Designing movie Recommender system based on quantity of tickets purchased by the userAlgorithms to find stuff a user would like based on other users likesMahout Recommendations on Binary dataBasic recommendation engine algorithmRecommendation system and baseline predictorsWeka ARFF How to handle features/attributes that can have more then 1 value per data-itemClustering before regression - recommender systemSplitting in Recommender SystemHow can I recommend content(say movies) based on multiple parameterscontent-based recommendation system: how to generate the feature vector?Content-based movie similarity metric
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Designing movie Recommender system based on quantity of tickets purchased by the user
Algorithms to find stuff a user would like based on other users likesMahout Recommendations on Binary dataBasic recommendation engine algorithmRecommendation system and baseline predictorsWeka ARFF How to handle features/attributes that can have more then 1 value per data-itemClustering before regression - recommender systemSplitting in Recommender SystemHow can I recommend content(say movies) based on multiple parameterscontent-based recommendation system: how to generate the feature vector?Content-based movie similarity metric
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I have a task of designing a movie recommender system for a multiplex brand.Data has basically three main fields i.e UserId,MovieName,Quantity of tickets Purchased.Apart from these three main columns we have columns such as genre of movie,director of movie and description of movie.
There is no ratings given by the user for the movies.So basically i don't know the user has liked the movie or not.I have quantity of tickets purchased but that doesn't mean he/she liked that movie.
How to approach this task of designing a recommender for this particular problem statement.Any insights would be highly appreciable!
python-3.x machine-learning recommendation-engine
asked Mar 8 at 5:58
SARTHAKSARTHAK
11
add a comment |
I have a task of designing a movie recommender system for a multiplex brand.Data has basically three main fields i.e UserId,MovieName,Quantity of tickets Purchased.Apart from these three main columns we have columns such as genre of movie,director of movie and description of movie.
There is no ratings given by the user for the movies.So basically i don't know the user has liked the movie or not.I have quantity of tickets purchased but that doesn't mean he/she liked that movie.
How to approach this task of designing a recommender for this particular problem statement.Any insights would be highly appreciable!
python-3.x machine-learning recommendation-engine
asked Mar 8 at 5:58
SARTHAKSARTHAK
11
add a comment |
I have a task of designing a movie recommender system for a multiplex brand.Data has basically three main fields i.e UserId,MovieName,Quantity of tickets Purchased.Apart from these three main columns we have columns such as genre of movie,director of movie and description of movie.
There is no ratings given by the user for the movies.So basically i don't know the user has liked the movie or not.I have quantity of tickets purchased but that doesn't mean he/she liked that movie.
How to approach this task of designing a recommender for this particular problem statement.Any insights would be highly appreciable!
python-3.x machine-learning recommendation-engine
asked Mar 8 at 5:58
SARTHAKSARTHAK
11
I have a task of designing a movie recommender system for a multiplex brand.Data has basically three main fields i.e UserId,MovieName,Quantity of tickets Purchased.Apart from these three main columns we have columns such as genre of movie,director of movie and description of movie.
There is no ratings given by the user for the movies.So basically i don't know the user has liked the movie or not.I have quantity of tickets purchased but that doesn't mean he/she liked that movie.
How to approach this task of designing a recommender for this particular problem statement.Any insights would be highly appreciable!
python-3.x machine-learning recommendation-engine
python-3.x machine-learning recommendation-engine
asked Mar 8 at 5:58
SARTHAKSARTHAK
11
asked Mar 8 at 5:58
SARTHAKSARTHAK
11
asked Mar 8 at 5:58
SARTHAKSARTHAK
11
asked Mar 8 at 5:58
SARTHAKSARTHAK
11
asked Mar 8 at 5:58
SARTHAKSARTHAK
11
11
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
I think you need to search more data like movie category, actors, imdb rating and many things
you can do eda and find quantities related analysis and make some clusters on some features.
try to find user repeated to watch to similar clusters again ?
is there any similar group watched similar movies ?
then on basis of history you can find category of user and recommend on basis of cluster.
answered Mar 8 at 6:23
AkhileshAkhilesh
461311
There are constarints on the data provided..Data is provided by client,so we can't tinker with it.
– SARTHAK
Mar 8 at 6:26
so you have data like userid and quantity purchased. you can do many things, users are main id here you need to check is that user coming again to similar kind of movie again, is there connection between old movie and this movie. if yes , you can make even better
– Akhilesh
Mar 8 at 6:31
Can you specify on modelling approach?
– SARTHAK
Mar 8 at 6:36
find similarity between movie a vs all movies , repeat with all, check how many similar groups are there. then check which user is going to which cluster.
– Akhilesh
Mar 8 at 6:41
ask these kind of questions at datascience.stackexchange.com
– Akhilesh
Mar 9 at 4:01
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I think you need to search more data like movie category, actors, imdb rating and many things
you can do eda and find quantities related analysis and make some clusters on some features.
try to find user repeated to watch to similar clusters again ?
is there any similar group watched similar movies ?
then on basis of history you can find category of user and recommend on basis of cluster.
answered Mar 8 at 6:23
AkhileshAkhilesh
461311
There are constarints on the data provided..Data is provided by client,so we can't tinker with it.
