RECOMMENDING OF AN ITEM TO A USER
The present disclose provides a method of recommending at least one item to a user, the method comprising: receiving the user's feedback on an item; predicting a user vector and an item vector related to the user in an online mode based on the received feedback; and recommending to the user an acceptable item based on the user vector and item vector.
1 . A method of recommending at least one item to a user, the method comprising:
receiving the user's feedback on an item;
predicting a user vector and an item vector related to the user in an online mode based on the received feedback; and
recommending to the user an acceptable item based on the predicted user vector and item vector.
2 . The method according to claim 1 , further comprising:
determining whether the user has any social information;
merging the social information with a user-item matrix to predict the user vector and the item vector.
3 . The method according to claim 2 , wherein the social information is expressed as a social network matrix.
4 . The method according to claim 2 , further comprising:
determining whether the user has any location information;
merging the location information with the user-item matrix to predict the user and item vectors.
5 . The method according to claim 4 , wherein the location information is expressed as a location network matrix.
6 . The method according to claim 1 , wherein the step of recommending the user the acceptable item is further based on the user's preference.
7 . A recommender system, comprising:
at least one processor and a recommendation engine to:
generate a user matrix based on received user's information;
generate an item matrix based on information of one or more items;
predict a rating value of at least one item in an online mode based on a user vector in the user matrix and an item vector in the item matrix; and
recommend to a user an acceptable item based on the ranking of the predicted rating value.
8 . The recommender system according to claim 7 , where the recommender system further includes at least one element of a group comprised of:
a user data module to store at least one of the user's information and the user's feedback;
an item module to store information of all items to be recommended; and
a repository to store at least one of the user matrix, item matrix and a user-item matrix.
9 . The online recommender system according to claim 7 , wherein the user's information further includes at least one of social information and location information for use by the recommendation engine to recommend the acceptable item.
10 . Apparatus comprising:
a processor; and
a memory storing computer readable instructions executable by the processor to:
update an initial user-item matrix by using an online prediction algorithm; and
recommend to a user an acceptable item based on ratings of items in the updated user-item matrix.
11 . The apparatus according to claim 10 , wherein the memory further stores instructions which, when executed by the processor, cause the processor to:
generate the initial user-item rating matrix based on a user's information.
12 . The apparatus according to claim 10 , wherein the memory further stores instructions which, when executed by the processor, cause the processor to:
generate the initial user-item rating matrix based on user's historical data.
13 . The apparatus according to claim 10 , wherein the online predication algorithm includes a local objective function for an online mode.
14 . The apparatus according to claim 13 , wherein the local objective function is to be minimized.
15 . The apparatus according to claim 13 , wherein the local objective function is a root mean square function.