The present invention relates to a content recommendation system, a content
recommendation method, a content recommendation device, and an information
storage medium, and more particularly, to content recommendation technology.
Backgroimd Art
[0002]
Recently, it has become possible to enjoy what one desires from among a
great number of pieces of content by use of a communication network such as the
Internet. Various recommendation technologies have been proposed according to a
great number of pieces of available content. For example, in content-based filtering,
content to be recommended to a user among a number of pieces of content is
extracted according to similarity between feature information of content and feature
information of content preferred by the user.
Summary of Invention
Technical Problem
[0003]
However, the content-based filtering has a problem in that the same content is
continuously recommended to the user as long as the feature information of the
content preferred by the user does not change.
[0004]
The present invention has been made in view of the above-described
problem, and an object of the invention is to provide a content recommendation
system, a content recommendation method, a content reconmiendation device, and an
information storage medium that can diversify content to be recommended to a user.
Solution to Problem
[0005]
According to the present invention in order to solve the above-described
problem, there is provided a content reconmiendation system characterized in
including: a user attribute information acquisition means for acquiring attribute
2
information of a given user; a list acquisition means for acquiring at least one list
from among lists of content sequentially generated over time on the basis of the
attribute information of the given user acquired by the user attribute information
acquisition means; a user preference information acquisition means for acquiring
user preference information, which is feature information of content preferred by the
given user; a content extraction means for extracting some content from content
included in the list acquired by the list acquisition means on the basis of the user
preference information acquired by the user preference information acquisition
means; and a content presentation means for presenting the content extracted by the
> content extraction means to the given user.
[0006]
According to the present invention, there is provided a content
recommendation method characterized in including: a user attribute information
acquisition step of acquiring attribute information of a given user; a list acquisition
step of acquiring at least one list from among lists of content sequentially generated
over time; a liser preference information acquisition step of acquiring user preference
information, which is feature information of content preferred by the given user; a
content extraction step of extracting some content from content included in the list
acquired in the list acquisition step on the basis of the user preference information
I acquired in the user preference information acquisition step; and a content
presentation step of presenting the content extracted in the content extraction step to
the given user.
[0007]
According to the present invention, there is provided a content
recommendation device characterized in including: a user attribute information
acquisition means for acquiring attribute information of a given user; a list
acquisition means for acquiring at least one list from among lists of content
sequentially generated over time; a user preference information acquisition means for
acquiring user preference information, which is feature information of content
preferred by the given user; a content extraction means for extracting some content
from content included in the list acquired by the list acquisition means on the basis of
3
the user preference information acquired by the user preference information
acquisition means; and a content presentation means for presenting the content
extracted by the content extraction means to the given user.
[0008]
According to the present invention, there is provided a program for causing a
computer to function as: a \iser attribute information acquisition means for acqviiring
attribute information of a given user; a list acquisition means for acquiring at least
one list from among lists of content sequentially generated over time; a user
preference information acquisition means for acquiring user preference information,
which is feature information of content preferred by the given user; a content
extraction means for extracting some content from content included in the list
acquired by the list acquisition means on the basis of the user preference information
acquired by the user preference information acquisition means; and a content
presentation means for presenting the content extracted by the content exfraction
means to the given user. An information storage medium according to the present
invention stores the above-described program.
Brief Description of Drawings
[0009]
[Fig. 1] Fig. 1 is an entire configuration diagram of a content recommendation
system according to an embodiment of the present invention.
[Fig. 2] Fig. 2 is a hardware configuration diagram of a server.
[Fig. 3] Fig. 3 is a hardware configuration diagram of a user device.
[Fig. 4] Fig. 4 is an external perspective diagram of the user device.
[Fig. 5] Fig. 5 is an external perspective diagram of a user device according to a
modified example.
[Fig. 6] Fig. 6 is a ftmctional block diagram of the user device.
[Fig. 7] Fig. 7 is a fimctional block diagram of a music distribution server.
[Fig. 8] Fig. 8 is a diagram showing storage content of a user information storage
vmit.
[Fig. 9] Fig. 9 is a configuration diagram of an aggregate database (DB).
