JP6065676B2 - Information processing program, information processing method and apparatus - Google Patents

Description

  The present technology relates to a technology for providing content recommendation information.

  Libraries often have one purpose to increase the library's usage rate by increasing the chances of users meeting and borrowing as many books as possible.

  From the point of view of book recommendation, for example, an online sales site may use a function of recommending a book purchased by another person who purchased the same book when purchasing the book. However, there are many cases where you just purchase a book together and you are not interested in other books purchased by others.

  There are other technologies for recommending content, but appropriate content is not necessarily recommended for the user.

JP 2012-103832 A JP 2008-310385 A

  Accordingly, an object of the present technology is, according to one aspect, to provide a new technology for enabling appropriate content to be recommended to a user.

  In the information processing method according to the present technology, (A) another data stored for the same period as the specified content from the first data storage unit that stores the data of the popular content in each of a plurality of periods (B) including a process of outputting recommendation information including at least a part of the extracted other content data.

  Appropriate content can be recommended to users.

FIG. 1 is a diagram illustrating an overview of a system according to an embodiment. FIG. 2 is a functional block diagram of the library server. FIG. 3 is a diagram illustrating an example of data stored in the user DB. FIG. 4 is a diagram illustrating an example of data stored in the lending DB. FIG. 5 is a diagram illustrating an example of data stored in the sales ranking DB. FIG. 6 is a diagram illustrating an example of data stored in the lending ranking DB. FIG. 7 is a diagram showing a main processing flow according to the present embodiment. FIG. 8 is a diagram illustrating a first display example. FIG. 9 is a diagram illustrating a processing flow of the lending base recommendation processing. FIG. 10 is a diagram illustrating a second display example. FIG. 11 is a diagram illustrating a processing flow of multi-year processing. FIG. 12 is a diagram illustrating an example of data stored in the data storage unit. FIG. 13 is a diagram illustrating a processing example of the data in FIG. FIG. 14 is a diagram illustrating a processing flow of the genre introduction process. FIG. 15 is a diagram illustrating a third display example. FIG. 16 is a diagram illustrating a processing flow of past-based introduction processing. FIG. 17 is a diagram illustrating a fourth display example. FIG. 18 is a diagram illustrating a fifth display example. FIG. 19 is a diagram illustrating a sixth display example. FIG. 20 is a functional block diagram of a computer.

  An overview of a system according to an embodiment of the present technology is shown in FIG. For example, one or a plurality of library terminals 5 and a library server 3 that executes main processing in the present embodiment are connected to a network 1 that is the Internet or a LAN (Local Area Network) in a library. When the network 1 is connected to a network outside the library such as the Internet or when the library LAN is released, the user terminal 7 may be connected.

  The library terminal 5 is installed in, for example, a library hall, and includes, for example, a touch panel display unit. May have a hardware keyboard. The library terminal 5 is assumed to be running a Web browser. It is assumed that the user terminal 7 is also executing a Web browser and a mail client application.

  FIG. 2 shows a functional block diagram of the library server 3. The library server 3 includes a request processing unit 31, a data extraction unit 32, a user database (DB) 33, a lending DB 34, a sales ranking DB 35, a lending ranking DB 36, a mail transmitting unit 37, and a data storage unit 38. Have

  For example, when the user logs in from the library terminal 5 or the user terminal 7, the request processing unit 31 generates web page data including the requested data and the like, and sends it to the requesting library terminal 5 and the user terminal 7. Send. The request processing unit 31 may include recommended book data in the web page data. In this case, the request processing unit 31 instructs the data extraction unit 32 to perform data extraction processing.

  As will be described below, the data extraction unit 32 generates or extracts recommended book data using the user DB 33, the lending DB 34, the sales ranking DB 35, and the lending ranking DB 36, and outputs it to the request processing unit 31. To do. Note that data being processed is stored in the data storage unit 38.

  In addition, the mail transmitting unit 37 transmits mail including various notices to the user's mail address registered in the user DB 33, and the mail may include recommended book data. When the recommended book data is included in the mail, the data extraction unit 32 is instructed to perform data extraction processing.

  For example, data as shown in FIG. 3 is stored in the user DB 33. In the example of FIG. 3, for each user, a user ID, a password, a user name, an address, a list of books borrowed in the past, a list of books on loan, a list of reserved books, and an email address And are to be registered. Although an example in which a real name (title) is registered is shown, a book identifier (ID) may be registered.

