WO2006067652A2 - Procede et appareil de recommandation de contenu - Google Patents

Procede et appareil de recommandation de contenu Download PDF

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Publication number
WO2006067652A2
WO2006067652A2 PCT/IB2005/054090 IB2005054090W WO2006067652A2 WO 2006067652 A2 WO2006067652 A2 WO 2006067652A2 IB 2005054090 W IB2005054090 W IB 2005054090W WO 2006067652 A2 WO2006067652 A2 WO 2006067652A2
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WIPO (PCT)
Prior art keywords
amount
feature
content
watched
user
Prior art date
Application number
PCT/IB2005/054090
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English (en)
Other versions
WO2006067652A3 (fr
Inventor
Declan Patrick Kelly
Xiaowei Shi
Original Assignee
Koninklijke Philips Electronics N.V.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by Koninklijke Philips Electronics N.V. filed Critical Koninklijke Philips Electronics N.V.
Priority to CNA200580044531XA priority Critical patent/CN101088288A/zh
Priority to JP2007547719A priority patent/JP2008525875A/ja
Priority to EP05823485A priority patent/EP1878221A2/fr
Publication of WO2006067652A2 publication Critical patent/WO2006067652A2/fr
Publication of WO2006067652A3 publication Critical patent/WO2006067652A3/fr

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/16—Analogue secrecy systems; Analogue subscription systems
    • H04N7/162—Authorising the user terminal, e.g. by paying; Registering the use of a subscription channel, e.g. billing
    • H04N7/163—Authorising the user terminal, e.g. by paying; Registering the use of a subscription channel, e.g. billing by receiver means only
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
    • H04N21/44213—Monitoring of end-user related data
    • H04N21/44222—Analytics of user selections, e.g. selection of programs or purchase activity
    • H04N21/44224—Monitoring of user activity on external systems, e.g. Internet browsing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/4508—Management of client data or end-user data
    • H04N21/4532—Management of client data or end-user data involving end-user characteristics, e.g. viewer profile, preferences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/47—End-user applications