4
[Fig. 10] Fig. 10 is a diagram showing storage content of an internal ranking storage
luiit.
[Fig. 11] Fig. 11 is a diagram showing ranking synthesis.
[Fig. 12] Fig. 12 is a configuration diagram of a music feature DB.
[Fig. 13] Fig. 13 is a configuration diagram of a music attribute DB.
[Fig. 14] Fig. 14 is an operation sequence diagram of the content recommendation
system according to an embodiment of the present invention.
Description of Embodiments
I [0010]
Hereinafter, preferred embodiments of the present invention will be described
in detail with reference to the appended drawings. Note that, in this specification and
the drawings, elements that have substantially the same function and structure are
denoted with the same reference signs, and repeated explanation is omitted.
> [0011]
Fig. 1 is an entire configuration diagram of a content recommendation system
according to an embodiment of the present invention. As shown in the same
drawing, the content recommendation system 10 is cormected to a data
commvinication network 18 such as the Intemet, and includes a music distribution
I server 14, a music ranking distribution server 15, and a plurality of user devices 12,
which can perform data commxmication with each other. The user device 12 is, for
example, a computer system such as a personal computer, a computer game system,
or a home server installed at home or a portable computer system such as a portable
game device or a mobile phone, and receives a list of music to be recommended to a
user of the user device 12 by accessing the music distribution server 14. The \iser
device 12 requests the music distribution server 14 to provide music data included in
the list, and receives and reproduces the music data. On the other hand, the music
distribution server 14 is constituted, for example, by a computer system such as a
well-known server computer, and transmits a list of music to be reconmiended to a
' user of each user device 12 to the user device 12. Individual music data is
transmitted in response to a request from each user device 12. Here, although an
5
application example of the present invention for a music recommendation has been
described, the present invention is not limited thereto. Of course, it is possible to
apply the present invention to recommend various content, for example, moving
images such as movies, still images such as photos, docimients such as novels, and
the like. The music ranking distribution server 15 is constituted, for example, by a
computer system such as a well-known server computer managed by a person
different from a manager of the music distribution server 14, and returns music
rankings in response to a request from the music distribution server 14. The music
rankings are issued, for example, periodically such as weekly or monthly, for
example, according to a music genre such as pop, jazz, or classic, by nationality, and
each ranking is stored in the music distribution server 14 in association with an
issuance time and a music genre. Rankings may be created in various viewpoints,
and may be, for example, music rankings according to an increase in the number of
purchases, music rankings according to an increase in the number of downloads,
music rankings according to an increase in the nimiber of times information related
to music (for example, supporting documentation) is browsed, or the like.
[0012]
Fig. 2 is a diagram showing a hardware configuration example of the music
distribution server 14 and the music ranking distribution server 15. As shown in the
same drawing, the music distribution server 14 or the music ranking distribution
server 15 includes a processor 20, a memory 21, a hard disk drive (HDD) 23, a
medium drive 24, and a communication interface (I/F) 26, which are cormected to a
bus 22 and configured to exchange data with one another. The memory 21 is
constituted by a read only memory (ROM) and a random access memory (RAM).
The ROM stores varioiis system programs, and the RAM is mainly used to work for
the processor 20. In the HDD 23, a program for music distribution or music ranking
distribution is stored, or various DBs for the music distribution or music ranking
distribution are constructed. The medium drive 24 is a device that reads data stored
in a computer-readable mediimi 25 such as a compact disk-read only memory (CDROM)
or a digital versatile disk-random access memory (DVD-RAM), or writes data
thereto. The communication I/F 26 controls an operation in which the music
6
distribution server 14 or the miisic ranking distribution server 15 performs data
communication with another computer system via the commimication network 18.
The processor 20 controls each part of the server according to a program stored in the
memory 21, the HDD 23, or the medivim 25.
[0013]
On the other hand. Fig. 3 is a diagram showing a hardware configuration
example of the user device 12. As shown in the same drawing, the user device 12
includes a processor 31, a memory 32, a display control unit 33, a soimd control unit
34, an HDD 35, manipulation devices 36, a medium drive 37, and a communication
I/F 38, which are connected to a bus 39 and configured to exchange data with one
another. The memory 32 is constituted by a ROM and a RAM. The ROM stores
various system programs, and the RAM is mainly used to work for the processor 31.
