`pyastroapi.api.recommender`

Module Contents

`matchdoc`(token[, abstract, title, author, year, ...])
`similar`(token[, sort, num_docs, top_n_reads, cutoff_days])	Find papers similar to what the user reads
`trending`(token[, sort, num_docs, top_n_reads, cutoff_days])	Find trending papers based on what the user reads
`reviews`(token[, sort, num_docs, top_n_reads, cutoff_days])	Find reviews based on what the user reads
`useful`(token[, sort, num_docs, top_n_reads, cutoff_days])	Find "usefull" papers based on what the user reads

pyastroapi.api.recommender.matchdoc(token: str, abstract: str = '', title: str = '', author: str = '', year: int = '', doctype: str = 'article', match_doctype: List[str] = ['article'], must_match=True)

pyastroapi.api.recommender.similar(token, sort='first_author', num_docs=20, top_n_reads=50, cutoff_days=7)

Find papers similar to what the user reads

Note for this to work you must be “active” that is logged in ADS, and in the past 90 days active for at least 5 days.

Parameters:

token (_type_) – ADS token
sort (str, optional) – Sort order, note this does not take a direction. Defaults to “first_author”.
num_docs (int, optional) – Maximum number of docs to return. Defaults to 20.
top_n_reads (int, optional) – Number of records to use. Defaults to 50.
cutoff_days (int, optional) – Days back to use for recommedations. Defaults to 7.

Returns:

_description_

Return type:

_type_

pyastroapi.api.recommender.trending(token, sort='first_author', num_docs=20, top_n_reads=50, cutoff_days=7)

Find trending papers based on what the user reads

Note for this to work you must be “active” that is logged in ADS, and in the past 90 days active for at least 5 days.

Parameters:

token (_type_) – ADS token
sort (str, optional) – Sort order, note this does not take a direction. Defaults to “first_author”.
num_docs (int, optional) – Maximum number of docs to return. Defaults to 20.
top_n_reads (int, optional) – Number of records to use. Defaults to 50.
cutoff_days (int, optional) – Days back to use for recommedations. Defaults to 7.

Returns:

_description_

Return type:

_type_

pyastroapi.api.recommender.reviews(token, sort='first_author', num_docs=20, top_n_reads=50, cutoff_days=7)

Find reviews based on what the user reads

Note for this to work you must be “active” that is logged in ADS, and in the past 90 days active for at least 5 days.

Parameters:

token (_type_) – ADS token
sort (str, optional) – Sort order, note this does not take a direction. Defaults to “first_author”.
num_docs (int, optional) – Maximum number of docs to return. Defaults to 20.
top_n_reads (int, optional) – Number of records to use. Defaults to 50.
cutoff_days (int, optional) – Days back to use for recommedations. Defaults to 7.

Returns:

_description_

Return type:

_type_

pyastroapi.api.recommender.useful(token, sort='first_author', num_docs=20, top_n_reads=50, cutoff_days=7)

Find “usefull” papers based on what the user reads

Note for this to work you must be “active” that is logged in ADS, and in the past 90 days active for at least 5 days.

Parameters:

token (_type_) – ADS token
sort (str, optional) – Sort order, note this does not take a direction. Defaults to “first_author”.
num_docs (int, optional) – Maximum number of docs to return. Defaults to 20.
top_n_reads (int, optional) – Number of records to use. Defaults to 50.
cutoff_days (int, optional) – Days back to use for recommedations. Defaults to 7.

Returns:

_description_

Return type:

_type_