  Further, the rental DB 34 stores data as shown in FIG. 4, for example. In the example of FIG. 4, a library name, a real name, a publication year, a shelf number of a shelf on which the book is placed, a writer name, a genre, and whether or not to lend are registered. Here, an example in which data of a plurality of libraries is stored is shown, but data of only the own library may be stored. The real name may be a book ID.

  In the sales ranking DB 35, for example, data as shown in FIG. 5 is stored. In the example of FIG. 5, a trend year, a real name, an author name, a genre, and a publication year are registered. In the sales ranking DB 35, for example, a predetermined number of books with higher sales are listed every year. The real name may be a book ID. Not every year but every other period such as every two years or every six months. In addition, data such as the number of sales may be further stored.

  In the lending ranking DB 36, for example, data as shown in FIG. 6 is stored. In the example of FIG. 6, the trend year, the real name, the writer name, and the number of loans are registered. In the lending ranking DB 36, for example, data of a predetermined number of books with a higher number of lending is listed every year. The real name may be a book ID. Not every year but every other period such as every two years or every six months.

  Next, processing contents of the library server 3 will be described with reference to FIGS.

  Here, it is assumed that after the user logs in, the request processing unit 31 of the library server 3 is before transmitting web page data including recommended book data to the user terminal 7 or the library terminal 5. As described above, it may be before the mail transmitting unit 37 transmits the mail including the data of the recommended book to the user's mail address.

  The request processing unit 31 specifies, for example, the user ID of the logged-in user (in the case of mail, a mail transmission destination user) (FIG. 7: Step S1), and the data extraction unit extracts a data request for a recommended book including the user ID. 32.

  When the data extraction unit 32 receives the data request for the recommended book, the data extraction unit 32 searches the user DB 33 with the user ID to identify the book that is being reserved (step S3). When there is no book that is reserved, a general recommended book is specified, and the description is omitted here.

  Thereafter, the data extraction unit 32 searches the sales ranking DB 35 for the book currently reserved (step S5). Here, if the search is not hit (step S7: No route), the data extraction unit 32 and the request processing unit 31 execute a lending base recommendation process described later (step S17). Then, the process ends.

  On the other hand, if the search is hit (step S7: Yes route), the data extraction unit 32 determines whether the number of hit years is only one year (step S9). If the hit year is multiple years, the data extraction unit 32 and the request processing unit 31 execute multi-year processing described later (step S19). Then, the process ends.

  On the other hand, if the number of hit years is only one year, the data extraction unit 32 extracts other book data registered for the hit year from the sales ranking DB 35 (step S11). Then, the data extraction unit 32 searches the loan DB 34 with the extracted books, determines whether or not the loan is being loaned, and narrows down to books that are not being loaned (step S13). In the example of FIGS. 3 and 5, if the book being reserved is “Great Adventure of X”, only the year 2000 is extracted. In the example of FIG. The books are “Health **” and “Spaceship **”. When the rental DB 34 (FIG. 4) is searched for “health **” and “space ship **”, “space ship **” is being rented in both libraries X and Y, but “health **”. If so, it is determined that the library Y is not lent. Then, the data extraction unit 32 outputs the book data that is determined not to be lent to the request processing unit 31.

  When the request processing unit 31 receives the data of the book that is not lent, the request processing unit 31 generates web page data including the book data as recommended book data, and transmits the web page data to the user terminal 7 or the library terminal 5 (Step S31). S15).

  For example, data as shown in FIG. 8 is included in the web page. In this way, users who have already booked “X Adventure” and have not borrowed it can immediately borrow “Health **” that was popular in the same year as “X Adventure”. The recommendation can give a new awareness to the user.

  In recent years, an increasing number of people read back books that have been popular in the past. This is also said to be due to the phenomenon that elderly people in their 40s miss the old days and read old books again. In addition, even though they wanted to read books that were popular at the time, they could not read because they were busy with work, housework, or childcare, but there were some people who had time to read because of childcare. If these people are effectively aware and can borrow other books, the library lending rate can be improved.

  Next, the lending base recommendation process will be described with reference to FIGS. The data extraction unit 32 searches the lending ranking DB (FIG. 6) 36 for the book being reserved (FIG. 9: step S21). If there is no hit in the search (step S23: No route), the data extraction unit 32 outputs general recommended book data (for example, a monthly recommended book) to the request processing unit 31. Upon receiving the general recommended book data, the request processing unit 31 generates web page data including the general recommended book data, and transmits it to the user terminal 7 or the library terminal 5 (step S25). Then, the process ends.