– SARTHAK
Mar 8 at 6:26
so you have data like userid and quantity purchased. you can do many things, users are main id here you need to check is that user coming again to similar kind of movie again, is there connection between old movie and this movie. if yes , you can make even better
– Akhilesh
Mar 8 at 6:31
Can you specify on modelling approach?
– SARTHAK
Mar 8 at 6:36
find similarity between movie a vs all movies , repeat with all, check how many similar groups are there. then check which user is going to which cluster.
– Akhilesh
Mar 8 at 6:41
ask these kind of questions at datascience.stackexchange.com
– Akhilesh
Mar 9 at 4:01
add a comment |
I think you need to search more data like movie category, actors, imdb rating and many things
you can do eda and find quantities related analysis and make some clusters on some features.
try to find user repeated to watch to similar clusters again ?
is there any similar group watched similar movies ?
then on basis of history you can find category of user and recommend on basis of cluster.
answered Mar 8 at 6:23
AkhileshAkhilesh
461311
There are constarints on the data provided..Data is provided by client,so we can't tinker with it.
– SARTHAK
Mar 8 at 6:26
so you have data like userid and quantity purchased. you can do many things, users are main id here you need to check is that user coming again to similar kind of movie again, is there connection between old movie and this movie. if yes , you can make even better
– Akhilesh
Mar 8 at 6:31
Can you specify on modelling approach?
– SARTHAK
Mar 8 at 6:36
find similarity between movie a vs all movies , repeat with all, check how many similar groups are there. then check which user is going to which cluster.
– Akhilesh
Mar 8 at 6:41
ask these kind of questions at datascience.stackexchange.com
– Akhilesh
Mar 9 at 4:01
add a comment |
I think you need to search more data like movie category, actors, imdb rating and many things
you can do eda and find quantities related analysis and make some clusters on some features.
try to find user repeated to watch to similar clusters again ?
is there any similar group watched similar movies ?
then on basis of history you can find category of user and recommend on basis of cluster.
answered Mar 8 at 6:23
AkhileshAkhilesh
461311
I think you need to search more data like movie category, actors, imdb rating and many things
you can do eda and find quantities related analysis and make some clusters on some features.
try to find user repeated to watch to similar clusters again ?
is there any similar group watched similar movies ?
then on basis of history you can find category of user and recommend on basis of cluster.
answered Mar 8 at 6:23
AkhileshAkhilesh
461311
answered Mar 8 at 6:23
AkhileshAkhilesh
461311
answered Mar 8 at 6:23
AkhileshAkhilesh
461311
answered Mar 8 at 6:23
AkhileshAkhilesh
461311
461311
There are constarints on the data provided..Data is provided by client,so we can't tinker with it.
– SARTHAK
Mar 8 at 6:26
so you have data like userid and quantity purchased. you can do many things, users are main id here you need to check is that user coming again to similar kind of movie again, is there connection between old movie and this movie. if yes , you can make even better
– Akhilesh
Mar 8 at 6:31
Can you specify on modelling approach?
– SARTHAK
Mar 8 at 6:36
find similarity between movie a vs all movies , repeat with all, check how many similar groups are there. then check which user is going to which cluster.
– Akhilesh
Mar 8 at 6:41
ask these kind of questions at datascience.stackexchange.com
– Akhilesh
Mar 9 at 4:01
add a comment |
There are constarints on the data provided..Data is provided by client,so we can't tinker with it.
– SARTHAK
Mar 8 at 6:26
so you have data like userid and quantity purchased. you can do many things, users are main id here you need to check is that user coming again to similar kind of movie again, is there connection between old movie and this movie. if yes , you can make even better
– Akhilesh
Mar 8 at 6:31
Can you specify on modelling approach?
– SARTHAK
Mar 8 at 6:36
find similarity between movie a vs all movies , repeat with all, check how many similar groups are there. then check which user is going to which cluster.
– Akhilesh
Mar 8 at 6:41
ask these kind of questions at datascience.stackexchange.com
– Akhilesh
Mar 9 at 4:01
There are constarints on the data provided..Data is provided by client,so we can't tinker with it.
– SARTHAK
Mar 8 at 6:26
There are constarints on the data provided..Data is provided by client,so we can't tinker with it.
– SARTHAK
Mar 8 at 6:26
so you have data like userid and quantity purchased. you can do many things, users are main id here you need to check is that user coming again to similar kind of movie again, is there connection between old movie and this movie. if yes , you can make even better
– Akhilesh
Mar 8 at 6:31
so you have data like userid and quantity purchased. you can do many things, users are main id here you need to check is that user coming again to similar kind of movie again, is there connection between old movie and this movie. if yes , you can make even better
– Akhilesh
Mar 8 at 6:31
Can you specify on modelling approach?
– SARTHAK
Mar 8 at 6:36
Can you specify on modelling approach?
– SARTHAK
Mar 8 at 6:36
find similarity between movie a vs all movies , repeat with all, check how many similar groups are there. then check which user is going to which cluster.
– Akhilesh
Mar 8 at 6:41
find similarity between movie a vs all movies , repeat with all, check how many similar groups are there. then check which user is going to which cluster.
– Akhilesh
Mar 8 at 6:41
ask these kind of questions at datascience.stackexchange.com
– Akhilesh
Mar 9 at 4:01
ask these kind of questions at datascience.stackexchange.com
– Akhilesh
Mar 9 at 4:01
add a comment |
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