WO2018227908A1 - 广告投放关键词推荐方法及装置、广告投放方法及装置 - Google Patents

广告投放关键词推荐方法及装置、广告投放方法及装置 Download PDF

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WO2018227908A1
WO2018227908A1 PCT/CN2017/116229 CN2017116229W WO2018227908A1 WO 2018227908 A1 WO2018227908 A1 WO 2018227908A1 CN 2017116229 W CN2017116229 W CN 2017116229W WO 2018227908 A1 WO2018227908 A1 WO 2018227908A1
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word
brand
words
frequency
attention
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French (fr)
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王经委
张杰伟
张霄
贺坚
程涛远
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Baidu Online Network Technology Beijing Co Ltd
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Baidu Online Network Technology Beijing Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation

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  • the present invention relates to the field of advertisement delivery technologies, and in particular, to an advertisement placement keyword recommendation method and device, an advertisement delivery method and device.
  • Search Engine Marketing is a very effective online marketing channel that promotes a variety of business information, such as brand advertising.
  • search engine marketing a customer purchases a keyword, and if the keyword is included in the content searched by the netizen, the brand advertisement of the customer is triggered.
  • an advertisement placement keyword recommendation method and apparatus for identifying an advertisement delivery effect by identifying a network owner's attention to a brand, and an advertisement delivery method and apparatus.
  • the present invention provides a method for recommending an advertisement placement keyword, including:
  • the combination of the brand word and any of the attention words corresponding to the point of interest is recommended as the keyword for the advertisement.
  • the present invention provides an advertisement delivery method, which comprises: using a combination of a brand word recommended by the advertisement keyword recommendation method and a focus word corresponding to a focus point as a keyword to perform advertisement placement.
  • the present invention provides an advertisement placement keyword recommendation apparatus, including a brand word acquisition unit, a focus point mining unit, and a keyword recommendation unit.
  • the brand word acquisition unit is configured to acquire a brand word of the brand
  • the point of interest mining unit is configured to acquire search information, and mine the attention words of the brand in the search information according to the brand words, and mark the attention points of the attention words;
  • the keyword recommendation unit is configured to recommend a combination of a brand word and any attention word corresponding to the point of interest as a keyword for advertisement placement.
  • the present invention provides an advertisement delivery device, including the above advertisement placement keyword recommendation device, and a delivery unit.
  • the delivery unit is configured to perform advertisement placement by using a combination of the recommended brand words and any attention words corresponding to the attention points as keywords.
  • the present invention also provides an apparatus comprising one or more processors and a memory, wherein the memory includes instructions executable by the one or more processors to cause the one or more processors to perform each of the The advertisement delivery keyword recommendation method or the advertisement delivery method provided by the embodiment.
  • the present invention further provides a computer readable storage medium storing a computer program, the computer program causing a computer to execute an advertisement placement keyword recommendation method or an advertisement delivery method according to various embodiments of the present invention.
  • the advertisement delivery keyword recommendation method and device provided by the embodiments of the present invention, the advertisement delivery method and the device, by using the brand word to mine the search information, pay attention to the attention word of the brand, and mark the attention point of the attention word, and obtain a plurality of A high-attention collection of attention words in focus units, and finally recommending a combination of a brand word and a focus word corresponding to a point of interest as a keyword for advertising, while ensuring accuracy of delivery, through attention point
  • the synonymous or synonymous attention word guarantees the coverage of the delivery, thus achieving the effect of effectively ensuring the advertisement delivery;
  • the advertisement delivery keyword recommendation method and device, the advertisement delivery method and device provided by some embodiments of the present invention further realize automatic and accurate identification of brand words in the company name, thereby eliminating the need for the user to manually set the brand words.
  • FIG. 1 is a flowchart of a method for recommending an advertisement placement keyword according to an embodiment of the present invention.
  • step S30 is a flow chart of step S30 in a preferred embodiment of the method of FIG. 1.
  • FIG. 3 is a flow chart of a preferred embodiment of the method of FIG. 1.
  • step S11 is a flow chart of step S11 in a preferred embodiment of the method of FIG.
  • Figure 5 is a flow diagram of a preferred embodiment of the method of Figure 4.
  • FIG. 6 is a flow chart of a preferred embodiment of the method of FIG.