Definitions

  • the present invention relates to a method and apparatus for recommending contents, and more particularly, to a technique for recommending users with contents more accurately.
  • each content comprises at least one content-feature, and content-features of a same genre composite a feature-genre.
  • the feature-genre for TV programs can be actor, and the corresponding content- features are Tom Hanks, Brad Pitt etc.
  • the feature-genre may also be program type, and the corresponding content-features are comedy, science documentaries, horror movie etc.
  • the feature-genre may also be director, and the corresponding content-features are Zhang Yimou, Fen Xiaogang etc.
  • These feature-genres may be from information sources such as broadcast, TV, or Internet, and are most typically transmitted to users together with programs through an electric program guide (EPG).
  • EPG electric program guide
  • the user profile in a content recommendation apparatus stores information about the user's preference to content- features of a program (Like degree) and weight (Weight) etc. and updates the user profile continuously.
  • a recommendation apparatus generally modifies a user profile based on the user's viewing history (e.g. what programs the user has watched) or viewing behavior (e.g. how long the user has watched).
  • viewing history e.g. what programs the user has watched
  • viewing behavior e.g. how long the user has watched.
  • the time duration that the user has watched a particular program is obtained firstly, then a predetermined threshold is subtracted from the obtained time duration, then the ratio of the result to the scheduled televising time duration is calculated to obtain the user's interest degree for this particular program, which can be used to revise the user profile.
  • the content recommendation apparatus 100 comprises a content receiving means 101 for receiving content with a particular content- feature; a user profile 102 for storing the user's viewing history; and a filter means 103 for recommending programs to the user according to the viewing history.
  • a recommendation system is disclosed in the American patent application US 10/066453 filed on January 31, 2002, and published on July 31, 2003 under the publication number US2003/0,145,326, and the assignee of which is KONINKLUKE PHILIPS ELECTRONICS Ltd.
  • the system tracks all programs the user watched and generates a user profile indicating the frequency of a particular program being watched.
  • the system provides the user with multiple predetermined schemes based on viewing custom stored in the user profile, so that the user may select pay per view programs according to his interests.
  • One disadvantage of the conventional recommendation system is that it only takes into account the amount that the user actually viewed but not the availability of a certain type of content. Because the user can only watch the televised programs, if the content the user really likes is shown very infrequently then it will not appear strongly in their profile. Therefore the programs the user likes strongly may not appear in the recommendation, so that the recommendation is not very accurate. For example, if a user watches 3 teleplays and 1 documentary in a certain week, the conventional recommendation system will update the user profile based on this, wherein the Like-degree to teleplays will be enhanced 3 times, and the Like-degree to documentaries will be enhanced 1 time. But in fact, there may be 15 teleplays and only 1 documentary available in that week. Since the user watches all available documentaries but watches part of the available teleplays, he may like watching documentaries more, and more enhanced Like-degree to documentaries will reflect the user's preference better.
  • One object of the present invention is to solve the problems discussed above and to provide a content recommendation apparatus with increased accuracy.
  • a content recommendation method including steps of: receiving information in relation to a content, the information comprising at least one content-feature of the content; obtaining the available amount of the content including the content-feature, and obtaining the amount of the content including the content-feature watched by a user; and deciding whether to recommend a content to the user based on the relationship between the available amount and the watched amount.
  • the received content is program
  • the available amount is the amount of the contents including the content-feature available within a predetermined time period, the predetermined time period is longer than at least one program with the content-feature
  • the watched amount is the amount of the contents including the content-feature watched by a user within the predetermined time period.
  • a program recommendation table for a predetermined time period is obtained firstly. Then the programs with the certain content- feature in the program recommendation table are statistically calculated to obtain the available amount, and the programs recommended in the program recommendation table and watched by the user within the predetermined time period are statistically calculated to obtain the watched amount.
  • the proportion of the recommended programs watched by the user can be evaluated, thereby correct the contents to be recommended and improve the accuracy of recommendation.
  • a Like-degree indicating the user's preference to the content-feature is included in the user profile.
  • the Like-degree is adjusted based on the relation between the available amount and the watched amount. Whether or not to recommend the program to the user is decided according to the adjusted Like-degree.
  • the available amount indicates how many hours programs including the content-feature are televised within a predetermined time period
  • the watched amount indicates how many hours the user actually watched the programs including the content-feature within the predetermined time period
  • the available amount may indicate the number of televised programs including the content-feature within a predetermined time period
  • the watched amount may also indicate the times the user actually watched the programs including the content-feature within the predetermined time period
  • the available amount may indicate the ratio of available programs with the content-feature to all available programs within a predetermined time period
  • the watched amount may also indicate the ratio of the programs with the content- feature actually watched by the user to all the available programs with the content-feature within the predetermined time period
  • a content recommendation system comprises: a receiving means for receiving information in relation to a content, the information comprising at least one content-feature of the content; an available amount obtaining means for obtaining the available amount of the content including the content-feature; a watched amount obtaining means for obtaining the watched amount of the content including the content-feature by a user; and a decision making means for deciding whether to recommend the content to the user based on the relationship between the available amount and the watched amount.
  • Fig.l shows the structural diagram of a conventional content recommendation apparatus
  • Fig.2 shows the structural diagram of the content recommendation apparatus according to one embodiment of the present invention
  • Fig.3 shows the structural diagram of the content recommendation apparatus according to another embodiment of the present invention.
  • Fig.4 shows the flow chart executed by the content recommendation apparatus according to one embodiment of the present invention
  • Fig.5 shows the flow chart executed by the content recommendation apparatus according to another embodiment of the present invention.
  • Fig.6 is a schematic view showing the input of a fuzzy membership function when implementing the apparatus of the present invention using fuzzy logic
  • Fig.7 is a schematic view showing the output of a fuzzy membership function when implementing the apparatus of the present invention using fuzzy logic.