In the HDD 35, various programs such as a music reproduction program are stored,
or various DBs are constructed. The medium drive 37 is a device that reads data
stored in a computer-readable medium 40 such as a CD-ROM or a DVD-RAM, or
writes data thereto. The coirmiunication I/F 38 controls an operation in which the
user device 12 performs data commimication with another computer system such as
the music distribution server 14 via the communication network 18. The display
control unit 33 is configured to include a video memory, and converts an image
drawn in the video memory by the processor 31 into a video signal, outputs the video
signal to a display, and causes the image to be displayed. The sound control unit 34
is configured to include a sound buffer, and the processor 31 converts sound data
stored in the sound buffer into an analog soimd signal and outputs the analog sound
signal to speakers to be audibly outputted. The manipulation devices 36 are means
for allowing the user to provide an instruction to the user device 12, and are, for
example, a keyboard, a pointing device such as a mouse, a gamepad, or the like.
Fiirthermore, the program 31 controls each part of the server device 12 according to a
program stored in the memory 32, the HDD 35 or the medium 40.
[0014]
The user device 12 may be implemented in various types, and configured, for
example, as a stationary game console operable by household power as shown in Fig.
7
4. In this case, each hardware element shown in Fig. 3 is accommodated in a
housing 43, and a display device 41a and built-in speakers 42 and 42 of a television
receiver 41 separate from the housing 43 are used as the display and the speakers.
The manipulation devices 36 are also provided separately from the housing 43.
[0015]
Alternatively, the user device 12 may be configured as a portable and
integrated game device operable by a battery shown in Fig. 5. In this case, each
hardware element shown in Fig. 3 is accommodated in a housing 44, and fiirthermore
a flat panel display 45 provided on a surface of the housing 44 is used as the display.
The manipulation devices 36 are also provided on the surface of the housing 44, and
are arranged, for example, on the left and right of the flat panel display 45. Speakers
(not shown) embedded in the housing 44 or stereo headphones 46 separate from the
housing 44 may be used as the speakers.
[0016]
Here, a fimctional configuration of the user device 12 will be described. Fig.
6 is a functional block diagram of the user device 12. As shown in the same
drawing, the user device 12 is configured to fimctionally include a manipulation unit
61 and a music reproduction unit 62. The above-described fimctional elements are
implemented by a program executed in the user device 12. The manipulation imit 61
is configured around the manipiilation devices 36, and transmits a request of a music
list (including a user ID) to the music distribution server 14 via the commxmication
I/F 38 if a specific request manipulation to the manipulation devices 36 is performed.
In the request, a user ID and a designation of an attribute of music (for example, a
mood such as relaxed, ballad, happy, or active) are included. The attribute of music
may be designated by the user using the manipulation devices 36, or may be
randomly selected. A preference vector to be described later may be managed by the
user device 12. In this case, user preference information managed may be included
in the request of the music list. The music reproduction unit 62 to be described later
receives a music list returned by the music distribution server 14 via the
communication network 18 in response to the request, and transmits identification
information of each piece of music included in the music list, that is, a music ID, to
8
the music distribution server 14 via the communication I/F 38. The music
reproduction imit 62 reproduces music data returned from the music distribution
server 14 using the soimd control unit 34 in response to the transmission of the music
ID. At this time, the music reproduction unit 62 displays song title data included in
the music data on the display as shown in Figs. 4 and 5.