  • FIG. 7 is a flowchart of an advertisement delivery method according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of an advertisement placement keyword recommendation apparatus according to an embodiment of the present invention.
  • Figure 9 is a schematic view showing the structure of a preferred embodiment of the apparatus shown in Figure 8.
  • Figure 10 is a schematic view showing the structure of a preferred embodiment of the apparatus shown in Figure 8.
  • Figure 11 is a schematic view showing the structure of a preferred embodiment of the apparatus shown in Figure 10.
  • Figure 12 is a schematic view showing the structure of a preferred embodiment of the apparatus shown in Figure 11.
  • FIG. 13 is a schematic structural diagram of an advertisement placing apparatus according to an embodiment of the present invention.
  • FIG. 14 is a schematic structural diagram of a device according to an embodiment of the present invention.
  • FIG. 1 is a flowchart of a method for recommending an advertisement placement keyword according to an embodiment of the present invention.
  • the advertisement placement keyword recommendation method provided by the present invention includes:
  • S30 acquiring search information, exchanging the attention words of the brand in the search information according to the brand words, and marking the attention points of the attention words;
  • S50 Recommend the combination of the brand word and any attention word corresponding to the point of interest as the keyword of the advertisement placement.
  • the brand word is obtained in the following two ways in the embodiment: identifying the brand word in the company name of the brand, and acquiring the brand word of the brand set by the user.
  • the brand word refers to the word that constitutes the font size in the company name, and the brand word may be the same as the brand name, or different but has a strong correlation with the brand.
  • the recognized brand word includes the brand name
  • the user can further supplement the setting of the nickname and abbreviation of the current brand, and the names, nicknames, abbreviations, etc. of other brands owned by the enterprise cannot pass. Identify the brand word obtained by the company name;
  • the recognized brand words still have a strong correlation with the brand.
  • the netizens who can recognize the brand words usually know the relationship between the brand words and the brand, and the user can further supplement the design.
  • the name, nickname, abbreviation of the current brand, and the brand name, nickname, abbreviation, etc. of other brands owned by the company cannot be obtained by identifying the company name.
  • step S30 includes:
  • S31 Acquire a plurality of search information, and filter search information that does not include a brand word
  • step S31 the search information of a large number of netizens is acquired through the background of the search engine, and the unrelated search information is filtered by using the brand word obtained in step S10, thereby filtering out the search information that concerns the brand;
  • each search information selected in step S31 is subjected to word-cutting, and words such as brand words and stop words are removed, and a plurality of sequences of attention words are obtained.
  • the stop words specifically refer to words such as modal particles that do not reflect the attention of netizens to the brand. Counting the word frequency of each word in a sequence of attention words and performing reverse ordering, thereby selecting a number of words with the highest frequency of words;
  • step S35 the points of interest of the attention words selected in step S33 are marked.
  • the attention points of the attention words such as “price reduction”, “promotion”, and “discount” are “price", “feel”, “comfort”
  • the focus of attention such as “durable” is "performance", and so on. Specifically, it can be marked by means of machine learning, or displayed for manual labeling.
  • step S30 Through the mining and labeling of step S30, several points of interest can be obtained, and each point of interest corresponds to a plurality of words with the same meaning, near meaning or high degree of association.
  • step S30 different mining algorithms commonly used in the field can also be used to mine the word of interest related to the brand word, and the same technical effect can be achieved.
  • step S50 the combination of the recommended brand word and any of the attention words corresponding to the point of interest, for example, the combination of the recommended brand word “Sheng Da” and the attention point “price” corresponds to any of the attention words:
  • the group's recommended keywords are used for advertising, as long as the search information includes the brand word “Shengda” and the “price reduction” corresponding to “price reduction/promotion/discount”, the advertisement will be triggered.
  • the above embodiment uses the brand words to mine the attention words of the brand in the search information, and points the attention points of the attention words, and obtains a plurality of attention words set with the attention point as the unit with high attention to the brand, and finally the brand words.
  • the combination of any word of interest corresponding to the point of interest is recommended as the keyword for advertising, and the coverage of the delivery is guaranteed by the synonymous or synonymous attention words corresponding to the point of interest while ensuring the accuracy of the delivery.