  • Fig.2 shows the structural diagram of the content recommendation apparatus according to one embodiment of the present invention.
  • the content recommendation apparatus 200 comprises a receiving means 210, an available amount obtaining means 220, a watched amount obtaining means 230, and a decision making means 240.
  • the content recommendation apparatus 200 may also comprise a user profile 250, and the available amount obtaining means 220 may also comprise a statistically calculating means 260.
  • the receiving means 210 is used for receiving information in relation to content, the information comprising at least one content-feature of the content.
  • the content is from any information source such as broadcast, TV, or the Internet.
  • the content can be TV program
  • the information in relation to the content can be EPG, wherein a feature-genre is "actor”, and one content-feature in the feature-genre is "Brad Pitt", and another content-feature is "Tom Hanks”. All these content-features are from the EPG.
  • the available amount obtaining means 220 is used for obtaining the available amount of programs including the content-feature.
  • the program provider may analyze the available amount of the contents including the content-feature within a predetermined time period and store the available amount in the EPG to transmit the available amount information together with the EPG to the recommendation apparatus.
  • the available amount obtaining means 220 may obtain the available amount of the contents with the content feature directly from the EPG. If the program provider did not provide the available amounts of each type of programs in the EPG, the available amount obtaining means 220 may analyze the programs with the content-feature in the received EPG through a statistically calculating means 260 to obtain the available amount of the programs with the content-feature.
  • the watched amount obtaining means 230 is used to obtain the amount of the programs with the content-feature watched by a user.
  • the watched amount obtaining means 230 may obtain the user's viewing history, analyze the user's viewing history to obtain the watched amount of the programs with the content-feature.
  • the decision making means 240 receives the available amount and the watched amount respectively from the available amount obtaining means 220 and the watched amount obtaining means 230, and decides whether to recommend the content or add the content into the recommendation list according to the relation between the available amount and the watched amount.
  • the available amount and the watched amount may be, for example, in proportional relationship, and when the proportion is higher than a predetermined value, the content should be recommended.
  • the relation between the available amount and the watched amount may also be calculated through fuzzy logic etc. as will be described latter. When a predetermined relationship is observed, the content should be recommended.
  • the recommendation apparatus of the present invention may further have a user profile 250 to store information in relation to the user's viewing programs, such as the current EPG and previous EPGs as well as the available amounts of various programs obtained by the program provider or the recommendation apparatus through analyzing the
  • the user profile 250 may also store the user's preference to various programs and the relevant weight, as well as information about the viewing history for various programs and actually watched amounts of various programs.
  • the available amount obtaining means 220 and the watched amount obtaining means 230 can obtain the available amount and the watched amount directly from the user profile 250.
  • Fig.3 shows the structural diagram of the content recommendation apparatus according to another embodiment of the present invention.
  • the content recommendation apparatus 300 comprises a receiving means 210, an available amount obtaining means 220, a watched amount obtaining means 230, a user profile 250, a statistically calculating means 260 which are identical to those in the embodiment shown in Fig.2.
  • the decision making means 310 has a user profile correcting means 320.
  • the user profile correcting means 320 corrects the user's preference to the content-feature according to the relationship between the available amount and the watched amount. For example, if the ratio of the watched amount to the available amount is higher than a predetermined value, the Like-degree to the content-feature in the user profile is increased.
  • the Like-degree to the content-feature in the user profile is decreased.
  • the user's preference to the content-feature may also be corrected based on a relationship between the available amount and the watched amount calculated through, for example, the fuzzy logic that will be described latter.
  • Fig.4 shows the flow chart executed by the content recommendation apparatus shown in Fig.2. Now an embodiment of the content recommendation method of the present invention will be described with reference to Fig.4.
  • the content is from any information source such as broadcast, TV, or the Internet.
  • the content can be TV program
  • the information in relation to the content can be EPG, wherein a feature-genre of the content is "actor”, and one content-feature in the feature-genre is "Brad Pitt", and another content-feature is "Tom Hanks”.
  • obtaining the available amount of the contents with the content- feature For a EPG, this can be done by statistically calculating the amount of the programs with a certain content-feature available within a predetermined time period.
  • the predetermined time period may be one month, one week, or other time periods.
  • the predetermined time period may be set by the manufacturer of the set-top box or by the manufacturer of the recommendation apparatus.
  • the time period may also be set or modified by the user.
  • the time period should not be shorter than the shortest program with the content-feature, that is, the time period should be longer than at least one program with the content-feature.
  • the available amount of the programs with a certain content-feature can be obtained in a umber of ways. For example, it can be calculated by the program provider and stored in the EPG, and transmitted with the EPG to the recommendation apparatus. If the available amount of the content is not provided in the EPG, the EPG can be analyzed upon received to obtain the available amount of the content with the certain content-feature.
  • the available amount of the content may also be obtained from a user profile.
  • the user profile may store the available amount of the content with the certain content- feature provided by the content provider. Thus the available amount of the content can be readout directly from the user profile.
  • the user profile stores the current EPG and previous EPGs. In this case, the available amount of the content can be obtained through reading the EPGs stored in the user profile and then analyzing the EPGs in a statistic manner.
  • an available amount table can be generated. For example, based on statistics of the televising hours, the following table can be generated:
  • the content-features can be based on TV- Anytime fields.
  • the system should filter duplicates when creating this table.
  • obtaining the watched amount of the contents with the content- feature This can be done through analyzing the time the user spent for actually watching various contents within the predetermined time period.
  • the predetermined time period for calculating the watched amount is identical to that for calculating the available amount.
  • the watched amount can be obtained in many ways.
  • the recommendation apparatus may obtain the user's viewing history through monitoring the set-top box, then analyze the obtained viewing history to obtain the actually watched amount of the contents with the content-feature.
  • the user profile records the user's viewing history, and the recommendation system may obtain the actually watched amount of the contents including the content-feature through analyzing the recorded viewing history.
