config.toml

[master]

# GRPC port of the master node. The default value is 8086.
port = 8086

# gRPC host of the master node. The default values is "0.0.0.0".
host = "0.0.0.0"

# HTTP port of the master node. The default values is 8088.
http_port = 8088

# HTTP host of the master node. The default values is "0.0.0.0".
http_host = "0.0.0.0"

# Number of working jobs in the master node. The default value is 1.
n_jobs = 1

# Meta information timeout. The default value is 10s.
meta_timeout = "10s"

# Username for the master node dashboard.
dashboard_user_name = ""

# Password for the master node dashboard.
dashboard_password = ""

[server]

# Default number of returned items. The default value is 10.
default_n = 10

# Secret key for RESTful APIs (SSL required).
api_key = "zhenghaoz"

# Clock error in the cluster. The default value is 5s.
clock_error = "5s"

# Insert new users while inserting feedback. The default value is true.
auto_insert_user = true

# Insert new items while inserting feedback. The default value is true.
auto_insert_item = true

[recommend]

# The cache size for recommended/popular/latest items. The default value is 10.
cache_size = 100

[recommend.data_source]

# The feedback types for positive events.
positive_feedback_types = ["star","like"]

# The feedback types for read events.
read_feedback_types = ["read"]

# The time-to-live (days) of positive feedback, 0 means disabled. The default value is 0.
positive_feedback_ttl = 0

# The time-to-live (days) of items, 0 means disabled. The default value is 0.
item_ttl = 0

[recommend.popular]

# The time window of popular items. The default values is 4320h.
popular_window = "720h"

[recommend.user_neighbors]

# The type of neighbors for users. There are three types:
#   similar: Neighbors are found by number of common labels.
#   related: Neighbors are found by number of common liked items.
#   auto: If a user have labels, neighbors are found by number of common labels.
#         If this user have no labels, neighbors are found by number of common liked items.
# The default value is "auto".
neighbor_type = "similar"

# Enable approximate user neighbor searching using vector index. The default value is true.
enable_index = true

# Minimal recall for approximate user neighbor searching. The default value is 0.8.
index_recall = 0.8

# Maximal number of fit epochs for approximate user neighbor searching vector index. The default value is 3.
index_fit_epoch = 3

[recommend.item_neighbors]

# The type of neighbors for items. There are three types:
#   similar: Neighbors are found by number of common labels.
#   related: Neighbors are found by number of common users.
#   auto: If a item have labels, neighbors are found by number of common labels.
#         If this item have no labels, neighbors are found by number of common users.
# The default value is "auto".
neighbor_type = "similar"

# Enable approximate item neighbor searching using vector index. The default value is true.
enable_index = true

# Minimal recall for approximate item neighbor searching. The default value is 0.8.
index_recall = 0.8

# Maximal number of fit epochs for approximate item neighbor searching vector index. The default value is 3.
index_fit_epoch = 3

[recommend.collaborative]

# Enable approximate collaborative filtering recommend using vector index. The default value is true.
enable_index = true

# Minimal recall for approximate collaborative filtering recommend. The default value is 0.9.
index_recall = 0.9

# Maximal number of fit epochs for approximate collaborative filtering recommend vector index. The default value is 3.
index_fit_epoch = 3

# The time period for model fitting. The default value is "60m".
model_fit_period = "60m"

# The time period for model searching. The default value is "360m".
model_search_period = "360m"

# The number of epochs for model searching. The default value is 100.
model_search_epoch = 100

# The number of trials for model searching. The default value is 10.
model_search_trials = 10

[recommend.replacement]

# Replace historical items back to recommendations. The default value is false.
enable_replacement = false

# Decay the weights of replaced items from positive feedbacks. The default value is 0.8.
positive_replacement_decay = 0.8

# Decay the weights of replaced items from read feedbacks. The default value is 0.6.
read_replacement_decay = 0.6

[recommend.offline]

# The time period to check recommendation for users. The default values is 1m.
check_recommend_period = "1m"

# The time period to refresh recommendation for inactive users. The default values is 120h.
refresh_recommend_period = "24h"

# Enable latest recommendation during offline recommendation. The default value is false.
enable_latest_recommend = true

# Enable popular recommendation during offline recommendation. The default value is false.
enable_popular_recommend = false

# Enable user-based similarity recommendation during offline recommendation. The default value is false.
enable_user_based_recommend = true

# Enable item-based similarity recommendation during offline recommendation. The default value is false.
enable_item_based_recommend = false

# Enable collaborative filtering recommendation during offline recommendation. The default value is true.
enable_collaborative_recommend = true

# Enable click-though rate prediction during offline recommendation. Otherwise, results from multi-way recommendation
# would be merged randomly. The default value is false.
enable_click_through_prediction = true

# The explore recommendation method is used to inject popular items or latest items into recommended result:
#   popular: Recommend popular items to cold-start users.
#   latest: Recommend latest items to cold-start users.
# The default values is { popular = 0.0, latest = 0.0 }.
explore_recommend = { popular = 0.1, latest = 0.2 }

[recommend.online]

# The fallback recommendation method is used when cached recommendation drained out:
#   item_based: Recommend similar items to cold-start users.
#   popular: Recommend popular items to cold-start users.
#   latest: Recommend latest items to cold-start users.
# Recommenders are used in order. The default values is ["latest"].
fallback_recommend = ["item_based", "latest"]

# The number of feedback used in fallback item-based similar recommendation. The default values is 10.
num_feedback_fallback_item_based = 10