  • the effect of ad serving is used to mine the attention words of the brand in the search information, and points the attention points of the attention words, and obtains a plurality of attention words set with the attention point as the unit with high attention to the brand, and finally the brand words.
  • step S10 includes at least one of steps S11 and S12, that is, in some preferred embodiments, step S10 may be configured to adopt either one of the above two modes or two according to actual needs. Ways to get brand words.
  • step S11 specifically includes:
  • the closeness Aff(word 1 , word 2 ) of two adjacent words word 1 and word 2 is calculated as:
  • Freq(word 1 ) is the word frequency of word 1
  • Freq(word 2 ) is the word frequency of word 2
  • Freq adj (word 1 , word 2 ) is the adjacent phrase frequency of word 1 and word 2
  • ⁇ is the smoothing parameter.
  • the above word frequency and adjacent phrase frequency are obtained by statistics, and the smoothing parameters are set and adjusted according to experience.
  • the tightness can also be configured as other different calculation methods, and the same technical effect can be achieved as long as the closeness of the two words is positively correlated with the adjacent phrase frequency.
  • step S115 the word segmentation sequence "Beijing-Shengda-Fire-Equipment-Limited-Company” is obtained by cutting the word (and the "limited-company” is merged into “limited company” by amendment);
  • step S116 the local noun "Beijing" and the institutional word "limited company” in the word segmentation sequence are marked;
  • step S117 traversing the word segmentation sequence from the heading to obtain the word segmentation sequence "Shengda-Fireproof-Device" between the first place name "Beijing” and the first organization word "Company”;
  • step S118 the word segment sub-sequence is traversed from the back to the front, the word frequency of the "device” is greater than the first threshold, and the condition is not met; the word frequency of the "fire prevention” is less than the first threshold, but the "fire” and “device” are obtained by calculation.
  • the tightness is greater than the second threshold, and the condition is not met; the word frequency of “Shengda” is less than the first threshold, and the closeness of “Shengda” and “Fireproof” is less than the second threshold, so “Shengda” is judged as a separator;
  • step S119 the word “Shengda” in the word segmentation subsequence “Shengda-Fireproof Equipment” and its previous words are marked as brand words, and the "fire prevention” and “equipment” after “Shengda” are marked as industry. word.
  • steps further include modifying the special type of company name, for example:
  • the method further includes modifying a special type of company name, for example, removing "(China)" in the company name, etc.;
  • step S117 if the noun is not obtained, the word segment sequence before the first institution word is used as the word segment subsequence; and other correction means configured for different special types of company names.
  • the above embodiment further realizes automatic and accurate identification of brand words in the company name, thereby eliminating the need for the user to manually set the brand words.
  • FIG. 5 is a flow diagram of a preferred embodiment of the method of Figure 4. As shown in FIG. 5, in a preferred embodiment, step S11 further includes:
  • S111 Obtain a number of company names and perform a word segmentation to obtain a sequence of certain word segments
  • S112 Counting the word frequency of each word in a sequence of partial words, and the frequency of adjacent phrases of each pair of adjacent words;
  • step S111 all the company names in the registration information table are obtained, and more company names (and deduplication) can be further obtained through other channels commonly used in the field, and each acquired company name is cut separately. Words to obtain a sequence of word segments;
  • step S112 the word frequency of each word and the adjacent phrase frequency of each pair of adjacent words are obtained by statistics
  • step S113 the closeness of each pair of adjacent words is calculated according to the above formula (1).
  • step S118 simply calls the tightness calculated in advance in step S113 without further calculation at the time of judgment.
  • the tightness can also be calculated according to different calculations configured.
  • step S11 further includes:
  • S114 Mark high frequency words in each end word of several word segmentation sequences as institutional words.
  • the screening method of the high frequency words in step S114 is: the word frequency of the high frequency words is not less than the third threshold, and the ratio of the sum of the word frequencies of the high frequency words to the sum of the word frequencies of the last words Not less than the fourth threshold.
  • the same technical effects can be achieved by using machine training, manual marking, obtaining an existing institutional vocabulary for marking, and the like by different means.
  • FIG. 7 is a flowchart of an advertisement delivery method according to an embodiment of the present invention.
