recommender system python code example for creating one classifier per user or only one classifier per item
$10-30 USD
Paid on delivery
recommender system python code example for creating one classifier per user
or only one classifier per item
you use/update known data set or simulate your data : 1000 users and 4000 items
take existing data and modify for example ?
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but code should work for any data as input
but mixed with collaborating filter mechanism
for one classifier per user for example for film recommender system
target is: this one given user used this film or not - YES OR NO
feach row is one row per item-film
when features/columns are:
1
item descriptions are mixture of categorical and continuous values like :
film duration
comedy or detective or film for kids
price
year
etc
2
each user ( except one particular user ) descriptions are mixture of categorical and continuous values like
age
gender
country
city
etc
3
rating for each user for this film, or just binary used or not
THEN I WANT TO TUNE CLASSIFIER ON TRAIN DATA AND RUN CLASSIFIER ON THE TEST DATA
all prediction for YES will be my recommendations
THEN MY RECOMMENDATIONS WILL BE ALL YES PREDICTIONS FOR new unseen for model creation ROWS
as mentioned in
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for each user we want to train a simple linear regression that takes item features as inputs and output the rating for this item.
for each item we want to train a Bayesian classifier that takes user features as inputs and output either “like” or “dislike”
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or
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or
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Building a recommender system via classification
BETTER FIND EXSISTING REPO IN GITHUB AND TEACH ME HOW TO USE IT
Project ID: #29796591
