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model_settings.yml
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model_settings.yml
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input_items: ''
input_sessions: ''
model:
number_of_recommendations: 10
# Sessions parameters
number_of_neighbors: 100
# - random: select random subset of sessions,
# - recent: select most recent sessions,
# - common_items: select sessions that contain any item from the recommended session,
# - weighted_events: select sessions based on the specific weights assigned to events.
sampling_strategy: "recent"
sample_size: 1000
# Required event (below) is an optional parameter. You may set it if you want to force algorithm to choose sessions
# with, for example, "purchase", "add to cart" or 33 event types.
required_sampling_event: null
# Items parameters
# - linear: basic linear weighting, distances between events in a sequence are equal and depended only on the
# position of element in a sequence, always gives larger weights than the other methods,
# - log: oldest elements starts from the larger weights than in the other methods. Then function returns very
# similar weights, from approx. 20-90% of the sequence and the newest 90-100% of the events sharply
# rose. It is good to mimic a short-term memory.
# - quadratic: the oldest elements are penalized much more than the middle-part and the newest elements in a
# sequence. Function rises quicker than the log10 function but values are smaller than returned from
# the linear function. Newest observation -> larger weight assigned to it.
weighting_func: "linear"
# - linear: simple linear function - up to 10 positions it is 1 - 0.1 * position normalized to [0:1], and 0 if
# the position is greater than 10.
# - inv: inverted position of the item: 1/position within (0:1] limits.
# - log: 1 / log10(position + 1.7) normalized to the range [0:1].
# - quadratic: 1 / (position**2) - events closer to the end of sequence have larger weights than the past events,
# it is similar to linear function but weights are decreasing at larger and non-linear rate.
ranking_strategy: "log"
return_events_from_session: True
recommend_any: False