Module flike

More information about Flike can be found here.

Installation

Install the flike-predict package with pip.

pip3 install flike-predict

Quick Guide

1. Install the module as described in Installation.

2. Import the module into your code

   from flike import *

3. Initialize the flike-recommend client by calling the initialize function with your API key as a paremeter.

4. 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, call like 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, call dislike when they stop watching after watching less than 50% of it.

5. Retrieve recommendations for a user by calling recommend.

6. Filter and sort the recommendations if any constraints need to be considered.

7. 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