  On the other hand, if there is a hit in the search (step S23: Yes route), the data extraction unit 32 determines whether the number of hit years is only one year (step S27). If the hit year is multiple years, the data extraction unit 32 and the request processing unit 31 execute multi-year processing described later (step S35). Then, the process ends.

  On the other hand, if the number of hit years is only one year, the data extraction unit 32 extracts other book data registered for the hit year from the lending ranking DB 36 (step S29). Then, the data extraction unit 32 searches the loan DB 34 with the extracted books, determines whether or not the loan is being loaned, and narrows down to books that are not being loaned (step S31). The basic processing content is the same as step S13 in FIG. Then, the data extraction unit 32 outputs the book data that is determined not to be lent to the request processing unit 31.

  When the request processing unit 31 receives the data of the book that is not lent, the request processing unit 31 generates web page data including the book data as recommended book data, and transmits the web page data to the user terminal 7 or the library terminal 5 (Step S31). S33). The output contents are the same as in FIG. However, the library name and the shelf number may be further extracted from the lending DB 34 as the data of the book that is not lent, and the data as shown in FIG. 10 may be output. In the example of FIG. 10, a library name and a shelf number are included as additional data. Then, the process ends.

  Next, the multi-year process will be described with reference to FIGS. The data extraction unit 32 extracts the year hit in step S5 and the real name and genre registered for the year from the sales ranking DB 35 and stores them in the data storage unit 38, for example (FIG. 11: step S41). For example, data as shown in FIG. 12 is stored in the data storage unit 38. This shows an example of data extracted when the book under reservation is “Spaceship **”, and the author name and publication year are also extracted here.

  Then, the data extraction unit 32 searches the loan DB 34 with the extracted book real name, and excludes the data of the book being loaned from the data storage unit 38 (step S43). In the example of FIG. 12, “banana ...” is excluded because it is rented, and data as shown in FIG. 13 is stored in the data storage unit 38.

  Thereafter, the data extraction unit 32 searches the user DB 33 with the user ID and determines whether a book has been borrowed in the past (step S45). For example, in the user DB (FIG. 3), if a real name is registered in the column of books borrowed in the past, a list of books borrowed in the past is read out.

  If a book has not been borrowed in the past, the data extraction unit 32 executes a genre introduction process described later (step S53). Then, the process ends.

  On the other hand, when borrowing a book in the past, the data extraction unit 32 determines whether the extracted years are consecutive and the same book appears in the consecutive years (step S47). For example, in the state of FIG. 12, “banana…” appears in both 1980 and 1981, and in such a case, it is determined that this condition is satisfied.

  If the extracted years are not consecutive, or the extracted years are continuous but the same book does not appear in the consecutive years, the process proceeds to step S51.

  On the other hand, if the extracted years are consecutive and the same book appears in the consecutive years, the data extraction unit 32 determines whether a year other than the consecutive years has been extracted (step S49). . For example, in the state as shown in FIG. 12, since 2000 is extracted in addition to 1980 and 1981, the condition is satisfied in such a state. When such a condition is satisfied, the data extraction unit 32 executes a past-based introduction process described later (step S51). Thereafter, the process ends.

  On the other hand, if a year other than consecutive years has not been extracted, the process proceeds to step S53.

  In steps S45 to S51, if there is a book borrowed in the past, basically a process of branching based on the book borrowed in the past is performed. On the other hand, if there is no book borrowed in the past, the genre introduction process is executed. Even if there are books borrowed in the past, as a result, if the extracted years are just one consecutive period, it is regarded as one year being extracted. Then, the genre introduction process is executed.

  Next, the genre introduction process will be described with reference to FIGS. The data extraction unit 32 classifies the books stored in the data storage unit 38 (that is, the books extracted in step S41 and narrowed down in step S43) by genre (step S61). Further, the data extraction unit 32 searches the lending DB 34 for the book being reserved, and specifies the genre of the book being reserved (step S63). After that, the data extraction unit 32 sorts the genres and books by giving the highest priority to the specified genre and giving priority to a genre with a large number of books for other genres (step S65). The data extraction unit 32 outputs this processing result to the request processing unit 31.

  The request processing unit 31 generates web page data including the data received from the data extracting unit 32 as recommended book data, and transmits the web page data to the library terminal 5 or the user terminal 7 (step S67). For example, if the book genre being booked is “Critical Review” and the book has not been borrowed in the past, and data as shown in FIG. 12 is stored in the data storage unit 38, as shown in FIG. Data is generated as recommended book data.

