WO2016082134A1 - 一种影视资源的推荐方法以及推荐影视资源的装置 - Google Patents
一种影视资源的推荐方法以及推荐影视资源的装置 Download PDFInfo
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- WO2016082134A1 WO2016082134A1 PCT/CN2014/092316 CN2014092316W WO2016082134A1 WO 2016082134 A1 WO2016082134 A1 WO 2016082134A1 CN 2014092316 W CN2014092316 W CN 2014092316W WO 2016082134 A1 WO2016082134 A1 WO 2016082134A1
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- 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]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
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- the invention belongs to the technical field of multimedia terminals, and particularly relates to a recommendation method system for film and television resources and a device for recommending video resources.
- the prior art has attached a "tag" to each video resource to classify the resources so that the user can search through the screening method.
- the user experience is still very large. Space that can be improved.
- the embodiment of the invention provides a method for recommending video resources, and the purpose is to enable the set-top box/smart TV to push appropriate video resources to the user through the user's past on-demand data.
- the present invention is implemented as follows: a method for recommending video resources, including the following steps:
- the method further includes determining whether the time when the user uses the video resource reaches a certain threshold.
- the method further includes: determining whether the number of repetitions of the information label reaches a certain threshold,
- An embodiment of the present invention further provides an apparatus for recommending a video resource, where the apparatus includes:
- a storage unit configured to store an information label corresponding to a video resource that the user has just clicked on
- a statistical unit whose input end is connected to the output end of the storage unit, is used for counting the information label, and outputs a ranking list of the number of times of information label repetition;
- a search unit whose input end is connected to the output end of the statistical unit, and is used for searching and recommending the video resource with the matching information label according to the information label in the repetition number ranking list.
- the device further includes:
- the user uses the time judging unit, and the output end thereof is connected to the storage unit.
- the device further includes:
- the repetition number judging unit has an input end connected to the storage unit and an output end connected to the statistical unit.
- the system by counting the corresponding information labels in the video resources that the user is accustomed to watch, the system will dynamically learn the user's viewing habits, and make recommendations for the video resources according to the user's viewing habits, so that the system can automatically recommend the user's favorite. Film and television resources.
- FIG. 1 is a schematic flowchart of a method for recommending a video resource according to an embodiment of the present invention
- FIG. 2 is a schematic flowchart of a method for recommending a second type of video resource according to an embodiment of the present invention
- FIG. 3 is a schematic flowchart of a method for recommending a third type of video resource according to an embodiment of the present invention
- FIG. 4 is a schematic structural diagram of an apparatus for recommending video resources according to an embodiment of the present invention.
- FIG. 5 is a schematic structural diagram of a second device for recommending video resources according to an embodiment of the present disclosure
- FIG. 6 is a schematic structural diagram of a third apparatus for recommending video resources according to an embodiment of the present invention.
- FIG. 1 is a schematic flowchart diagram of a method for recommending a video resource according to an embodiment of the present invention; for convenience of description, only parts related to the embodiment of the present invention are shown.
- step S101 the information tag corresponding to the video resource that the user has just clicked is stored according to the on-demand record of the user.
- the tags include, but are not limited to, the name of the actor, the type of film and television resources (eg, comedy, drama, horror), the income of the box office, and the type of ending (eg, a happy ending, a wake-up ending,
- the director's narrative technique such as: flattening, climax, etc.), the director's name, the country of the filming, the production company, etc.
- step S102 the information tag is counted, and the information tag repetition number ranking list is output.
- step S103 the video resource with the matching information tag is searched for and recommended according to the information tag in the repetition number list.
- the working principle of the present invention is: in step S101, storing the information label corresponding to the video resource that the user has just clicked, and in step S102, when the video resource of the user on demand exceeds a certain amount of data, such as 10 parts, the statistics are started.
- the repetition rate of the information tag For example, in the various video resources that a user clicked, the information label of "Actor Liu Tianwang” appeared six times, the information label of "Fashion Show” appeared five times, and the information label of "Fully ending” appeared four times.
- the number of repetitions of the above labels outputs a ranking list.
