US2018322131A1PendingUtilityA1
System and Method for Content-Based Media Analysis
Est. expiryFeb 8, 2036(~9.5 yrs left)· nominal 20-yr term from priority
G06N 3/08G06Q 30/0631G06F 17/3071G06N 20/00G06F 16/355
47
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Claims
Abstract
A media analysis system includes one or more hardware processors, a memory storing synopses associated with media items, and a content analysis engine. The content analysis engine generates a media vector for each media item based on the associated synopsis by generating a word vector for each word in the synopsis, combining the plurality of word vectors into a mean vector for the media item, and storing the mean vector as the media vector associated with the media item. The content analysis engine also identifies a target media item associated with a seed media vector, determines R nearest neighbors for the target media item from the plurality of media items based on (1) the seed media vector and (2) the media vectors associated with the plurality of media items, clusters the R nearest neighbors into K clusters, and selects media items for recommendation to a user based on the K clusters.
Claims
exact text as granted — not AI-modified1 . A media analysis system comprising:
one or more hardware processors; a memory storing synopses associated with a plurality of media items, each media item in the plurality of media items including a different synopsis; and a content analysis engine, executable by the one or more hardware processors, configured to perform operations comprising:
generating a different media vector for each media item of the plurality of media items based on the synopsis of the media item, the generating comprising:
generating a word vector for each word of a plurality of words in the synopsis of the media item, thereby generating a plurality of word vectors;
combining the plurality of word vectors into a mean vector for the media item, the mean vector being the media vector; and
storing the mean vector, in the memory, as the media vector associated with the media item;
identifying a target media item, the target media item associated with a seed media vector;
determining R nearest neighbors for the target media item from the plurality of media items based on (1) the seed media vector and (2) the media vectors associated with the plurality of media items;
clustering the R nearest neighbors for the target media item into K clusters; and
selecting a second plurality of media items for recommendation to a user based on the K clusters.
2 . The media analysis system of claim 1 , wherein generating the word vector for each word of the plurality of words in the synopsis of the media item comprises generating the word vector using word2vec.
3 . The media analysis system of claim 1 ,
wherein the synopsis of the media item includes N words, wherein the plurality of word vectors includes a multiset of word vectors, X, of N word vectors {x 1 , x 2 , . . . , x N }, and wherein combining the plurality of word vectors into the mean vector, v, comprises computing:
v
=
1
N
∑
i
=
1
N
x
i
.
4 . The media analysis system of claim 1 , wherein the content analysis engine is further configured to perform operations comprising:
computing a cosine distance, cosine(s,t), between each pairing of the seed media vector, s, and the associated media vector, t, of each media item of the plurality of media items:
cosine
(
s
,
t
)
=
1
-
s
·
t
||
s
||
||
t
||
,
wherein s·t is the dot product of the vectors s and t, thereby generating an associated cosine distance for each media item of the plurality of media items; and
determining the R nearest neighbors for the target media item from the plurality of media items further based on the associated cosine distance for each media item of the plurality of media items.
5 . The media analysis system of claim 1 , wherein the content analysis engine is further configured to perform operations comprising selecting one media item from each of the K clusters.
6 . The media analysis system of claim 1 , wherein the content analysis engine is further configured to perform operations comprising:
clustering the seed media vector into a seed cluster, the seed cluster being one of the K clusters, wherein selecting the second plurality of media items based on the K clusters excludes the seed cluster.
7 . The media analysis system of claim 1 , wherein the content analysis engine is further configured to perform operations comprising:
generating a media vector for each catalog movie of a plurality of catalog movies based on an associated synopsis of each catalog movie, wherein determining the R nearest neighbors for the target media item further includes identifying the R nearest neighbors from the plurality of catalog movies, and further based on the media vectors associated with the plurality of catalog movies.
8 . A computer-implemented method for content-based media analysis, the method comprising:
generating a different media vector for each media item of a plurality of media items based on a synopsis of the media item, the generating comprising:
generating a word vector for each word of a plurality of words in the synopsis of the media item, thereby generating a plurality of word vectors;
combining the plurality of word vectors into a mean vector for the media item, the mean vector being the media vector; and
storing the mean vector, in a memory, as the media vector associated with the media item;
identifying a target media item, the target media item associated with a seed media vector; determining R nearest neighbors for the target media item from the plurality of media items based on (1) the seed media vector and (2) the media vectors associated with the plurality of media items; clustering the R nearest neighbors for the target media item into K clusters; and selecting a second plurality of media items for recommendation to a user based on the K clusters.