  In this way, books that are popular in the same year can be recommended with priority given to books of the same genre as the book being reserved.

  Next, past-based introduction processing will be described with reference to FIGS. The data extraction unit 32 extracts book data borrowed in the past stored in association with the user ID related to the process from the user DB 33 (FIG. 16: step S71). Then, the data extraction unit 32 searches the sales ranking DB 35 for books borrowed in the past to identify the trendy year, classifies the books borrowed in the past for each trendy year, and counts the number of books (step S73). For example, data such as 3 volumes in 1980, 5 volumes in 1981, 4 volumes in 1982, 1 volume in 2000, etc. can be obtained. If the same book is prevalent in multiple years, it is counted in each year of multiple years. Further, when it is not registered in the sales ranking DB 35, it is not counted.

  Then, the data extraction unit 32 determines whether there is a year in which the number of books is prominent in step S5 from the processing result in step S73 (step S75). For example, if there is a year with more than three books than other years, it is determined that this condition is satisfied. Three books are an example, and the standard can be determined such that the number of books is 50% or more higher than the average value.

  If there is a year that has been hit in step S5 and the number of books is prominent, the data extraction unit 32 stores data of the extracted book in the year that has been hit in step S5 and has a prominent number of books in the data storage unit 38. Is output to the request processing unit 31 as recommended book data. Upon receiving the recommended book data from the data extracting unit 32, the request processing unit 31 generates web page data including the recommended book data and transmits it to the library terminal 5 or the user terminal 7 (step S77). Then, the process ends. For example, assuming that 1980 is a prominent year with a large number of books, and data as shown in FIG. 12 is stored in the data storage unit 38, data as shown in FIG. 17 is included as data for the recommended book. Will be. This makes it possible to present other books popular in the year that the user is paying attention to.

  On the other hand, if the condition of step S75 is not satisfied, the data extraction unit 32 determines whether there are consecutive years that are hit in step S5 and have a large number of books (step S79). For example, if it is confirmed in step S49 that only consecutive years have been hit, the condition may be satisfied in this step. For example, if the number of books is a predetermined number (for example, 3) or more, the condition is determined to be satisfied. Alternatively, it may be determined to satisfy the condition when the number of books is larger than in other years. It should be noted that the year range hit in step S5 only needs to include a year range where the number of books is equal to or greater than a predetermined number, and may not completely match. For example, if the range of hit years is 1980 to 1982, and the range of consecutive years is 1980 and 1981 with a predetermined number of books or more, then 1980 and 1981 satisfy the condition.

  When such a condition is satisfied, the data extraction unit 32 reads out the data of the extracted book in the consecutive year with a large number of books, which is a year hit in step S5, from the data storage unit 38, and recommends the book. Is output to the request processing unit 31. Upon receiving the recommended book data from the data extracting unit 32, the request processing unit 31 generates web page data including the recommended book data and transmits it to the library terminal 5 or the user terminal 7 (step S81). Then, the process ends. For example, if there is a book corresponding to 1980 to 1983, data as shown in FIG. 18 is included as data of the recommended book. In this way, it becomes possible to present other books that have become popular during a period in which the user is slightly focused.

  On the other hand, if the condition of step S79 is not satisfied, the data extraction unit 32 generates data in which the years hit in step S5 are arranged in order of oldness, and outputs the data to the request processing unit 31 as recommended book data. To do. Upon receiving the recommended book data, the request processing unit 31 generates web page data including the recommended book data and transmits it to the library terminal 5 or the user terminal 7 (step S83). Then, the process ends. For example, if data as shown in FIG. 12 is stored in the data storage unit 38, data as shown in FIG. 19 is included as data of the recommended book. In the example of FIG. 19, books popular in 1980, 1981, and 2000 are listed. Since 1980 and 1981 are the same book, only the old one may be left.

  By executing such processing, it is possible to identify the year that should be prioritized based on the books borrowed in the past, and to recommend other books accordingly.

  Although the embodiment of the present technology has been described above, the present technology is not limited to this. For example, the configuration of the library server 3 shown in FIG. 2 is an example, and may not match the program module configuration. Further, the function of the library server 3 may be realized by a plurality of computers. Furthermore, the client-side device may be a device such as a mobile phone or a smartphone.

  As described above, the recommended book data may be notified by other means such as e-mail. Moreover, although the example of a book was shown in the example described above, it may be content such as video or music instead of a book. Note that not only physical books but also electronic books may be included.

