INFORMATION PROCESSING APPARATUS AND NON-TRANSITORY COMPUTER READABLE MEDIUM
An information processing apparatus includes a memory and a processor. The memory stores, for each object judged by a user whether to select the object, a selection history indicating whether the user has selected the object and a recommendation history indicating whether the object has been recommended to the user. The processor reads information from the memory. The processor is configured to conduct machine learning for estimating an effect of recommending each object from the selection history and the recommendation history, and to output, based on a result of the machine learning, information concerning an object that is predicted to be more likely to be selected by the user as a result of being recommended so as to satisfy a predetermined possibility level or higher.
1 . An information processing apparatus comprising:
a memory that stores, for each object judged by a user whether to select the object, a selection history indicating whether the user has selected the object and a recommendation history indicating whether the object has been recommended to the user; and
a processor that reads information from the memory,
the processor configured to
conduct machine learning for estimating an effect of recommending each object from the selection history and the recommendation history, and
output, based on a result of the machine learning, information concerning an object that is predicted to be more likely to be selected by the user as a result of being recommended so as to satisfy a predetermined possibility level or higher.
2 . The information processing apparatus according to claim 1 , wherein the machine learning categorizes objects into a plurality of object groups including a first object group and a second object group and rates the first object group as a positive example and the second object group as a negative example, the first object group being an object group which is recommended to a user and is selected by the user, the second object group being an object group which is not recommended to a user and is selected by the user.
3 . The information processing apparatus according to claim 2 , wherein the machine learning categorizes objects into the plurality of object groups including a third object group and rates the third object group as a negative example, the third object group being an object group which is recommended to a user and is not selected by the user.
4 . The information processing apparatus according to claim 2 , wherein the machine learning categorizes objects into the plurality of object groups including a fourth object group and rates the fourth object group as a positive example with a smaller weight than a weight for the first object group, the fourth object group being an object group which is not recommended to a user and is not selected by the user.
5 . The information processing apparatus according to claim 3 , wherein the machine learning categorizes objects into the plurality of object groups including a fourth object group and rates the fourth object group as a positive example with a smaller weight than a weight for the first object group, the fourth object group being an object group which is not recommended to a user and is not selected by the user.
6 . The information processing apparatus according to claim 2 , wherein the machine learning rates, among the plurality of object groups, an object group recommended to a user by changing a weight to be applied to an object categorized as the object group in accordance with a type of recommendation made to the object.
7 . The information processing apparatus according to claim 3 , wherein the machine learning rates, among the plurality of object groups, an object group recommended to a user by changing a weight to be applied to an object categorized as the object group in accordance with a type of recommendation made to the object.
8 . The information processing apparatus according to claim 4 , wherein the machine learning rates, among the plurality of object groups, an object group recommended to a user by changing a weight to be applied to an object categorized as the object group in accordance with a type of recommendation made to the object.
9 . The information processing apparatus according to claim 2 , wherein the machine learning rates, among the plurality of object groups, an object group selected by a user by changing a weight to be applied to an object categorized as the object group in accordance with a type of selection made to the object.
10 . The information processing apparatus according to claim 3 , wherein the machine learning rates, among the plurality of object groups, an object group selected by a user by changing a weight to be applied to an object categorized as the object group in accordance with a type of selection made to the object.
11 . The information processing apparatus according to claim 4 , wherein the machine learning rates, among the plurality of object groups, an object group selected by a user by changing a weight to be applied to an object categorized as the object group in accordance with a type of selection made to the object.
12 . The information processing apparatus according to claim 6 , wherein the machine learning rates, among the plurality of object groups, an object group selected by a user by changing a weight to be applied to an object categorized as the object group in accordance with a type of selection made to the object.
13 . The information processing apparatus according to claim 2 , wherein the machine learning rates the first object group by changing a weight to be applied to an object categorized as the first object group in accordance with a time from when the object is recommended until the object is selected.
14 . The information processing apparatus according to claim 3 , wherein the machine learning rates the first object group by changing a weight to be applied to an object categorized as the first object group in accordance with a time from when the object is recommended until the object is selected.
15 . The information processing apparatus according to claim 4 , wherein the machine learning rates the first object group by changing a weight to be applied to an object categorized as the first object group in accordance with a time from when the object is recommended until the object is selected.
16 . The information processing apparatus according to claim 6 , wherein the machine learning rates the first object group by changing a weight to be applied to an object categorized as the first object group in accordance with a time from when the object is recommended until the object is selected.
17 . The information processing apparatus according to claim 9 , wherein the machine learning rates the first object group by changing a weight to be applied to an object categorized as the first object group in accordance with a time from when the object is recommended until the object is selected.
18 . The information processing apparatus according to claim 1 , wherein the machine learning categorizes objects into a plurality of object groups and rates an object group which is recommended to a user and is selected by the user during a certain period and which is not recommended to the user and is not selected by the user during another period as a positive example.
19 . The information processing apparatus according to claim 1 , wherein the machine learning rates an object group which is recommended to a user and is not selected by the user during a certain period and which is not recommended to the user and is selected by the user during another period as a negative example.
20 . A non-transitory computer readable medium storing a program causing a processor to execute a process, the processor reading information from a memory that stores, for each object judged by a user whether to select the object, a selection history indicating whether the user has selected the object and a recommendation history indicating whether the object has been recommended to the user, the process comprising:
conducting machine learning for estimating an effect of recommending each object from the selection history and the recommendation history; and
outputting, based on a result of the machine learning, information concerning an object that is predicted to be more likely to be selected by the user as a result of being recommended so as to satisfy a predetermined possibility level or higher.