  • an advertisement delivery method provided by this embodiment includes the advertisement delivery keyword recommendation method provided by any of the foregoing embodiments, and:
  • step S70 Perform the advertisement placement by using the combination of the brand word recommended in step S50 and any attention word corresponding to the attention point as a keyword.
  • FIG. 8 is a schematic structural diagram of an advertisement placement keyword recommendation apparatus according to an embodiment of the present invention.
  • the apparatus shown in Fig. 8 can correspondingly perform the method shown in Fig. 1.
  • the present invention provides an advertisement placement keyword recommendation device 10, which includes a brand word acquisition unit 11, a focus point mining unit 13, and a keyword recommendation unit 15.
  • the brand word obtaining unit 11 is configured to acquire a brand word of the brand
  • the point of interest mining unit 13 is configured to acquire search information, and mine the attention words of the brand in the search information according to the brand words, and mark the attention points of the attention words;
  • the keyword recommendation unit 15 is configured to recommend a combination of a brand word and any attention word corresponding to the point of interest as a keyword for advertisement placement.
  • Figure 9 is a schematic view showing the structure of a preferred embodiment of the apparatus shown in Figure 8.
  • the apparatus shown in FIG. 9 can correspondingly perform the method shown in FIG. 2.
  • the point of interest mining unit 13 includes a filter subunit 131, a focus word mining subunit 133, and a point of interest labeling subunit 135.
  • the filtering sub-unit 131 is configured to acquire a plurality of search information, and filter the search information that does not include the brand word;
  • the attention word mining sub-unit 133 is configured to cut the filtered search information, remove the brand words and then sort the words to select a number of high-frequency attention words;
  • the attention point labeling sub-unit 135 is configured to mark the points of interest of the respective attention words.
  • Figure 10 is a schematic view showing the structure of a preferred embodiment of the apparatus shown in Figure 8.
  • the apparatus shown in FIG. 10 can correspondingly perform the method shown in FIG.
  • the brand word acquisition unit 11 includes at least one of the following:
  • a brand word recognition unit 111 configured to identify a brand word in a company name of the brand
  • the brand word setting unit 113 is configured to acquire a brand word of the brand set by the user.
  • Figure 11 is a schematic view showing the structure of a preferred embodiment of the apparatus shown in Figure 10.
  • the apparatus shown in Fig. 11 can correspondingly perform the method shown in Fig. 4.
  • the brand word identifying unit 111 includes a pre-processing sub-unit 1113, a dividing sub-unit 1115, and a marking sub-unit 1117.
  • the pre-processing sub-unit 1113 is configured to cut a word for the company name, obtain a word segmentation sequence, mark the local nouns and the organization words in the word segmentation sequence, and traverse the word segment sequence from the arrival to obtain the first place noun and the first a segmentation sub-sequence between the institutional words (corresponding to steps S115-S117 of the method shown in Figure 4);
  • the dividing sub-unit 1115 is configured to traverse the word segment sub-sequence from the back to the front to obtain a delimiter word whose word frequency is smaller than the first threshold and whose closeness to the next adjacent word is less than the second threshold; wherein the closeness is based on two adjacent words Word frequency and adjacent phrase frequency determination (corresponding to step S118 of the method shown in Figure 4);
  • the tag subunit 1117 is configured to mark the word and the separator word before the separator word as the brand word in the word segment subsequence, and the word after the separator word as the industry word (corresponding to step S119 of the method shown in FIG. 4).
  • Figure 12 is a schematic view showing the structure of a preferred embodiment of the apparatus shown in Figure 11.
  • the apparatus shown in Figure 12 can perform the method shown in Figures 5-6.
  • the brand word recognition unit 111 further includes a data support subunit 1111.
  • the data support sub-unit 1111 is configured to acquire a number of company names and perform word-cutting, obtain a sequence of partial words, count the word frequency of each word in the sequence of partial words, and the adjacent phrase frequency of each pair of adjacent words, and calculate each The closeness to adjacent words.
  • the brand word recognition unit 111 is further configured to mark high frequency words in each of the plurality of word segmentation sequences as institutional words.
  • FIG. 13 is a schematic structural diagram of an advertisement placing apparatus according to an embodiment of the present invention.