  Furthermore, although the sales ranking DB 35 and the lending ranking DB 36 are searched for the book being reserved, the search may be performed by paying attention to any other book. Furthermore, although the process is switched depending on whether the search result is a single year or a plurality of years, the process similar to that for a single year may be performed even if it is a plurality of years. Furthermore, although the process was switched according to the fashion year of the book borrowed in the past, for example, it may be arranged in the order of the number of books and the upper predetermined number may be presented.

  Although the example using the sales ranking DB 35 and the lending ranking DB 36 is shown above, only one of them may be used. Moreover, although the example using sales ranking DB35 was shown in FIG. 16, rental ranking DB36 may be used.

  The library server 3, library terminal 5, and user terminal 7 described above are computer devices, and as shown in FIG. 20, a memory 2501, a CPU (Central Processing Unit) 2503, a hard disk drive (HDD: Hard). Disk Drive) 2505, a display control unit 2507 connected to the display device 2509, a drive device 2513 for the removable disk 2511, an input device 2515, and a communication control unit 2517 for connecting to a network are connected by a bus 2519. . An operating system (OS) and an application program for executing the processing in this embodiment are stored in the HDD 2505, and are read from the HDD 2505 to the memory 2501 when executed by the CPU 2503. The CPU 2503 controls the display control unit 2507, the communication control unit 2517, and the drive device 2513 according to the processing content of the application program, and performs a predetermined operation. Further, data in the middle of processing is mainly stored in the memory 2501, but may be stored in the HDD 2505. In an embodiment of the present technology, an application program for performing the above-described processing is stored in a computer-readable removable disk 2511 and distributed, and installed from the drive device 2513 to the HDD 2505. In some cases, the HDD 2505 may be installed via a network such as the Internet and the communication control unit 2517. Such a computer apparatus realizes various functions as described above by organically cooperating hardware such as the CPU 2503 and the memory 2501 described above and programs such as the OS and application programs. .

  The above-described embodiment can be summarized as follows.

  In the information processing method according to the present embodiment, (A) from the first data storage unit that stores the data of content popular in the period for each predetermined period, the same period as the specified content (B) processing for outputting recommendation information including at least a part of the extracted other content data.

  In this way, it becomes possible to recommend other content popular in the same period as the specified content to the user, for example, to give notice to a user who is interested in a certain period in the past. become.

  In addition, the output processing described above is performed based on (b1) the second data storage unit that manages the rental of content, and determines whether or not other extracted content is being lent. A process for identifying content that is not in the middle may be included. In this way, it is possible to give notice to other contents that are currently available for lending.

  In addition, the popular content described above may be content specified based on the number of loans or the number of sales.

  In addition, the designated content may be content reserved by the user to whom the recommendation information is output. It becomes possible to focus on books other than books that cannot be borrowed during reservation, and book lending will be promoted.

  In addition, when the output process described above is (b2) when there are a plurality of the same period, from the third data storage unit that stores the data of the past rented contents for each user, the user of the output destination of the recommended information (B3) Specify the period in which the extracted past rental content is stored from the first data storage unit, (b4) Extract based on the specified period A process of narrowing down the other content that has been performed may be included. In this way, when the designated content is popular in a plurality of periods, the period and contents can be narrowed down based on the user's past data.

  In addition, when the specified period is the same as the continuous period included in the same period, and the number of times the specified period is specified is greater than the other periods or the number of times equal to or greater than the threshold, May include a process of narrowing down other contents by a continuous period included in the same period. In this way, a continuous period can be handled as a range.

  It is possible to create a program for causing a computer to carry out the processing described above, such as a flexible disk, an optical disk such as a CD-ROM, a magneto-optical disk, and a semiconductor memory (for example, ROM). Or a computer-readable storage medium such as a hard disk or a storage device. Note that data being processed is temporarily stored in a storage device such as a RAM.

  The following supplementary notes are further disclosed with respect to the embodiments including the above examples.

(Appendix 1)
Extracting data of other content stored for the same period as the designated content from the first data storage unit that stores the data of content popular in each of a plurality of periods,
A program for causing a computer to execute a process of outputting recommendation information including at least a part of the extracted data of the other content.

(Appendix 2)
The output process is:
The method according to appendix 1, including a process of determining whether the extracted other content is being lent based on the second data storage unit that manages the renting of the content, thereby determining whether the extracted other content is being lent. program.

(Appendix 3)
The program according to appendix 1 or 2, wherein the popular content is content specified based on the number of loans or the number of sales.

