ITEM RECOMENDATION
An example method is provided in according with one implementation of the present disclosure. The method includes extracting features related to a plurality of users and a plurality of items and computing a correction parameter score for each of a plurality of user-item pair combinations. The method further includes computing a user response value for a user-item pair combination by applying a generalized linear model to the features of the user-item pair combination and using the correction parameter score for the user-item pair combination in the generalized linear model.
1 . A method comprising, by at least one processor
extracting features related to a plurality of users and a plurality of items;
computing a correction parameter score for each of a plurality of user-item pair combinations; and
computing a user response value for a user-item pair combination by applying a generalized linear model to the features of the user-item pair combination and using the correction parameter score for the user-item pair combination in the generalized linear model.
2 . The method of claim 1 , wherein extracting features further comprises:
extracting user features related to each user from the plurality of users;
extracting item features related to each item from the plurality of items; and
extracting user-item interaction features related to interactions between a user and an item in each of the user-item pair combinations.
3 . The method of claim 2 , further comprising computing coefficients for the user features, the item features, and the user-item interaction features of the plurality of user-item pair combinations for the generalized linear model, by adding the correction parameter scores and the features of the plurality of user-item pair combinations to the generalized linear model.
4 . The method of claim 3 , further comprising using the coefficients, the user features, the item features, the user-item interaction features for a user-item pair combination, and the correction parameter score for the user-item pair combination to compute the user response value for the user-item pair combination, wherein the user response value is a real value.
5 . The method of claim 3 , wherein the generalized linear model is a logistic regression model.
6 . The method of claim 1 , wherein the correction parameter score is computed by using an item-based collaborative filtering technique, and wherein the correction parameter score is a numerical value that represents a user's tendency to like an item.
7 . The method of claim 1 , wherein the features are extracted by using a content based filtering technique.
8 . A system comprising:
a features engine to identify features related to a plurality of users and a plurality of items;
a correction parameter engine to compute a correction parameter score for each of a plurality of user-item pair combinations;
a response value engine to compute a user response value for a user-item pair combination by applying a generalized linear model to the features of the user-item pair combination and augmenting the generalized linear model with the correction parameter score for the user-item pair combination; and
a recommender engine to provide an item recommendation based on the user response value.
9 . The system of claim 8 , wherein the features engine is further to:
extract user features related to each user from the plurality of users;
extract item features related to each item from the plurality of items; and
extract user-item interaction features related to interactions between a user and an item in each of the user-item pair combinations.
10 . The system of claim 9 , wherein the response value engine is further to:
compute coefficients for the user features, the item features, and the user-item interaction features of the plurality of user-item pair combinations for the generalized linear model, by adding the correction parameter scores and the features of the plurality of user-item pair combinations to the generalized linear model.
11 . The system of claim 10 , the response value engine is further to:
use the coefficients, the user features, the item features, the user-item interaction features for a user-item pair combination, and the correction parameter score for the user-item pair combination to compute the user response value for the user-item pair combination.
12 . A non-transitory machine-readable storage medium encoded with instructions executable by at least one processor, the machine-readable storage medium comprising instructions to:
identify features related to a plurality of users and a plurality of items;
compute a correction parameter score for each of a plurality of user-item pair combinations;
compute a user response value for an identified user-item pair combination by applying a generalized linear model to the features of the user-item pair combination and augmenting the generalized linear model with the correction parameter score for the user-item pair combination; and
provide an item recommendation based on the user response value.
13 . The non-transitory machine-readable storage medium of claim 12 , further comprising instructions to:
extract user features related to each user from the plurality of users;
extract item features related to each item from the plurality of items; and
extract user-item interaction features related to interactions between a user and an item in each of the user-item pair combinations.
14 . The non-transitory machine-readable storage medium of claim 13 , further comprising instructions to compute coefficients for the user features, the item features, and the user-item interaction features of the plurality of user-item pair combinations for the generalized linear model, by adding the correction parameter scores and the features of the plurality of user-item pair combinations to the generalized linear model.
15 . The non-transitory machine-readable storage medium of claim 14 , further comprising instructions to use the coefficients, the user features, the item features, the user-item interaction features for the identified user-item pair combination, and the correction parameter score for the identified user-item pair combination to compute the user response value for the identified user-item pair combination, wherein the user response value is a real value.