- the “searching and recommending the video resource with the matching information label” means that the highest priority search and recommendation is: the video resource of all the labels is satisfied at the same time; the second priority search and recommendation is: the last one in the ranking list is not satisfied. Item (the item with the lowest number of repetitions), while satisfying the resources of other items; the third priority search and recommendation is: the last two items in the ranking list (the two items with the lowest number of repetitions) are not satisfied, and the resources of other items are satisfied. And so on.
- the system by counting the corresponding information labels in the video resources that the user is accustomed to watch, the system will dynamically learn the user's viewing habits, and make recommendations for the video resources according to the user's viewing habits, so that the system can automatically recommend the user's favorite. Film and television resources.
- the step S201 is introduced to determine whether the time when the user uses the video resource reaches a threshold (eg, half an hour). If the threshold is lower than the threshold, the user may be inferred that the user is not interested in the video resource.
- the corresponding information tag has no reference value, and the system does not process it; otherwise, it enters the storage and statistics process of the information tag.
- step S301 After the system stores the information label corresponding to all the video resources that the user has just clicked, the process is introduced to step S301, and it is determined whether the number of repetitions of the information label reaches a certain level.
- the threshold for example, one of the information labels is repeated 5 times, and if so, the information labels are output as elements in the repeating number ranking list; if not, no processing is performed.
- FIG. 4 is a schematic structural diagram of an apparatus for recommending a video resource according to an embodiment of the present invention, where the apparatus for recommending a video resource includes:
- a statistical unit 402 having an input end connected to an output end of the storage unit
- the search unit 403 has an input connected to the output of the statistical unit.
- the storage unit 401 is configured to store the information label corresponding to the video resource that the user has just clicked, and output the statistical result to the statistics unit 402.