  • the apparatus shown in Fig. 13 can correspondingly perform the method shown in Fig. 7.
  • the present invention further provides an advertisement placement device 20, which includes the advertisement placement keyword recommendation device 10 and the delivery unit 21 provided by any of the above embodiments.
  • the delivery unit 21 is configured to perform advertisement placement by using a combination of the brand word recommended by the keyword recommendation unit 15 and any of the attention words corresponding to the attention point as a keyword.
  • FIG. 14 is a schematic structural diagram of a device according to an embodiment of the present invention.
  • the present application also provides an apparatus 1400 including one or more central processing units (CPUs) 1401, which may be according to programs stored in a read only memory (ROM) 1402 or Various appropriate actions and processes are performed from the program loaded into the random access memory (RAM) 1403 by the storage portion 1408.
  • CPUs central processing units
  • RAM random access memory
  • various programs and data required for the operation of the device 1400 are also stored.
  • the CPU 1401, the ROM 1402, and the RAM 1403 are connected to each other through a bus 1404.
  • An input/output (I/O) interface 1405 is also coupled to bus 1404.
  • the following components are connected to the I/O interface 1405: an input portion 1406 including a keyboard, a mouse, etc.; an output portion 1407 including a cathode ray tube (CRT), a liquid crystal display (LCD), and the like, and a speaker; a storage portion 1408 including a hard disk or the like And a communication portion 1409 including a network interface card such as a LAN card, a modem, or the like.
  • the communication section 1409 performs communication processing via a network such as the Internet.
  • Driver 1410 is also coupled to I/O interface 1405 as needed.
  • a removable medium 1411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory or the like is mounted on the drive 1410 as needed so that a computer program read therefrom is installed into the storage portion 1408 as needed.
  • the advertisement placement keyword recommendation method or the advertisement delivery method described in any of the above embodiments may be implemented as a computer software program.
  • an embodiment of the present disclosure includes a computer program product comprising a computer program tangibly embodied on a machine readable medium, the computer program comprising program code for performing an advertisement placement keyword recommendation method or an advertisement delivery method .
  • the computer program can be downloaded and installed from the network via the communication portion 1409, and/or installed from the removable medium 1411.
  • the present application further provides a computer readable storage medium, which may be a computer readable storage medium included in the apparatus of the above embodiment; or may exist separately, not assembled A computer readable storage medium in a device.
  • the computer readable storage medium stores one or more programs that are used by one or more processors to perform an ad placement keyword recommendation method or an ad delivery method as described in this application.
  • each block of the flowchart or block diagram can represent a module, a program segment, or a portion of code that includes one or more of the logic functions for implementing the specified.
  • Executable instructions can also occur in a different order than that illustrated in the drawings. For example, two successively represented blocks may in fact be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved.
  • each block of the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented by a dedicated hardware-based system that performs the specified functions or operations. Or can be implemented by a combination of dedicated hardware and computer instructions.
  • the units or modules described in the embodiments of the present application may be implemented by software or by hardware.
  • the described units or modules may also be provided in the processor.
  • each of the units may be a software program disposed in a computer or a mobile smart device, or may be a separately configured hardware device.
  • the names of these units or modules do not in any way constitute a limitation on the unit or module itself.