  • step S440 checking whether the available amount and the watched amount meet a predetermined relationship. If meet, the content is recommended to the user at step S450, or the content is not recommended at step S460.
  • the available amount and the watched amount may be in proportional relationship, and when the proportion is higher than a predetermined value, the content is recommended to the user.
  • the recommendation system may be set so that content with the same content-feature is recommended to the user when the ratio of the watched amount to the available amount is higher than 50%.
  • the available amount of documentaries in the last week is 3 hours, and the user watched all 3 hours, that is, the watched amount is 3 hours, and the ratio of the watched amount to the available amount is 100%, excess the predetermined value. Therefore, the recommendation system will recommend the documentaries in the current EPG to the user, or will add the documentaries in the current EPG to a recommendation list for the user to select according to the list.
  • the relationship between the available amount and the watched amount can be determined through, for example, the fuzzy logics as will be described latter.
  • the available amount of content in step S420 may also be denoted as the number of contents available.
  • the number of contents played with a same content-feature can be calculated to create the following table 3 (Numbers of contents with various content-features available in the EPG).
  • step S430 the analysis to the user's actually watched amount may be conducted by calculating the number of contents actually watched by the user, and a corresponding table 4 may be obtained.
  • the available amount of content in step S420 may also be denoted as the actually watched percentage of all the available contents.
  • the available amount column in the table should be denoted as percentage as shown in table 5.
  • the watched amount should be denoted as the percentage of the time the user spent for watching contents including the content-feature to the total time the user spent for watching various contents, which is shown in table 6.
  • the available amount and the watched amount may be collected not in all the programs in the
  • a program recommendation list with respect to the predetermined time period may be obtained from the user profile. Then the contents including the content- feature in the program recommendation list are analyzed to get the available amount. The programs among those recommended in the program recommendation list watched by the user in the predetermined time period are analyzed according to the user profile to get the watched amount. Analyzing in this way can evaluate the ratio of the programs actually watched by the user to all the recommended programs, whereby correcting the contents to be recommended and improve the recommendation accuracy.
  • the user profile in the recommendation system should be varied continuously. Dynamically correcting the user profile in the recommendation system according to the user's interests to recommend the user with really interesting contents can improve the recommendation accuracy. Therefore, figure 5 shows a further embodiment of the present invention.
  • the recommendation apparatus recommends programs based on the like-degree in the user profile. The steps
  • S410 to S430 in figure 5 are identical to the steps S410 to S430 in figure 4 respectively.
  • step S540 adjusting the Like-degree of the content-feature in the user profile according to the relationship between the available amount and the watched amount. For example, when the ratio of the watched amount to the available amount is higher than a predetermined value, the like-degree to the content-feature is increased in the user profile.
  • the like-degree to the content-feature in the user profile is more accurate.
  • the recommendation system will increase the like-degree to the content-feature in the user profile by 10% when the ratio of the watched amount to the available amount is higher than 50%. Therefore, the available documentaries in the last week was 3 hours, and the user watched all the 3 hours, that is, the watched amount is 3 hours. The ratio of the watched amount to the available amount is 100%, higher than 50%. So the recommendation system increases the like-degree to documentaries in the user profile from 50% to 60%.
  • the relationship between the available amount and the watched amount may also be calculated in the fuzzy logics as will be described latter.
  • step S550 upon receiving information about the content, including at least one content-feature of the content, the recommendation apparatus will retrieve the user's like-degree to the content-feature from the user profile and determine whether the like-degree meets a predetermined value. If yes, the content will be recommended to the user in step S560. If not, the content will not be recommended to the user in step S570.
  • the recommendation apparatus receives the EPG and determines that one of the programs is a documentary, it will obtain the user's like- degree of 60% to documentaries from the user profile. Since the like-degree is higher than 50%, the documentary will be recommended.
  • recommending programs to the user or adjusting the Like-degree in the user profile based on the relationship between the available amount and the watched amount can be conducted in many approaches. Beside those described above, the following approaches can also be applied.
  • Steps for calculating the Like-degree are as following: First we calculate two values, WL and WH:
  • the available amount may be calculated not for the programs televised within a time period, but for the contents recommended to the user.
  • the actually watched proportion of the contents recommended to the user by the recommendation system can be analyzed. Adjusting the Like-degree based on this proportion makes the Like-degree to converge to the correct value over time, thereby recommending contents more accurately.
  • Recommending contents to a user may also be implemented using fuzzy logic approach.
  • fuzzy logic approach The advantage of using fuzzy logic approach is that the robust of the content recommendation apparatus is improved, and the content recommendation apparatus is more intelligent.
  • each entry in the vector represents one of the features (e.g. genre) and the associated Like-degree (how much we think the user likes this feature) and Weight (how important we think this feature is).
  • the Weight is the same for all features of the same type (e.g. the Weight is the same for all genres or all actors).
  • This user profile vector can be represented as a table 7.
  • Table7 Data structure for storing the user profile based
  • Weighti the Weight of feature type t.
  • Weighti' the updated Weight, ;
  • ⁇ Weighti the change of Weighti. Different kind of term has different Weight difference;
  • the invention will valuate the change of user profile by fuzzy inference theory to simulate human intelligence.
  • the fuzzy logic approach of the present invention is based on the knowledge base in this content recommendation apparatus, which is from the analysis of available amount available-time (%) and actually watched amount watch-time (%) datum stored in Memory.
  • ⁇ [l] The height of output area from lth rule
  • yi The gravity's horizontal coordinate of output area from lth rule
  • m The number of the rules.
  • ⁇ Weighti can be computed as following,
  • the search engine can obtain the relationship between the total amount of contents provided by the search engine and the amount of contents actually watched by the user, and adjust the searching result based on this relationship, thereby improve the hit ratio of the search and recommend more interested contents to the user.
  • the present invention can be further improved. For example, if the user's device is networked then each user can export a summary of his viewing (i.e. a summary of the amount of time they spend watching each type of content) to a server. Based on this the server can determine the profile of the typical user, deviations from this profile then indicate a stronger or weaker preference for this user. Users may be concerned with privacy issues but this can be done anonymously to avoid such concerns.