- the statistics unit 402 is configured to collect the information label and output the information label. Repeating the ranking list to the search unit 403; searching unit 403 for Searching for and recommending a video resource with a matching information tag according to the information tag in the repetition number ranking list.
- the system will dynamically learn the user's viewing habits and make recommendations for the video resources according to the user's viewing habits, so that the system can automatically recommend the user's favorite video resources.
- the user usage time judging unit 501 is connected to the storage unit 401 for determining whether the time when the user uses the video resource reaches a certain threshold, and if so, storing the user's on-demand broadcast. The information label corresponding to the video resource; if not, it will not be processed.
- the repetition number determination unit 601 is introduced, and the repetition number determination input terminal is connected to the storage unit 401, and the output terminal is connected to the statistical unit 401 for judging information. Whether the number of repetitions of the label reaches a certain threshold, and if so, the information label is placed in the repetition number ranking list; if not, no processing is performed.
- the system by counting the corresponding information labels in the video resources that the user is accustomed to watch, the system will dynamically learn the user's viewing habits, and make recommendations for the video resources according to the user's viewing habits, so that the system can automatically recommend the user's favorite. Film and television resources.
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Abstract
本发明属于多媒体终端技术领域，提供了一种影视资源的推荐方法，所述方法包括如下步骤：存储用户点播过的影视资源所对应的信息标签；统计所述信息标签，并输出信息标签重复次数排行列表；根据所述重复次数排行列表中的信息标签，搜索并推荐具有相符信息标签的影视资源。通过该方法，可以使用户能享受到其未发现的，但与其观赏风格相符的影视资源。
Description
本发明属于多媒体终端技术领域，尤其是涉及一种影视资源的推荐方法统以及推荐影视资源的装置。
随着互联网的发展，影视资源逐渐增多，而且增速越来越大。在这种情况下，用户想要在茫茫的资源海中，找到自己喜欢的影视资源越来越难。而且看电影/电视的行为虽然属于休闲娱乐行为，但本身也有较大的时间成本。如何快速定位自己喜欢的片子，已经是很多用户关心的问题。
现有技术已经对每部影视资源贴上了“信息标签”(tag),以将资源分类，方便用户通过筛选的方式查找，在现有技术上，对用户的体验而言，还有很大可以改善的空间。
发明内容
本发明实施例提供了一种影视资源的推荐方法，目的在于使机顶盒/智能电视可以通过用户的过往点播数据向用户推送合适的影视资源。
本发明是这样实现的：一种影视资源的推荐方法，包括以下步骤：
存储用户点播过的影视资源所对应的信息标签；
统计所述信息标签，并输出信息标签重复次数排行列表；
根据所述重复次数排行列表中的信息标签，搜索并推荐具有相符信息标签的影视资源。
进一步地，所述方法还包括判断用户使用影视资源的时间是否到达一定阈值，
若是，则存储所述用户点播过的影视资源所对应的信息标签；
若否，则不做处理。
进一步地，所述方法还包括，判断信息标签的重复次数是否达到一定阈值，
若是，将所述信息标签放入所述重复次数排行列表；
若否，则不作处理。
本发明实施例还提供了一种推荐影视资源的装置，所述装置包括：
存储单元，用于存储用户点播过的影视资源所对应的信息标签；
统计单元，其输入端与所述存储单元的输出端连接，用于统计所述信息标签，并输出信息标签重复次数排行列表；
搜索单元，其输入端与所述统计单元的输出端连接，用于根据所述重复次数排行列表中的信息标签，搜索并推荐具有相符信息标签的影视资源。
进一步地，所述装置还包括：
用户使用时间判断单元，其输出端与存储单元连接，
