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Copy file name to clipboardExpand all lines: lib/recombee_api_client/api/delete_cart_addition.rb
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@@ -17,13 +17,19 @@ class DeleteCartAddition < ApiRequest
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# * *Required arguments*
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# - +user_id+ -> ID of the user who made the cart addition.
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# - +item_id+ -> ID of the item of which was added to cart.
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# - +timestamp+ -> Unix timestamp of the cart addition.
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#
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definitialize(user_id,item_id,timestamp)
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# * *Optional arguments (given as hash optional)*
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# - +timestamp+ -> Unix timestamp of the cart addition. If the `timestamp` is omitted, then all the cart additions with given `userId` and `itemId` are deleted.
Copy file name to clipboardExpand all lines: lib/recombee_api_client/api/delete_detail_view.rb
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@@ -17,13 +17,19 @@ class DeleteDetailView < ApiRequest
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# * *Required arguments*
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# - +user_id+ -> ID of the user who made the detail view.
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# - +item_id+ -> ID of the item of which the details were viewed.
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# - +timestamp+ -> Unix timestamp of the detail view.
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#
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definitialize(user_id,item_id,timestamp)
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# * *Optional arguments (given as hash optional)*
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# - +timestamp+ -> Unix timestamp of the detail view. If the `timestamp` is omitted, then all the detail views with given `userId` and `itemId` are deleted.
Copy file name to clipboardExpand all lines: lib/recombee_api_client/api/item_based_recommendation.rb
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@@ -10,7 +10,7 @@ module RecombeeApiClient
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#Recommends set of items that are somehow related to one given item, *X*. Typical scenario for using item-based recommendation is when user *A* is viewing *X*. Then you may display items to the user that he might be also interested in. Item-recommendation request gives you Top-N such items, optionally taking the target user *A* into account.
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#
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# - +filter+ -> Boolean-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to filter recommended items based on the values of their attributes.
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# - +booster+ -> Number-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to boost recommendation rate of some items based on the values of their attributes.
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# - +allowNonexistent+ -> If the user does not exist in the database, returns a list of non-personalized recommendations instead of causing HTTP 404 error.
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# - +allowNonexistent+ -> Instead of causing HTTP 404 error, returns some (non-personalized) recommendations if either item of given *itemId* or user of given *targetUserId* does not exist in the database. It creates neither of the missing entities in the database.
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# - +cascadeCreate+ -> If item of given *itemId* or user of given *targetUserId* doesn't exist in the database, it creates the missing enity/entities and returns some (non-personalized) recommendations. This allows for example rotations in the following recommendations for the user of given *targetUserId*, as the user will be already known to the system.
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# - +scenario+ -> Scenario defines a particular application of recommendations. It can be for example "homepage" or "cart". The AI which optimizes models in order to get the best results may optimize different scenarios separately, or even use different models in each of the scenarios.
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# - +diversity+ -> **Expert option** Real number from [0.0, 1.0] which determines how much mutually dissimilar should the recommended items be. The default value is 0.0, i.e., no diversification. Value 1.0 means maximal diversification.
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#
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# - +minRelevance+ -> **Expert option** Specifies the threshold of how much relevant must the recommended items be to the user. Possible values one of: "low", "medium", "high". The default value is "low", meaning that the system attempts to recommend number of items equal to *count* at any cost. If there are not enough data (such as interactions or item properties), this may even lead to bestseller-based recommendations to be appended to reach the full *count*. This behavior may be suppressed by using "medium" or "high" values. In such case, the system only recommends items of at least the requested qualit, and may return less than *count* items when there is not enough data to fulfill it.
Copy file name to clipboardExpand all lines: lib/recombee_api_client/api/user_based_recommendation.rb
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#Based on user's past interactions (purchases, ratings, etc.) with the items, recommends top-N items that are most likely to be of high value for a given user.
@@ -21,7 +21,9 @@ class UserBasedRecommendation < ApiRequest
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# * *Optional arguments (given as hash optional)*
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# - +filter+ -> Boolean-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to filter recommended items based on the values of their attributes.
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# - +booster+ -> Number-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to boost recommendation rate of some items based on the values of their attributes.
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# - +allowNonexistent+ -> If the user does not exist in the database, returns a list of non-personalized recommendations instead of causing HTTP 404 error.
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# - +allowNonexistent+ -> If the user does not exist in the database, returns a list of non-personalized recommendations instead of causing HTTP 404 error. It doesn't create the user in the database.
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# - +cascadeCreate+ -> If the user does not exist in the database, returns a list of non-personalized recommendations and creates the user in the database. This allows for example rotations in the following recommendations for that user, as the user will be already known to the system.
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# - +scenario+ -> Scenario defines a particular application of recommendations. It can be for example "homepage" or "cart". The AI which optimizes models in order to get the best results may optimize different scenarios separately, or even use different models in each of the scenarios.
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# - +diversity+ -> **Expert option** Real number from [0.0, 1.0] which determines how much mutually dissimilar should the recommended items be. The default value is 0.0, i.e., no diversification. Value 1.0 means maximal diversification.
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#
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# - +minRelevance+ -> **Expert option** Specifies the threshold of how much relevant must the recommended items be to the user. Possible values one of: "low", "medium", "high". The default value is "low", meaning that the system attempts to recommend number of items equal to *count* at any cost. If there are not enough data (such as interactions or item properties), this may even lead to bestseller-based recommendations to be appended to reach the full *count*. This behavior may be suppressed by using "medium" or "high" values. In such case, the system only recommends items of at least the requested qualit, and may return less than *count* items when there is not enough data to fulfill it.
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