(Appendix 4)
The program according to any one of supplementary notes 1 to 3, wherein the designated content is content that is reserved by a user to whom the recommendation information is output.

(Appendix 5)
The output process is:
If there are a plurality of the same period, from the third data storage unit that stores the data of the past rental content for each user, the data of the past rental content of the user of the output destination of the recommendation information,
From the first data storage unit, specify a period in which the extracted past rented content is stored,
The program according to any one of appendices 1 to 4, including a process of narrowing down the extracted other content based on the specified period.

(Appendix 6)
The process of narrowing down
If the specified period coincides with a continuous period included in the same period, and the specified period is more than the other period or equal to or more than a threshold, the same period The program according to appendix 5, including a process of narrowing down the other content by a continuous period included in the period.

(Appendix 7)
Extracting data of other content stored for the same period as the designated content from the first data storage unit that stores the data of content popular in each of a plurality of periods,
An information processing method executed by a computer, including a process of outputting recommendation information including at least a part of the extracted data of the other content.

(Appendix 8)
An extraction unit that extracts data of other content stored for the same period as the specified content from the first data storage unit that stores the data of the content popular in each period of the plurality of periods;
An output unit that outputs recommendation information including at least a part of the extracted data of the other content;
An information processing apparatus.

1 Network 3 Library Server 5 Library Terminal 7 User Terminal 31 Request Processing Unit 32 Data Extraction Unit 33 User DB
34 Rental DB
35 Sales Ranking DB
36 Lending Ranking DB
37 Mail transmission unit 38 Data storage unit

Claims (8)

Refer to the first data storage unit that stores data of popular content in association with each period of a plurality of periods, extract data of other content associated with the same period as the specified content,
Referring to the second data storage unit that stores the lending information of the content, among the extracted other content, identify the content that is not being lent,
Outputting recommendation information including data of the specified content that is not on loan ;
A program characterized by executing the process to the computer. The trendy content is content identified based on the number of loans or the number of sales ,
The program according to claim 1 . The designated content is content that is reserved by the user to whom the recommendation information is output .
The program according to claim 1 or 2 . Refer to the first data storage unit that stores data of popular content in association with each period of a plurality of periods, extract data of other content associated with the same period as the specified content,
If the same period is more, with reference to the second data storage unit for storing data of past lending content to each user to extract data of past lending content output destination of the user estimated Como Information ,
Referring to the first data storage unit to identify a period associated with the extracted past rented content;
Based on the identified said period, extracted to narrow down the other content, and outputs the recommendation information including data content after refinement,
A program that causes a computer to execute processing. Refer to the first data storage unit that stores data of popular content in association with each period of a plurality of periods, extract data of other content associated with the same period as the specified content,
Referring to the second data storage unit that stores the lending information of the content, among the extracted other content, identify the content that is not being lent,
Outputting recommendation information including data of the specified content that is not on loan ;
The information processing method characterized by treating the computer executes. Extraction that extracts data of other content associated with the same period as the specified content with reference to the first data storage unit that stores data of popular content associated with each of a plurality of periods And
A second data storage unit that stores content lending information, a specifying unit that identifies content that is not being lent out of the other content extracted by the extraction unit;
An output unit that outputs recommendation information including data of content that is not being lent specified by the specifying unit ;
An information processing apparatus. Refer to the first data storage unit that stores data of popular content in association with each period of a plurality of periods, extract data of other content associated with the same period as the specified content,
If the same period is plural, refer to the second data storage unit that stores the data of past rented content for each user, extract the data of the past lent content of the user to whom the recommendation information is output,
Referring to the first data storage unit to identify a period associated with the extracted past rented content;
Based on the specified period, the extracted other content is narrowed down, and the recommendation information including data of the narrowed content is output.
An information processing method, wherein a computer executes a process. A first data storage unit that stores data of popular content associated with each period of a plurality of periods is referred to, and data of other content associated with the same period as the designated content is extracted. One extractor;
When there are a plurality of the same period, the second data storage unit that stores past rental content data for each user is referred to, and the past rental content data of the user to whom the recommendation information is output is extracted. 2 extraction units;
A specifying unit that refers to the first data storage unit and specifies a period associated with the past rental content extracted by the second extraction unit;
Based on the period specified by the specifying unit, the other content extracted by the first extracting unit is narrowed down, and the output unit that outputs the recommendation information including the data of the content after narrowing down;
An information processing apparatus.

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