用于判断用户使用影视资源的时间是否到达一定阈值，
若是，则存储所述用户点播过的影视资源所对应的信息标签；
若否，则不做处理。
进一步地，所述装置还包括：
重复次数判断单元，其输入端与存储单元连接，输出端与统计单元连接，
用于判断信息标签的重复次数是否达到一定阈值，
若是，将所述信息标签放入所述重复次数排行列表；
若否，则不作处理。
在本发明中，通过统计用户习惯收看的影视资源中对应的信息标签，系统将动态学习用户的收视习惯，并根据用户的收视习惯做出影视资源的推荐，从而使系统能自动推荐用户喜欢的影视资源。
图1是本发明实施例提供的影视资源的推荐方法的流程示意图；
图2是本发明实施例提供的第二种影视资源的推荐方法的流程示意图；
图3是本发明实施例提供的第三种影视资源的推荐方法的流程示意图；
图4是本发明实施例提供的推荐影视资源的装置的结构示意图；
图5是本发明实施例提供的第二种推荐影视资源的装置的结构示意图；
图6是本发明实施例提供的第三种推荐影视资源的装置的结构示意图；
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅仅用以解释本发明，并不用于限定本发明。
图1示出了本发明实施例提供的影视资源的推荐方法的流程示意图；为了便于说明，只示出了与本发明实施例相关的部分。
在步骤S101中，根据用户的点播记录，存储用户点播过的影视资源所对应的信息标签。
所述标签包括但不限定于：演员的名字、影视资源的类型(如：喜剧片、剧情片、恐怖片)、票房的收入、结局的类型(如：圆满结局、发人深醒的结局、转折较大的结局、悲伤的结局)、导演的叙事手法(如：平铺直叙、高潮迭起等)导演的名字、拍摄的国家、制片公司等。
在步骤S102中，统计所述信息标签，并输出信息标签重复次数排行列表。
在步骤S103中，根据所述重复次数排列表中的信息标签，搜索并推荐具有相符信息标签的影视资源。
本发明的工作原理是：在S101步骤下，存储用户点播过的影视资源所对应的信息标签，在S102步骤下，当用户点播的影视资源超过一定的数据，如10部时，便开始统计这些信息标签的重复率。如某用户点击的各种影视资源中，“演员刘天王”的信息标签出现过六次，“时装戏”的信息标签出现过五次，“圆满结局”的信息标签出现过四次，这根据上述标签的重复次数输出排行列表。在S103步骤下，在影视资源库中搜索具有“演员刘天王”、“时装戏”、以及“圆满结局”的影视资源，并进行推荐。
所述“搜索并推荐具有相符信息标签的影视资源”指的是：最优先搜索和推荐的是：同时满足所有标签的影视资源；第二优先搜索和推荐的是：不满足排行列表中最后一项(重复次数最低的一项)，而满足其他项的资源；第三优先搜索和推荐的是：不满足排行列表中最后两项(重复次数最低的两项)，而满足其他项的资源，以此类推。
在本发明中，通过统计用户习惯收看的影视资源中对应的信息标签，系统将动态学习用户的收视习惯，并根据用户的收视习惯做出影视资源的推荐，从而使系统能自动推荐用户喜欢的影视资源。
由于用户可能打开一些影视资源，观看几分钟后发现自己不感兴趣而马上关闭，如果这些影视资源对应的标签也统计进去的话，会造成推荐结果的不精确。综上，如图2所示，引入S201步骤，判断用户使用该影视资源的时间是否达到一个阈值(如：半个小时)，如果低于该阈值，则可推断为用户对影视资源不感兴趣，其对应的信息标签没有参考价值，而系统不作处理；反之，则进入信息标签的存储、统计流程。
同时，在实践中还存在一个可能性，即，随着对影视资源的日益细分，可能一部电影会因为演员、剧情、导演、拍摄国家、结局、叙事风格、类型、拍摄年代、票房、点播热度等维度，而对应十多个，甚至数十个信息标签。而有的信息标签是用户不在意的，没有作为影视资源推荐依据的价值。
在这种情况下，为了推荐的精确性，如图3所示，在系统存储了用户点播过的所有影视资源所对应的信息标签后，引入步骤S301，判断信息标签的重复次数是否达到一定的阈值(如：某一个信息标签重复了5次)，若是，则将这些信息标签作为重复次数排行列表中的元素输出；若否，则不作处理。
此外，图4是本发明实施例提供的推荐影视资源的装置的结构示意图，该推荐影视资源的装置包括：
存储单元401；
统计单元402，其输入端与所述存储单元的输出端连接；
搜索单元403，其输入端与所述统计单元的输出端连接。
在实际工作中，存储单元401，用于存储用户点播过的影视资源所对应的信息标签，并将统计结果输出至统计单元402；统计单元402，用于统计所述信息标签，并输出信息标签重复次数排行列表至搜索单元403；搜索单元403，用于
根据所述重复次数排行列表中的信息标签，搜索并推荐具有相符信息标签的影视资源。
从而通过统计用户习惯收看的影视资源中对应的信息标签，系统将动态学习用户的收视习惯，并根据用户的收视习惯做出影视资源的推荐，从而使系统能自动推荐用户喜欢的影视资源。
由于用户可能打开一些影视资源，观看几分钟后发现自己不感兴趣而马上关闭，如果这些影视资源对应的标签也统计进去的话，可能会造成推荐结果的不精确。综上，如图5所示，加入用户使用时间判断单元501，其输出端与存储单元401连接，用于判断用户使用影视资源的时间是否到达一定阈值，若是，则存储所述用户点播过的影视资源所对应的信息标签；若否，则不做处理。
通过加入该单元，使得搜索和推荐的结果更加精确。
同时，在实践中还存在一个可能性，即，随着对影视资源的日益细分，可能一部电影会因为演员、剧情、导演、拍摄国家、结局、叙事风格、类型、拍摄年代、票房、点播热度等维度，而对应十多个，甚至数十个信息标签。而有的信息标签是用户不在意的，没有作为影视资源推荐依据的价值。
在这种情况下，为了推荐的精确性，如图6所示，引入重复次数判断单元601，该重复次数判断输入端与存储单元401连接，输出端与统计单元401连接，其用于判断信息标签的重复次数是否达到一定阈值，若是，将所述信息标签放入所述重复次数排行列表；若否，则不作处理。
在本发明中，通过统计用户习惯收看的影视资源中对应的信息标签，系统将动态学习用户的收视习惯，并根据用户的收视习惯做出影视资源的推荐，从而使系统能自动推荐用户喜欢的影视资源。
