Content Item Recommendations Based On Content Attribute Sequence

Patent metadata

Field	Value
Publication number	US-2016299906-A1
Application number	US-201514684063-A
Country	US
Kind code	A1
Filing date	Apr 10, 2015
Priority date	Apr 10, 2015
Publication date	Oct 13, 2016
Grant date	—

Abstract

User created playlists can be analyzed to create a statistical language model indicating the likelihood that a particular sequence of content attributes will be found in a playlist created by a user, as well as the likelihood of any sequence of one or more content attributes following a playlist or partial playlist created by a user. The language model can be used to generate a recommended content attribute sequence based on a partial playlist of one or more content items. A recommended content item sequence that will be pleasant to a user when added to the partial playlist can be selected based on the recommended content attribute sequence.

First claim

1 . A method comprising:
receiving, by a computer processor, data identifying a set of seed content items and a sequential order for the set of seed content items, wherein the set of seed content items includes at least one seed content item included in a first content item playlist, and the sequential order indicates an order in which seed content items from the set of seed content items are ordered to be performed in the first content item playlist; generating, by the computer processor, a seed content attribute sequence based on attributes of at least a first seed content item of the set of seed content items and the sequential order for the set of seed content items; generating, by the computer processor, a recommended content attribute sequence that is likely to follow the seed content attribute sequence, the recommended content attribute sequence determined from the seed content attribute sequence and a statistical language model based on an analysis of a set of reference content attribute sequences, wherein the set of reference content attribute sequences were generated from a set of user created content playlists; and selecting, by the computer processor, a set of recommended content items to be added to the first content item playlist and performed sequentially after the set of seed content items in a recommended sequential order, the set of recommended content item and the recommended sequential order selected based on content attributes of the set of recommended content items and the recommended content attribute sequence.

2 . The method of claim 1 , further comprising:
selecting a subset of a set of available content times yielding a set of candidate content items, wherein the first recommended content item is selected from the set of candidate content items.

3 . The method of claim 2 , further comprising:
determining a set of similarity rankings indicating similarity between content items in the set of available content items and at least one seed content item, wherein the selecting the subset of available content items is based on the set of similarity rankings.

4 . The method of claim 2 , further comprising:
gathering content preferences data describing a human user's preferences for content items, the human user having selected the set of seed content items, wherein the selecting the subset of available content items is based on the content preferences.

5 . The method of claim 1 , further comprising:
receiving a denial message indicating that the human user has denied at least a first recommended content item of the set of recommended content items; and generating an updated set of recommend content items, the updated set of recommended content item including an updated recommended content item in place of the first recommend content item, wherein the updated content item is not included in the set of recommended content items.

6 . The method of claim 1 , further comprising:
modifying the first content item playlist to include the set of recommended content item sequence, the set of recommended content items ordered in the first content item playlist to be performed after the set of seed content items and in the recommended sequential order.

7 . The method of claim 1 , further comprising:
gathering content item attributes for content items included in the set of user created content playlists; and generating the set of reference content attribute sequences based on the content item attributes for content items included in the set of user created content playlists and a set of sequential orders associated with the user created playlist, each sequential order of the set of sequential orders describing the order of content items in one of the user created playlists.

8 . A system comprising:
a computer processor; and a memory containing instructions that, when executed cause the computer processor to:
receive data identifying a set of seed content items and a sequential order for the set of seed content items, wherein the set of seed content items includes at least one seed content item included in a first content item playlist, and the sequential order indicates an order in which seed content items from the set of seed content items are ordered to be performed in the first content item playlist;
generate a seed content attribute sequence based on attributes of at least a first seed content item of the set of seed content items and the sequential order for the set of seed content items;
generate a recommended content attribute sequence that is likely to follow the seed content attribute sequence, the recommended content attribute sequence determined from the seed content attribute sequence and a statistical language model based on an analysis of a set of reference content attribute sequences, wherein the set of reference content attribute sequences were generated from a set of user created content playlists; and
select a set of recommended content items to be added to the first content item playlist and performed sequentially after the set of seed content items in a recommended sequential order, the set of recommended content item and the recommended sequential order selected based on content attributes of the set of recommended content items and the recommended content attribute sequence.

9 . The system of claim 8 , wherein the instructions further cause the computer processor to:
select a subset of a set of available content times yielding a set of candidate content items, wherein the first recommended content item is selected from the set of candidate content items.

10 . The system of claim 9 , wherein the instructions further cause the computer processor to:
determine a set of similarity rankings indicating similarity between content items in the set of available content items and at least one seed content item, wherein the selecting the subset of available content items is based on the set of similarity rankings.

11 . The system of claim 9 , wherein the instructions further cause the computer processor to:
gather content preferences data describing a human user's preferences for content items, the human user having selected the set of seed content items, wherein the selecting the subset of available content items is based on the content preferences.

12 . The system of claim 8 , wherein the instructions further cause the computer processor to:
receive a denial message indicating that the human user has denied at least a first recommended content item of the set of recommended content items; and generate an updated set of recommend content items, the updated set of recommended content item including an updated recommended content item in place of the first recommend content item, wherein the updated content item is not included in the set of recommended content items.

13 . The system of claim 8 , wherein the instructions further cause the computer processor to:
modify the first content item playlist to include the set of recommended content item sequence, the set of recommended content items ordered in the first content item playlist to be performed after the set of seed content items and in the recommended sequential order.

14 . The system of claim 8 , wherein the instructions further cause the computer processor to:
gather content item attributes for content items included in the set of user created content playlists; and generate the set of reference content attribute sequences based on the content item attributes for content items included in the set of user created content playlists and a set of sequential orders associated with the user created playlist, each sequential o

Assignees

• Apple Inc

Inventors

• Cartoon Daniel
• Levy Mark H

Classifications

• G06F16/4387 Primary

  by the use of playlists · CPC title

• G06F17/30684

  Physics · mapped topic

• G06F17/30053

  Physics · mapped topic

• G06F17/3064 Primary

  Physics · mapped topic