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Project description
Module flike
More information about Flike can be found here.
Installation
Install the flike-predict package with pip.
pip3 install flike-predict
Quick Guide
-
Install the module as described in Installation.
-
Import the module into your code
from flike import *
-
Initialize the
flike-recommend
client by calling theinitialize
function with your API key as a paremeter. -
Call the corresponding functions whenever a user interacts with a content item.
start
when a user starts interacting with a content item.like
when a user seems to like a content item. E.g., in the case of a video, calllike
when the user watched more than 80% of a video.dislike
when a user seems to dislike a content item. E.g., in the case of a video, calldislike
when they stop watching after watching less than 50% of it.
-
Retrieve recommendations for a user by calling
recommend
. -
Filter and sort the recommendations if any constraints need to be considered.
-
Display/Use the recommendation in your application in whatever way applicable.
Functions
dislike(user_id: str, item_id: str)
: Registers a user-started item as 'disliked' by the user.
'Dislike' refers to any action indicating that a user dislikes the content item.
E.g. for a video, this could be a user only watching 5% of the video and not finishing it.
Parameters
----------
user_id : str
The unique identifier of the user
item_id : str
The unique identifier of the content item
inititialize(api_key: str, server_url: str = None, version: str = None)
: Initialize the recommender.
Parameters
----------
api_key : str
Your API Key
server_url : str (optional)
This is only used for internal testing
version : str (optional)
Version of the API to use
like(user_id: str, item_id: str)
: Registers a user-started item as 'liked' by the user.
'Like' refers to any action indicating that a user likes the content item.
E.g. for a video, this could be a user watching more than 85% of the video.
Parameters
----------
user_id : str
The unique identifier of the user
item_id : str
The unique identifier of the content item
recommend(user_id: str, num_item: int)
: Get an array of content items that a user is probable to consume/buy/subscribe/like or similar.
Recommendations are sorted by descending probability of a user 'liking' them.
Parameters
----------
user_id : str
The unique identifier of the user
num_item : str
Number of content items that should be suggested
start(user_id: str, item_id: str, correlation_id: str)
: Registers a user starting to consume/interact with a content item..
Parameters
----------
user_id : str
The unique identifier of the user
item_id : str
The unique identifier of the
corellation_id
The unique identifier of a recommendation
Classes
FlikeException(response: requests.models.Response)
: Exception raised by Flike API.
Attributes
----------
status : str
status code of the error (HTTP error code)
message : str
explanation of the error
### Ancestors (in MRO)
* builtins.Exception
* builtins.BaseException
Recommendation(item_id: str, probability: float)
: Recommendation of a content item for a user
Attributes
----------
item_id : str
Unique identifier of the content item being recommended
probability : str
Probability of a user 'liking' the recommended item
RecommendationsResponse(items: list[flike.Recommendation], correlation_id: str)
: Response to a recommendation request.
Attributes
----------
items : str
Recommendations for a user
correlation_id : str
Unique identifier of the content item being recommended
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