[0017]
When an exphcit negative manipulation (a specific manipulation indicating
that the user does not like the corresponding music) to the manipulation devices 36 is
performed during music reproduction, the manipulation unit 61 transmits a negative
evaluation to the music distribution server 14 along with a music ID of reproduced
music and a user ID. When a skip manipulation to the manipulation devices 36 is
performed during music reproduction, the music reproduction imit 62 is notified of
the skip manipulation. The music reproduction vinit 62 stops the reproduction of
music according to the notification, transmits the next music ID to the music
distribution server 14, and reproduces returned music data. Even when the skip
manipulation is performed as described above, the manipulation unit 61 may be
configured to transmit a negative evaluation to the mvisic distribution server 14 along
with a music ID of reproduced music and a user ID. When mxisic is fiiUy reproduced
without being skipped, the music reproduction imit 62 notifies the manipulation unit
61 of the fact that the music has been fiiUy reproduced without being skipped. In this
case, the manipulation unit 61 transmits a positive evaluation to the music
distribution server 14 along with a music ID of fiiUy reproduced music and a user ID.
Furthermore, when an explicit positive manipulation (a specific manipulation
indicating that the user likes the corresponding music) to the manipulation devices 36
is performed during music reproduction, a positive evaluation is transmitted to the
music distribution server 14 along with a music ID of reproduced music and a user
ID. The positive or negative evaluation generated for each piece of music delivered
to the music distribution server 14 is used to generate a preference vector indicating a
feature of music preferred by each user or generate rankings of pieces of music
preferred by a plurality of users in the music distribution server 14.
[0018]
9
Next, a functional configuration of the music distribution server 14 will be
described. Fig. 7 is a functional block diagram of the music distribution server 14.
As shown in the same drawing, the music distribution server 14 is configiired to
fiinctionally include a user information storage unit 71, an aggregate imit 72, an
internal ranking generation unit 73, an internal ranking storage unit 74, a ranking
selection and synthesis unit 75, a preference vector update unit 77, a preference
vector storage unit 78, a sorting unit 79, a secondary list generation unit 80, a
primary list storage unit 81, a music attribute DB 82, a music feature DB 83, a music
distribution imit 84, and a music DB 85. These functional elements are implemented
by a program executed in the music distribution server 14.
[0019]
The user information storage imit 71 is configured using the HDD 23 or a
separate DB (not shown), and stores attributes of users. Fig. 8 schematically shows
storage content of the user information storage unit 71. User IDs, which are
identification information of users, and attributes of the users such as age, a city of
residence, and use langixage are associated and stored. The storage content of the
user information storage unit 71 may be pre-registered from the user device 12.
[0020]
The aggregate unit 72 aggregates information related to an evaluation
transmitted from the user device 12 and information related to a music list
transmitted to the user device 12. The aggregate unit 72 includes an aggregate DB
72a configured using the HDD 23 or a separate DB (not shown). An aggregate result
is stored in the aggregate DB 72a. Fig. 9 schematically shows storage content of the
aggregate DB 72a in which a music ID, which is music identification information,
and an aggregate value x-y-z of a mer attribute range (in which age is 13 years old or
less, a city of residence is Tokyo, use language is Japanese, and the like) are
associated and stored. Here, the aggregate value includes three numeric values: the
number of times x included in a music list transmitted to the user device 12, the
nimiber of times y a positive evaluation for corresponding music is transmitted from
the user device 12, and the nimiber of times z a negative evaluation for
corresponding music is transmitted from the user device 12. If a music ID, a user ID,
10
and a positive or negative evaluation are received from the user device 12, the
aggregate unit 72 reads attributes of a user associated with the user ID received from
the user information storage unit 71, and updates the number of times y or the
number of times z in an aggregate value of a user attribute range to which the
attributes of the user belong. Specifically, the number of times y is incremented by 1
if the positive evaluation is received, and the number of times z is incremented by 1
if the negative evaluation is received. When the sorting xmit 79 generates a music list
and transmits the generated music list to the user device 12, a music ID and a user ID
of a user of the user device 12 of a destination of the music list included in the music
list are acquired, and attributes of the user associated with the user ID are read from
the user information storage unit 71. In the aggregate value of the user attribute
range to which the read attributes belong, the nimiber of times x described above is
incremented by 1.
