Computer-based communication generation using phrases selected based on behaviors of communication recipients
View Patent ↗A method and system for generating adaptive explanations for associated recommendations is disclosed. The adaptive explanations comprise a syntactical structure and associated phrases that are selected in accordance with usage behaviors and/or inferences associated with usage behaviors. The phrases included in an adaptive explanation may be selected through application of a non-deterministic process. The adaptive explanations may be beneficially applied to recommendations that are associated with content, products, and people, including recommendations that comprise advertisements.
1. A computer-based recommendation method comprising:
generating an affinity vector between a first user of a computer-based system and a plurality of computer-based objects based, at least in part, on the first user's behaviors;
generating a similarity metric between the first user and a second user of the computer-based system based, at least in part, on the affinity vector of the first user and an affinity vector of the second user;
generating a recommendation for delivery to the first user based, at least in part, on the affinity vector of the first user and the similarity metric; and
generating an explanation for the recommendation comprising one or more phrases, wherein the selection of the one or more phrases is based, at least in part, on a plurality of user behaviors and in accordance with a computer-implemented syntactical structure.
2. The method of claim 1 wherein generating an affinity vector between a first user of a computer-based system and a plurality of computer-based objects based, at least in part, on the first user's behaviors comprises:
generating affinities between the user and a plurality of topic objects.
3. The method of claim 1 wherein generating a recommendation for delivery to the first user based, at least in part, on the affinity vector of the first user and the similarity metric comprises:
recommending the second user to the first user.
4. The method of claim 1 wherein generating a recommendation for delivery to the first user based, at least in part, on the affinity vector of the first user and the similarity metric comprises:
generating a recommendation responsive to the user's search request.
5. The method of claim 1 wherein generating a recommendation for delivery to the first user based, at least in part, on the affinity vector of the first user and the similarity metric comprises:
generating a recommendation based on a recommendation preference setting established by the user.
6. The method of claim 1 wherein generating an explanation for the recommendation comprising one or more phrases, wherein the selection of the one or more phrases is based, at least in part, on a plurality of user behaviors and in accordance with a computer-implemented syntactical structure comprises:
selecting phrases for inclusion in the explanation based on a frequency distribution.
7. The method of claim 1 wherein generating an explanation for the recommendation comprising one or more phrases, wherein the selection of the one or more phrases is based, at least in part, on a plurality of user behaviors and in accordance with a computer-implemented syntactical structure comprises:
selecting phrases for inclusion in the explanation in accordance with the calculated confidence level of the recommendation.
8. A computer-based recommendation system comprising:
means to generate an affinity vector between a first user of a computer-based system and a plurality of computer-based objects based, at least in part, on the first user's behaviors;
means to generate a similarity metric between the first user and a second user of the computer-based system based, at least in part, on the affinity vector of the first user and an affinity vector of the second user;
means to generate a recommendation for delivery to the first user based, at least in part, on the affinity vector of the first user and the similarity metric; and
means to generate an explanation for the recommendation comprising one or more phrases, wherein the selection of the one or more phrases is based, at least in part, on a plurality of user behaviors and in accordance with a computer-implemented syntactical structure.
9. The system of claim 8 wherein means to generate an affinity vector between a first user of a computer-based system and a plurality of computer-based objects based, at least in part, on the first user's behaviors comprises:
means to generate affinities between the user and a plurality of topic objects.
10. The system of claim 8 wherein means to generate a recommendation for delivery to the first user based, at least in part, on the affinity vector of the first user and the similarity metric comprises:
means to recommend the second user to the first user.
11. The system of claim 8 wherein means to generate a recommendation for delivery to the first user based, at least in part, on the affinity vector of the first user and the similarity metric comprises:
means to generate a recommendation responsive to the user's search request.
12. The system of claim 8 wherein means to generate a recommendation for delivery to the first user based, at least in part, on the affinity vector of the first user and the similarity metric comprises:
means for generating a recommendation based on a recommendation preference setting established by the user.
13. The system of claim 8 wherein means to generate an explanation for the recommendation comprising one or more phrases, wherein the selection of the one or more phrases is based, at least in part, on a plurality of user behaviors and in accordance with a computer-implemented syntactical structure comprises:
means to select phrases for inclusion in the explanation based on a frequency distribution.
14. The system of claim 8 wherein means to generate an explanation for the recommendation comprising one or more phrases, wherein the selection of the one or more phrases is based, at least in part, on a plurality of user behaviors and in accordance with a computer-implemented syntactical structure comprises:
means to select phrases for inclusion in the explanation in accordance with the calculated confidence level of the recommendation.
15. A computer-based recommendation explanation system comprising:
means to generate a recommendation based, at least in part, on a plurality of usage behaviors;
a syntactical structure for an explanation of an associated recommendation;
a plurality of phrase arrays associated with the syntactical structure, wherein each phrase array comprises a plurality of phrases;
a mapping of usage behaviors and corresponding phrase arrays appropriate to apply; and
means to generate an explanation for the recommendation, wherein the explanation is generated in accordance with the syntactical structure and associated phrase arrays, and the mapping of usage behaviors with the corresponding phrase arrays appropriate to apply.
16. The system of claim 15 wherein means to generate an explanation for the recommendation, wherein the explanation is generated in accordance with the syntactical structure and associated phrase arrays, and the mapping of usage behaviors with the corresponding phrase arrays appropriate to apply comprises:
means to probabilistically select from alternative syntactical structures.