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Abstract

一种广告投放关键词推荐方法及装置、广告投放方法及装置，该广告投放关键词推荐方法包括：获取品牌的品牌词（S10）；获取搜索信息，根据品牌词挖掘搜索信息中关注该品牌的关注词，并标注关注词的关注点（S30）；将品牌词和关注点对应的任一关注词的组合推荐为广告投放的关键词（S50）。所述方法及装置通过利用品牌词挖掘搜索信息中关注该品牌的关注词，并标注关注词的关注点，获取若干对该品牌具有高关注度的以关注点为单位的关注词集合，最终将品牌词和关注点对应的任一关注词的组合推荐为广告投放的关键词，在保障投放精准度的同时，通过关注点对应的同义或近义的关注词保障投放的覆盖面，从而实现了有效保障广告投放的效果。

Description

广告投放关键词推荐方法及装置、广告投放方法及装置
相关申请的交叉引用
本申请要求百度在线网络技术(北京)有限公司于2017年6月12日提交的、发明名称为“广告投放关键词推荐方法及装置、广告投放方法及装置”的、中国专利申请号“201710439477.3”的优先权。
技术领域
本申请涉及广告投放技术领域，具体涉及一种广告投放关键词推荐方法及装置、广告投放方法及装置。
背景技术
搜索引擎营销(简称SEM)是一种非常有效的网络营销途径，可以推广各种商业信息，比如品牌广告。在搜索引擎营销中，客户购买关键词，如果网民搜索的内容中包含该关键词，就会触发该客户投放的品牌广告。
目前现有方案需要客户自定义关键词，或者采用系统推荐的关键词。前者的缺陷在于，自定义关键词仅凭经验或直觉自定义，由于不清楚网民对品牌的关注点，自定义关键词往往缺乏针对性；而后者的缺陷在于，系统通常只推荐客户已购买关键词的相关词，与品牌的关联性较差。因此，现有方案的缺陷会导致关键词的质量难以保障广告投放的效果，可能导致广告投放效果较差。
发明内容
鉴于现有技术中的上述缺陷或不足，期望提供一种通过识别网民对品牌的关注点保障广告投放效果的广告投放关键词推荐方法及装置，以及广告投放方法及装置。
第一方面，本发明提供一种广告投放关键词推荐方法，包括：
获取品牌的品牌词；
获取搜索信息，根据品牌词挖掘搜索信息中关注该品牌的关注词，并标注关注词的关注点；
将品牌词和关注点对应的任一关注词的组合推荐为广告投放的关键词。
第二方面，本发明推荐一种广告投放方法，包括：采用上述广告投放关键词推荐方法所推荐的品牌词和关注点对应的任一关注词的组合作为关键词，进行广告投放。
第三方面，本发明提供一种广告投放关键词推荐装置，包括品牌词获取单元、关注点挖掘单元和关键词推荐单元。
其中，品牌词获取单元配置用于获取品牌的品牌词；
关注点挖掘单元配置用于获取搜索信息，根据品牌词挖掘搜索信息中关注该品牌的关注词，并标注关注词的关注点；
关键词推荐单元配置用于将品牌词和关注点对应的任一关注词的组合推荐为广告投放的关键词。
第四方面，本发明推荐一种广告投放装置，包括上述广告投放关键词推荐装置，以及投放单元。
其中，投放单元配置用于采用所推荐的品牌词和关注点对应的任一关注词的组合作为关键词，进行广告投放。
第五方面，本发明还提供一种设备，包括一个或多个处理器和存储器，其中存储器包含可由该一个或多个处理器执行的指令以使得该一个或多个处理器执行根据本发明各实施例提供的广告投放关键词推荐方法或广告投放方法。
第六方面，本发明还提供一种存储有计算机程序的计算机可读存储介质，该计算机程序使计算机执行根据本发明各实施例提供的广告投放关键词推荐方法或广告投放方法。
本发明诸多实施例提供的广告投放关键词推荐方法及装置、广告投放方法及装置通过利用品牌词挖掘搜索信息中关注该品牌的关注词，并标注关注词的关注点，获取若干对该品牌具有高关注度的以关注点为单位的关注词集合，最终将品牌词和关注点对应的任一关注词的组合推荐为广告投放的关键词，在保障投放精准度的同时，通过关注点对应的同义或近义的关注词保障投放的覆盖面，从而实现了有效保障广告投放的效果；
本发明一些实施例提供的广告投放关键词推荐方法及装置、广告投放方法及装置进一步实现了自动精准识别公司名称中的品牌词，从而无需用户手动设置品牌词。
附图说明
通过阅读参照以下附图所作的对非限制性实施例所作的详细描述，本申请的其它特征、目的和优点将会变得更明显：