[0021]
The internal ranking generation unit 73 generates a ranking for a range of
various user attributes periodically such as weekly or monthly on the basis of storage
content of the aggregate DB 72a, and stores the ranking in the internal ranking
storage unit 74. As shown in Fig. 10, the internal ranking storage unit 74 is
configured using the HDD 23 or a separate DB (not shown), and various rankings
generated by the internal ranking generation unit 73 are stored in association with
generation times and user attribute ranges thereof. For example, rankings of pieces
of music preferred by a user who is 15 years old or less, whose city of residence is
Tokyo, and whose use language is Japanese are generated by listing IDs of a
predetermined number of (for example, 100) pieces of music in descending order of
total values of the niunber of times y recorded in columns of "13 years old or
less/Tokyo/Japanese," "14 years old/Tokyo/Japanese," and "15 years
old/Tokyo/Japanese" shown in Fig. 9. At this time, the rankings may be generated
by listing a predetermined niraiber of music IDs in descending order of a ratio of a
total value of the number of times y to a total value of the nimiber of times x
described above, that is, a ratio of the nimiber of positive evaluations to the number
of recommendations.
11
[0022]
The ranking selection and synthesis unit 75 reads the attributes of the user
associated with the user ID included in the request of the music list transmitted from
the user device 12 from the user information storage imit 71, and reads rankings
associated with a range including the attributes of the user from the internal ranking
storage imit 74. The rankings corresponding to the attributes of the user are received
from the music ranking distribution server 15. For example, the latest rankings in the
city of residence of the user may be received, and the rankings issued in the city of
residence of the user when the user was 15 years old may be received on the basis of
the age of the user. The ranking selection and synthesis imit 75 receives the rankings
associated with the attributes of the user as described above and synthesizes the
rankings as shown in Fig. 11. At this time, all music IDs included in rankings may
not be included in a list after synthesis.
[0023]
The ranking selection and synthesis unit 75 may read the rankings associated
with the range including the attributes of the user and rankings associated with a
range adjacent thereto from the internal ranking storage unit 74, and synthesize the
rankings. In this case, the number of pieces of music included in the list after the
synthesis from the rankings associated with the range including the attributes of the
user may be less than the number of pieces of music included in the list after the
synthesis from the rankings associated with the adjacent range. The rankings
synthesized by the ranking selection and synthesis imit 75, that is, a list of music IDs,
are stored in the primary list storage unit 81 configured using the HDD 23 or a
separate DB (not shown).
[0024]
Next, the music feature DB 83 is configured using the HDD 23 or a separate
DB (not shown) in which music IDs and a plurality of feature quantities are
associated and stored as shown in Fig. 12. The features are, for example, a music
tempo, a degree of so\md of a specific frequency included in music, a frequency of a
specific keyword included in expository text of music, and the like. The feature
quantities may be input as results of analysis processing by a computer. Hereinafter,
12
a vector having each feature quantity as a component is referred to as a feature
vector.
[0025]
The preference vector storage unit 78 is configured using the HDD 23 or a
separate DB (not shown), and stores a preference vector of each user. Like the
feature vector of each piece of music, the preference vector is a vector having each
feature quantity as a component shown in Fig. 12. The preference vector may be
generated on the basis of a feature vector of music preferred by the user.
[0026]
The preference vector update unit 77 updates a preference vector stored in the
preference vector update unit 77 on the basis of information related to an evaluation
for each piece of music transmitted from the user device 12. For example, if a music
ID, a user ID, and a positive evaluation are received from the user device 12, a
feature vector associated with the received music ID is read from the music feature
DB 83, and a direction of the preference vector stored in the preference vector
storage unit 78 associated with the received user ID is set to be close to a direction of
the read feature vector. The preference vector update unit 77 is provided in the side
of the user device 12, so that the preference vector update imit 77 within the user
device 12 can update the preference vector stored in the user device 12 on the basis
of information related to an evaluation for each piece of music obtained by the
manipulation unit 61. At this time, the preference vector update unit 77 may
download a feature vector of music from the music feature DB 83 within the music
distribution server 14, or a DB equal to the music feature DB 83 is provided in the
user device 12, so that the feature vector of music can be read from the DB.
Thereby, a preference vector constantly managed within the user device 12 to be
most recent may be included in a request of the music list as described above and
transmitted to the music distribution server 14. The music distribution server 14
receiving the preference vector stores the preference vector in the preference vector
storage unit 78 of the music distribution server 14.