17. The system of claim 15 wherein means to generate an explanation for the recommendation, wherein the explanation is generated in accordance with the syntactical structure and associated phrase arrays, and the mapping of usage behaviors with the corresponding phrase arrays appropriate to apply comprises:
means to select phrases from a frequency distribution for inclusion in the explanation.
18. The system of claim 15 wherein means to generate an explanation for the recommendation, wherein the explanation is generated in accordance with the syntactical structure and associated phrase arrays, and the mapping of usage behaviors with the corresponding phrase arrays appropriate to apply comprises:
means to apply behavioral thresholds to trigger appropriate phrase arrays.
19. The system of claim 15 wherein means to generate an explanation for the recommendation, wherein the explanation is generated in accordance with the syntactical structure and associated phrase arrays, and the mapping of usage behaviors with the corresponding phrase arrays appropriate to apply comprises:
means to adjust the selection of phrases for inclusion in the explanation based on the phrase composition of previously generated explanations.
20. The system of claim 15 wherein means to generate an explanation for the recommendation, wherein the explanation is generated in accordance with the syntactical structure and associated phrase arrays, and the mapping of usage behaviors with the corresponding phrase arrays appropriate to apply comprises:
means to adjust the selection of phrases for inclusion in the explanation based on usage behaviors of recipients of previously generated explanations.
21. A method comprising:
storing user behavior information representing a plurality of user behaviors in a content network comprising content objects and relationships between the content objects, the content objects representing items of audio content or multimedia, the content objects associated with vectors, respectively, the vectors represented in an n-dimensional space, wherein each content object comprises meta information of the user behavior information and relationship information representing the relationships between the content objects;
wherein the relationship information represents a degree to which the corresponding content object is related to another content object of the content objects, wherein the relationship information is based, at least in part, on deriving a distance in the n-dimensional space between a first vector of the vectors and a second vector of the vectors, the first vector associated with a first content object of the content objects and the second vector associated with a second different content object of the content objects;
generating a frequency distribution of a plurality of candidate phrases used to assemble a natural language communication applying a syntactical structure, wherein the frequency distribution of the plurality of candidate phrases is selected based, at least in part, on the plurality of user behaviors; and
generating the natural language communication applying the syntactical structure for delivery to a user, the natural language communication applying the syntactical structure based, at least in part, on the generated frequency distribution.
22. The method of claim 21 , wherein the user comprises a first user of a computer-based system and the method further comprises:
generating an affinity vector between the first user and a plurality of computer-based objects based, at least in part, on the first user's behaviors; and
generating a similarity metric between the first user and a second user of the computer-based system based, at least in part, on the affinity vector;
wherein the natural language communication is generated based at least in part on the affinity vector and the similarity metric.
23. The method of claim 21 , wherein at least a portion of the user behavior information is captured by tracking user interactions with a user interface of an adaptive system.
24. The method of claim 23 , wherein the at least the portion of the user behavior information comprises user key strokes, mouse clicks, a time period between successive key strokes, a time period between successive mouse clicks, timestamps, or audio content or multimedia web page access.
25. The method of claim 21 , wherein the natural language communication is generated in a context of a current system navigation using a user interface of an adaptive system, a current access using the user interface, or a current activity using the user interface.
26. An apparatus including a processor and a computer-readable memory having instructions stored thereon that, in response to execution by the processor, cause the processor to perform operations comprising:
storing user behavior information representing a plurality of user behaviors in a content network comprising content objects and relationships between the content objects, the content objects associated with vectors, respectively, the vectors represented in an n-dimensional space and associated with meta information of the user behavior information and relationship information representing the relationships between the content objects;
wherein the relationship information represents a degree to which the corresponding content object is related to another content object of the content objects, wherein the relationship information is based, at least in part, on deriving a distance in the n-dimensional space between a first vector of the vectors and a second vector of the vectors, the first vector associated with a first content object of the content objects and the second vector associated with a second different content object of the content objects;
identifying a frequency distribution of a plurality of candidate phrases used to assemble a natural language communication applying a syntactical structure, wherein the frequency distribution of the plurality of candidate phrases is selected based, at least in part, on the plurality of user behaviors; and
generating the natural language communication applying the syntactical structure for delivery to the user, the natural language communication applying the syntactical structure based, at least in part, on the generated frequency distribution.
27. The apparatus of claim 26 , wherein the user comprises a first user of a computer-based system and the operations further comprise:
generating an affinity vector between the first user and a plurality of computer-based objects based, at least in part, on the first user's behaviors; and
generating a similarity metric between the first user and a second user of the computer-based system based, at least in part, on the affinity vector;
wherein the natural language communication is generated based at least in part on the affinity vector and the similarity metric.
28. The apparatus of claim 26 , wherein at least a portion of the user behavior information is captured by tracking user interactions with a user interface of an adaptive system.
29. The apparatus of claim 28 , wherein the at least the portion of the user behavior information comprises user key strokes, mouse clicks, a time period between successive key strokes, a time period between successive mouse clicks, timestamps, or audio content or multimedia web page access.
30. The apparatus of claim 26 , the multimedia includes at least one image.
31. The apparatus of claim 26 , wherein the natural language communication is generated in a context of a current system navigation using a user interface of an adaptive system, a current access using the user interface, or a current activity using the user interface.