Dataset Viewer
Auto-converted to Parquet Duplicate
actor_type
string
is_agent
int64
record_type
string
topic
string
source_session_dir
string
source_file
string
source_row_index
int64
ingest_format
string
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string
experimentId
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storeMode
string
ts
string
eventName
string
page
string
productId
string
price
float64
userAgent
string
kafka_partition_id
float64
kafka_offset
float64
kafka_timestamp_ms
float64
kafka_compression
string
kafka_is_transactional
bool
kafka_headers
string
kafka_key_payload
string
kafka_key_encoding
string
kafka_key_schema_id
float64
kafka_value_encoding
string
kafka_value_schema_id
float64
kafka_value_size
float64
metadata_cabinClass
string
metadata_dateIndex
float64
metadata_dwellTime
float64
metadata_elementText
string
metadata_fareRule
string
metadata_flightType
string
metadata_itemCount
float64
metadata_nights
float64
metadata_price
float64
metadata_referrer
string
metadata_roomType
string
metadata_total
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metadata_type
string
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End of preview. Expand in Data Studio
PHANTOM research banner

whoclickedit

Dataset on HF Rows Columns Sessions Human rows Agent rows License

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

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