[0027]
The music attribute DB 82 is configured using the HDD 23 or a separate DB
13
(not shown) in which a music ID and flags indicating whether music corresponds to a
plurality of respective music attributes are stored to be associated with each other, as
shown in Fig. 13. The music attribute may be, for example, a mood such as relaxed,
ballad, happy, active, and the like, and may be input as a result of analysis processing
by a computer.
[0028]
The secondary list generation unit 80 reads flags associated with each music
ID included in a list stored in the primary list generation unit 81 from the music
attribute DB 82, and determines whether or not there is m\isic suitable for attributes
included in a request transmitted from the user device 12. Only music IDs of pieces
of music suitable for the attributes included in the request transmitted from the user
device 12 are extracted from music IDs included in the list stored in the primary list
storage unit 81, and a list of the extracted music IDs is supplied to the sorting unit 79
as a secondary list. The sorting unit 79 reads feature vectors associated with the
music IDs included in the secondary list from the music feature DB 83, and reads a
preference vector stored in the preference vector storage imit 78 in association with
the user ID included in the request received from the user device 12. Similarity
between two vectors is calculated and music IDs are sorted in descending order of
similarity. The similarity between vectors may be an angle between two vectors. In
this case, the smaller the angle, the higher the similarity. The sorting unit 79
includes a predetermined number of (for example, 10) music IDs in a
recommendation music list in descending order of similarity between two vectors,
and returns the same list to the user device 12.
[0029]
The music DB 85 is configiored using the HDD 23 or a separate DB (not
shown), and stores data of music associated with a music ID. The music distribution
unit 84 receives a music ID from the user device 12, reads music data stored in
association with the music ID from the music DB 85, and returns the music data to
the user device 12.
[0030]
Here, an operation of the content recommendation system 10 according to
14
this embodiment will be described. Fig. 14 is an operation sequence diagram of the
content recommendation system 10. As shown in the same drawing, the user device
12 transmits a music list request including a user ID and a music attribute to the
music distribution server 14 (SI01). In the music distribution server 14, the ranking
selection and synthesis unit 75 reads user attributes associated with the received user
ID from the user information storage imit 71 (SI02), and acquires rankings
corresponding to the read user attributes from the internal ranking storage unit 74 and
the music ranking distribution server 15 (SI03 and SI04). The acquired rankings are
synthesizedandstoredintheprimary list storage unit 81 (SI05). Next, in the
music distribution server 14, the secondary list generation unit 80 generates a
secondary list by narrowing down music IDs stored in the primary list storage unit 81
on the basis of music attributes (SI06). Furthermore, the sorting unit 79 sorts music
IDs included in the secondary list according to similarities between feature vectors
associated with the music IDs and a preference vector of a user (SI 07), and generates
a recommendation music list from a predetermined number of music IDs in
descending order of similarity to return the recommendation music list to the user
device 12 (SI08). In the liser device 12, the music reproduction imit 62 receives the
recommendation music list and transmits the music IDs included in the
recommendation music list to the music distribution server 14 (SI09). The music
distribution server 14 returns music data associated with the music IDs received by
the music distribution unit 84 to the user device 12 (SI 10). The music reproduction
unit 62 of the user device 12 reproduces the received music data. Likewise, if music
reproduction for all the music IDs included in the recommendation music list is
performed, the user device 12 re-executes the process of SI01.
[0031]
Because a recommendation mvisic list is generated using various rankings,
which are a list of content sequentially generated over time, the above-described
content recommendation system 10 can prevent a problem of the same music being
continuously recommended to the user, and recommend various music to the user.
[0032]
The present invention is not limited to the above-described embodiments, and
15
various modifications are possible. Although an example in which the music
distribution server 14 generates a recommendation music list has been described
above, the functions shown in Fig. 7, particularly, the ranking selection and synthesis
imit 75, the primary list storage imit 81, the secondary list generation imit 80, the
sorting unit 79, the preference vector storage unit 78, and the preference vector
update unit 77, are provided in the user device 12, so that the user device 12 can
generate a recommendation rnusic list.