9 . The method of claim 8 , wherein generating the word vector for each word of the plurality of words in the synopsis of the media item comprises generating the word vector using word2vec.
10 . The method of claim 8 ,
wherein the synopsis of the media item includes N words, wherein the plurality of word vectors includes a multiset of word vectors, X, of N word vectors {x 1 , x 2 , . . . , x N }, and wherein combining the plurality of word vectors into the mean vector, v, includes computing:
v
=
1
N
∑
i
=
1
N
x
i
.
11 . The method of claim 8 , further comprising:
computing a cosine distance, cosine(s,t), between each pairing of the seed media vector, s, and the associated media vector, t, of each media item of the plurality of media items:
cosine
(
s
,
t
)
=
1
-
s
·
t
||
s
||
||
t
||
,
wherein s·t is the dot product of the vectors s and t, thereby generating an associated cosine distance for each media item of the plurality of media items; and
determining the R nearest neighbors for the target media item from the plurality of media items further based on the associated cosine distance for each media item of the plurality of media items.
12 . The method of claim 8 , further comprising selecting one media item from each of the K clusters.
13 . The method of claim 8 , further comprising:
clustering the seed media vector into a seed cluster, the seed cluster being one of the K clusters, wherein selecting the second plurality of media items based on the K clusters excludes the seed cluster.
14 . The method of claim 8 , further comprising:
generating a media vector for each catalog movie of a plurality of catalog movies based on an associated synopsis of each catalog movie, wherein determining the R nearest neighbors for the target media item further includes identifying the R nearest neighbors from the plurality of catalog movies, and further based on the media vectors associated with the plurality of catalog movies.
15 . A non-transitory machine-readable medium storing processor-executable instructions which, when executed by a processor, cause the processor to:
generate a different media vector for each media item of a plurality of media items based on a synopsis of the media item, the generating comprising:
generating a word vector for each word of a plurality of words in the synopsis of the media item, thereby generating a plurality of word vectors;
combining the plurality of word vectors into a mean vector for the media item, the mean vector being the media vector; and
storing the mean vector, in a memory, as the media vector associated with the media item;
identify a target media item, the target media item is associated with a seed media vector; determine R nearest neighbors for the target media item from the plurality of media items based on (1) the seed media vector and (2) the media vectors associated with the plurality of media items; cluster the R nearest neighbors for the target media item into K clusters; and select a second plurality of media items for recommendation to a user based on the K clusters.
16 . The machine-readable medium of claim 15 ,
wherein the synopsis of the media item includes N words, wherein the plurality of word vectors includes a multiset of word vectors, X, of N word vectors {x 1 , x 2 , . . . , x N }, and wherein combining the plurality of word vectors into the mean vector, v, includes computing:
v
=
1
N
∑
i
=
1
N
x
i
.
17 . The machine-readable medium of claim 15 , wherein the processor-executable instructions further cause the processor to:
compute a cosine distance, cosine(s,t), between each pairing of the seed media vector, s, and the associated media vector, t, of each media item of the plurality of media items:
cosine
(
s
,
t
)
=
1
-
s
·
t
||
s
||
||
t
||
,
wherein s·t is the dot product of the vectors s and t, thereby generating an associated cosine distance for each media item of the plurality of media items; and
determine the R nearest neighbors for the target media item from the plurality of media items further based on the associated cosine distance for each media item of the plurality of media items.
18 . The machine-readable medium of claim 15 , wherein the processor-executable instructions further cause the processor to select one media item from each of the K clusters.
19 . The machine-readable medium of claim 15 , wherein the processor-executable instructions further cause the processor to:
cluster the seed media vector into a seed cluster, the seed cluster being one of the K clusters, wherein selecting the second plurality of media items based on the K clusters excludes the seed cluster.
20 . The machine-readable medium of claim 15 , wherein the processor-executable instructions further cause the processor to:
generate a media vector for each catalog movie of a plurality of catalog movies based on an associated synopsis of each catalog movie, wherein determining the R nearest neighbors for the target media item further includes identifying the R nearest neighbors from the plurality of catalog movies, and further based on the media vectors associated with the plurality of catalog movies.Join the waitlist — get patent alerts
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