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  • Engineering & Computer Science (AREA)
  • Databases & Information Systems (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Social Psychology (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Computer Security & Cryptography (AREA)
  • Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

La présente invention se rapporte à un procédé et à un appareil de recommandation de contenu, qui permettent d'améliorer l'exactitude de la recommandation d'un contenu à des utilisateurs. Le procédé selon l'invention comprend les étapes consistant: à recevoir des informations liées à un contenu, lesdites informations contenant au moins un attribut de contenu du contenu; à obtenir la quantité disponible du contenu contenant ledit attribut de contenu, et à obtenir la quantité du contenu contenant ledit attribut de contenu regardée par un utilisateur; et à décider s'il est utile de recommander un contenu à l'utilisateur sur la base de la relation entre la quantité disponible et la quantité regardée.
PCT/IB2005/054090 2004-12-23 2005-12-07 Procede et appareil de recommandation de contenu WO2006067652A2 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
CNA200580044531XA CN101088288A (zh) 2004-12-23 2005-12-07 一种内容推荐方法和装置
JP2007547719A JP2008525875A (ja) 2004-12-23 2005-12-07 コンテンツを推薦するための方法及び装置
EP05823485A EP1878221A2 (fr) 2004-12-23 2005-12-07 Procede et appareil de recommandation de contenu

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CN200410104978 2004-12-23
CN200410104978.9 2004-12-23

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CN107958384A (zh) * 2016-10-14 2018-04-24 北京酷我科技有限公司 一种基于用户听歌行为的数据分析方法及系统

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CN104837034B (zh) * 2015-03-09 2019-04-12 腾讯科技（北京）有限公司 一种信息处理方法、客户端及服务器

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