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Link session items

network · basket analysis · association rules

Link items (e.g. products) in sessions (baskets) if one item makes the presence of the other in the same session more likely.

A link (or association) A->B is created between items A and B if the presence of A makes the presence of B in the same session N times more likely.

For further details about the algorithm see e.g. association rule learning.

Usage


The following are the step's expected inputs and outputs and their specific types.

Step signature
link_session_items(
    items: category|number,
    sessions: category, 
    {
        "param": value
    }
) -> (targets: list[number], weights: list[number])

where the object {"param": value} is optional in most cases and if present may contain any of the parameters described in the corresponding section below.

Example

The following call creates links between pairs of items A and B, if:

  • A occurs in at least 7 sessions
  • B occurs in at least 25% of sessions containing A
  • The presence of A in a session makes the presence of B in the same session at least twice as likely.

Note that the last condition is equivalent to saying that the overall frequency of B in all sessions must be less than 12.5% (half of 25%). In other words, a minimum lift of 2 means that the frequency of B, in sessions already containing A, must be twice the background frequency of B in general.

As an example, the percentage of shopping baskets containing milk (item B) may be 10%. However, amongst those baskets already containing cereals, the percentage containing milk is likely to be higher. If milk occured e.g. in 30% of baskets also having cereals, than the lift of the rule cereal->milk would be 3. The buying of cereal make the buying of milk 3 times more likely.

Example call (in recipe editor)
link_session_items(items.id, sessions.item_ids, {
  "min_support": 7
  "min_confidence": 25
  "min_lift": 2
}) -> (items.targets, items.weights)

Inputs


items: column:category|number

A column containing the IDs of items to analyze.


sessions: column:category

A column containing lists of IDs corresponding to items in the same sessions, basket etc.

Outputs


targets: column:list[number]

A column containing for each item a list of IDs (row numbers) identfying other items it will be linked to.


weights: column:list[number]

A column containing for each item a list of weights identfying the "importance" of each link to other items identified in the targets column.

Parameters


min_support: number | integer = 10

Minimum Support. Minimum support of a rule antecedent. If it is < 1 it will be taken as a proportion. In any other case it will be expected as a positive integer representing the count. Create link A->B only if A occurred in at least this many sessions.


min_confidence: number = 20

Minimum Confidence. Expressed as a rule as a percentage. Include link A->B only if B occurred in at least this percentage of sessions also containing A.

Range: 0 ≤ min_confidence ≤ 100


min_lift: number | null

Minimum Lift. Expressed as multipler/ratio. Include link A->B only if A makes the presence of B in the same sessions at least this many times more likely.


weight_metric: string = "rule_lift_pct"

Metric for link weight.

Must be one of: "itemset_support_abs", "itemset_support_pct", "filter_metric_abs", "filter_metric_pct", "antecedent_support_abs", "antecedent_support_pct", "consequent_support_abs", "consequent_support_pct", "rule_confidence_pct", "rule_lift_abs", "rule_lift_pct"

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