exclude read posts from the recommendations

[Xoshbin]

Gorse version
v0.4.15

Describe the bug
In a pagination request, I'm requesting 10 items per recommendation request, the issue is the for the next page when I'm requesting another 10 items it's responding with 10 items again but only 3 items are new compared to the first page. and so on in some pages sometimes there is even no new items. and even on the dashboard it's not removing the read or "seen" "in my situation" from the recommended items.

to be said, I have changed the read feedback to seen, because I'm trying to use it in new aggregator app and reading a post is a positive feedback.

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

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

To Reproduce
I'm going to add config file below.

Expected behavior
I expect the recommender to give new items each time I request for new recommendation and exclude the seen items.

Additional context

# The naming prefix for tables (collections, keys) in databases. The default value is empty.
table_prefix = ""

# The naming prefix for tables (collections, keys) in cache storage databases. The default value is `table_prefix`.
cache_table_prefix = ""

# The naming prefix for tables (collections, keys) in data storage databases. The default value is `table_prefix`.
data_table_prefix = ""

[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"

# AllowedDomains is a list of allowed values for Http Origin.
# The list may contain the special wildcard string ".*" ; all is allowed
# If empty all are allowed.
http_cors_domains = []

# AllowedMethods is either empty or has a list of http methods names. Checking is case-insensitive.
http_cors_methods = []

# 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 = ""

# Secret key for admin APIs (SSL required).
admin_api_key = ""

[server]

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

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

# 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

# Server-side cache expire time. The default value is 10s.
cache_expire = "10s"

[recommend]

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

# Recommended cache expire time. The default value is 72h.
cache_expire = "72h"

[recommend.data_source]

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

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

# The time-to-live (days) of positive feedback, 0 means disabled. The default value is 0.
positive_feedback_ttl = 15

# The time-to-live (days) of items, 0 means disabled. The default value is 0.
item_ttl = 5

[recommend.popular]

# The time window of popular items. The default values is 4320h.
popular_window = "48h"

[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

# Enable searching models of different sizes, which consume more memory. The default value is false.
enable_model_size_search = false

[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 = true

# 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", "popular"]

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

[tracing]

# Enable tracing for REST APIs. The default value is false.
enable_tracing = false

# The type of tracing exporters should be one of "jaeger", "zipkin", "otlp" and "otlphttp". The default value is "jaeger".
exporter = "jaeger"

# The endpoint of tracing collector.
collector_endpoint = "http://localhost:14268/api/traces"

# The type of tracing sampler should be one of "always", "never" and "ratio". The default value is "always".
sampler = "always"

# The ratio of ratio based sampler. The default value is 1.
ratio = 1