以上仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。
Claims (6)
- 一种影视资源的推荐方法，其特征在于，所述方法包括如下步骤：存储用户点播过的影视资源所对应的信息标签；统计所述信息标签，并输出信息标签重复次数排行列表；根据所述重复次数排行列表中的信息标签，搜索并推荐具有相符信息标签的影视资源。
- 如权利要求1所述的影视资源的推荐方法，其特征在于：所述方法还包括:判断用户使用影视资源的时间是否到达一定阈值，若是，则存储所述用户点播过的影视资源所对应的信息标签；若否，则不做处理。
- 如权利要求1或2所述的影视资源的推荐方法，其特征在于：所述方法还包括:判断信息标签的重复次数是否达到一定阈值，若是，将所述信息标签放入所述重复次数排行列表；若否，则不作处理。
- 一种推荐影视资源的装置，其特征在于，所述装置包括：存储单元，用于存储用户点播过的影视资源所对应的信息标签；统计单元，其输入端与所述存储单元的输出端连接，用于统计所述信息标签，并输出信息标签重复次数排行列表；搜索单元，其输入端与所述统计单元的输出端连接，用于根据所述重复次数排行列表中的信息标签，搜索并推荐具有相符信息标签的影视资源。
- 如权利要求4所述的推荐影视资源的装置，其特征在于，所述装置还包括：用户使用时间判断单元，其输出端与存储单元连接，用于判断用户使用影视资源的时间是否到达一定阈值，若是，则存储所述用户点播过的影视资源所对应的信息标签；若否，则不做处理。
- 如权利要求4或5所述的推荐影视资源的装置，其特征在于，所述装置还包括：重复次数判断单元，其输入端与存储单元连接，输出端与统计单元连接，用于判断信息标签的重复次数是否达到一定阈值，若是，将所述信息标签放入所述重复次数排行列表；若否，则不作处理。
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110427556A (zh) * | 2019-07-30 | 2019-11-08 | 牡丹江师范学院 | 一种基于文艺学学习的电影推荐方法 |
| CN111491187A (zh) * | 2020-04-15 | 2020-08-04 | 腾讯科技（深圳）有限公司 | 视频的推荐方法、装置、设备及存储介质 |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110208570A1 (en) * | 2010-02-24 | 2011-08-25 | Dehai Kong | Apparatus, system, and method for individualized and dynamic advertisement in cloud computing and web application |
| CN102811371A (zh) * | 2012-07-10 | 2012-12-05 | Tcl集团股份有限公司 | 智能电视应用程序推荐的方法、系统及装置 |
| CN103458275A (zh) * | 2013-09-02 | 2013-12-18 | 深圳市茁壮网络股份有限公司 | 实时交互的数字电视资讯推荐系统及方法 |
| CN104079976A (zh) * | 2014-05-15 | 2014-10-01 | 惠州市水木网络科技有限公司 | 机顶盒用户界面推荐方法和系统 |
| CN104104994A (zh) * | 2014-06-11 | 2014-10-15 | 广东九联科技股份有限公司 | 一种电视节目快速搜索方法及装置 |
-
2014
- 2014-11-26 WO PCT/CN2014/092316 patent/WO2016082134A1/zh active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110208570A1 (en) * | 2010-02-24 | 2011-08-25 | Dehai Kong | Apparatus, system, and method for individualized and dynamic advertisement in cloud computing and web application |
| CN102811371A (zh) * | 2012-07-10 | 2012-12-05 | Tcl集团股份有限公司 | 智能电视应用程序推荐的方法、系统及装置 |
| CN103458275A (zh) * | 2013-09-02 | 2013-12-18 | 深圳市茁壮网络股份有限公司 | 实时交互的数字电视资讯推荐系统及方法 |
| CN104079976A (zh) * | 2014-05-15 | 2014-10-01 | 惠州市水木网络科技有限公司 | 机顶盒用户界面推荐方法和系统 |
| CN104104994A (zh) * | 2014-06-11 | 2014-10-15 | 广东九联科技股份有限公司 | 一种电视节目快速搜索方法及装置 |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110427556A (zh) * | 2019-07-30 | 2019-11-08 | 牡丹江师范学院 | 一种基于文艺学学习的电影推荐方法 |
| CN110427556B (zh) * | 2019-07-30 | 2022-10-11 | 牡丹江师范学院 | 一种基于文艺学学习的电影推荐方法 |
| CN111491187A (zh) * | 2020-04-15 | 2020-08-04 | 腾讯科技（深圳）有限公司 | 视频的推荐方法、装置、设备及存储介质 |
| CN111491187B (zh) * | 2020-04-15 | 2023-10-31 | 腾讯科技（深圳）有限公司 | 视频的推荐方法、装置、设备及存储介质 |
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