime
• metadata_elementText
• metadata_fareRule
• metadata_flightType
• metadata_itemCount
• metadata_nights
• metadata_price
• metadata_referrer
• metadata_roomType
• metadata_total
• metadata_type

Quick start

from datasets import load_dataset

ds = load_dataset("velocitatem/whoclickedit")

Recommended split strategy:

• Prefer session-aware or time-aware splits.
• Do not split rows from the same sessionId across train and test.

Intended use

• Human-vs-agent behavior classification.
• Session-level telemetry modeling for dynamic pricing defenses.
• Robustness experiments under agent-mediated reconnaissance.

Safety and limitations

• userAgent and referrer metadata can be quasi-identifying in very small samples.
• Data comes from a controlled research platform, not a full production marketplace.
• Current release has stronger coverage for hotel flows than airline flows.

Citation

If you use this dataset, cite the PHANTOM thesis project and link this page: https://huggingface.co/datasets/velocitatem/whoclickedit