In so far as the embodiments of th^ invention described above are implemented, at least in part,
using software-controlled data processing apparatus, it will be appreciated that a computer
program providing such software control and a transmission, storage or other medium by which
such a computer program is provided are envisaged as aspects of the present invention.

We Claim:
Claim 1
A content recommendation system characterized in comprising:
a user attribute information acquisition means for acquiring attribute
information of a given user;
a list acquisition means for acquiring at least one list from among lists of
content sequentially generated over time on the basis of the attribute information of
the given user acquired by the user attribute information acquisition means;
a user preference information acquisition means for acquiring \iser preference
information, which is feature information of content preferred by the given user;
a content extraction means for extracting some content from content included
in the list acquired by the list acquisition means on the basis of the user preference
information acquired by the user preference information acquisition means; and
a content presentation means for presenting the content extracted by the
content extraction means to the given user.
Claim 2
The content recommendation system according to claim 1, characterized in
that the content extraction means extracts some content from the content included in
the list acquired by the list acquisition means on the basis of attributes of the content,
and ftirther extracts some content from some extracted content on the basis of the
feature information of the some content and the user preference information acquired
by the user preference information acquisition means.
Claims
The content recommendation system accordmg to claim 1 or 2, characterized
in that:
the list acquisition means acquires two or more lists, and
the content extraction means extracts some or all content from content
included in each of the two or more lists acquired by the list acquisition means, and
17
extracts some content based on the user preference information acquired by the user
preference information acquisition means from the extracted content.
Claim 4
The content recommendation system according to claim 3, characterized in
that the content extraction means extracts pieces of content of which the number
corresponds to the attribute information of the given user from the content included
in each of the two or more lists acquired by the list acquisition means, and extracts
some content based on the user preference information acquired by the user
preference information acquisition means from the extracted content.
Claims
The content recommendation system according to any one of claims 1 to 3,
characterized in fiirther comprising:
a list generation means for sequentially generating lists of content over time;
and
a list storage means for storing the lists generated by the list generation means
in association with a user attribute information range,
wherein the list acquisition means acquires the lists stored in the list storage
means in association with the attribute information of the given user.
Claim 6
The content recommendation system according to claim 5, characterized in
that the list generation means calculates a ratio between the number of times the
content presentation means has presented each piece of the content to the user and
the number of times the user corresponding to attribute information of each range has
performed a specific manipulation for the content, and generates a list for each
attribute information range on the basis of the ratio.
Claim?
A content recommendation method characterized in comprising:
18
a user attribute information acquisition step of acquiring attribute information
of a given user;
a list acquisition step of acquiring at least one list from among lists of content
sequentially generated over time;
a user preference information acquisition step of acquiring user preference
information, which is feature information of content preferred by the given user;
a content extraction step of extracting some content from content included in
the list acquired in the list acquisition step on the basis of the user preference
information acquired in the user preference information acquisition step; and
a content presentation step of presenting the content extracted in the content
extraction step to the given user.
Claims
A content recorrmiendation device characterized in comprising:
a user attribute information acquisition means for acquiring attribute
information of a given user;
a list acquisition means for acquiring at least one list from among lists of
content sequentially generated over time;
a user preference information acquisition means for acquiring user preference
information, which is feature information of content preferred by the given user;
a content extraction means for extracting some content from content included
in the list acquired by the list acquisition means on the basis of the user preference
information acquired by the user preference information acquisition means; and
a content presentation means for presenting the content extracted by the
content extraction means to the given user.
Claim 9
An information storage medium storing a program for causing a computer to
fimction as:
a user attribute information acquisition means for acquiring attribute
information of a given user;
19
a list acquisition means for acquiring at least one list from among lists of
content sequentially generated over time;
a user preference information acquisition means for acquiring user preference
information, which is feature information of content preferred by the given user;
a content extraction means for extracting some content from content included
in the list acquired by the list acquisition means on the basis of the user preference
information acquired by the user preference information acquisition means; and
a content presentation means for presenting the content extracted by the
content extraction means to the given user.
Dated this 05/01/2012