图1为本发明一实施例提供的一种广告投放关键词推荐方法的流程图。
图2为图1所示方法的一种优选实施方式中步骤S30的流程图。
图3为图1所示方法的一种优选实施方式的流程图。
图4为图3所示方法的一种优选实施方式中步骤S11的流程图。
图5为图4所示方法的一种优选实施方式的流程图。
图6为图5所示方法的一种优选实施方式的流程图。
图7为本发明一实施例提供的一种广告投放方法的流程图。
图8为本发明一实施例提供的一种广告投放关键词推荐装置的结构示意图。
图9为图8所示装置的一种优选实施方式的结构示意图。
图10为图8所示装置的一种优选实施方式的结构示意图。
图11为图10所示装置的一种优选实施方式的结构示意图。
图12为图11所示装置的一种优选实施方式的结构示意图。
图13为本发明一实施例提供的一种广告投放装置的结构示意图。
图14为本发明一实施例提供的一种设备的结构示意图。
具体实施方式
下面结合附图和实施例对本申请作进一步的详细说明。可以理解的是，此处所描述的具体实施例仅仅用于解释相关发明，而非对该发明的限定。另外还需要说明的是，为了便于描述，附图中仅示出了与发明相关的部分。
需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互组合。下面将参考附图并结合实施例来详细说明本申请。
图1为本发明一实施例提供的一种广告投放关键词推荐方法的流程图。
如图1所示，在本实施例中，本发明提供的广告投放关键词推荐方法包括：
S10：获取品牌的品牌词；
S30：获取搜索信息，根据品牌词挖掘搜索信息中关注该品牌的关注词，并标注关注词的关注点；
S50：将品牌词和关注点对应的任一关注词的组合推荐为广告投放的关键词。
在步骤S10中，本实施例中具体通过以下两种方式获取品牌词：识别该品牌的公司名称中的品牌词，以及，获取用户设定的该品牌的品牌词。其中，品牌词具体指公司名称中组成字号的词，品牌词可能与品牌名称相同，或不相同但与品牌具有较强的关联性。
具体地，当公司名称包括品牌名称时，识别出的品牌词包括品牌名称，用户可以进一步自行补充设定当前品牌的别称、简称，以及企业所拥有的其它品牌的名称、别称、简称等无法通过识别公司名称获取的品牌词；
当公司名称不包括品牌名称时，识别出的品牌词仍具有与该品牌较强的关联性， 能够认知品牌词的网民通常会知道品牌词与品牌之间的关联，用户可以进一步自行补充设定当前品牌的名称、别称、简称，以及企业所拥有的其它品牌的名称、别称、简称等无法通过识别公司名称获取的品牌词。
图2为图1所示方法的一种优选实施方式中步骤S30的流程图。如图2所示，在本实施例中，步骤S30包括：
S31：获取若干搜索信息，过滤不包括品牌词的搜索信息；
S33：对过滤后的搜索信息进行切词，去除品牌词后统计词频并排序以选取若干高频的关注词；
S35：标注各关注词的关注点。
具体地，在步骤S31中，通过搜索引擎的后台获取大量网民的搜索信息，利用步骤S10获得的品牌词过滤不相关的搜索信息，从而筛选出关注该品牌的搜索信息；
在步骤S33中，对步骤S31筛选出的各搜索信息分别进行切词，并去除品牌词、停用词等词语，得到若干关注词序列。其中，停用词具体指语气词等无法体现网民对品牌的关注点的词。统计若干关注词序列中每个词的词频并进行倒序排列，从而选取出若干词频最高的关注词；
在步骤S35中，标注步骤S33筛选出的各关注词的关注点，例如，“降价”、“促销”、“打折”等关注词的关注点为“价格”，“手感”、“舒适度”、“耐用”等关注词的关注点为“性能”，等等。具体可以通过机器学习的方法进行标注，或，显示以供人工标注等方式进行标注。
通过步骤S30的挖掘和标注，可以获得若干个关注点，每个关注点对应若干个同义、近义或关联度较高的词。
在更多实施例中，在步骤S30中还可以采用本领域常用的不同挖掘算法挖掘品牌词相关的关注词，可实现相同的技术效果。
在步骤S50中，推荐品牌词和关注点对应的任一关注词的组合，例如，推荐品牌词“盛达”和关注点“价格”对应的任一关注词的组合，则有：当采用该组推荐的关键词进行广告投放时，只要搜索信息包括品牌词“盛达”，以及“降价/促销/打折”等任一“价格”对应的关注词，即会触发广告的投放。
上述实施例通过利用品牌词挖掘搜索信息中关注该品牌的关注词，并标注关注词的关注点，获取若干对该品牌具有高关注度的以关注点为单位的关注词集合，最终将品牌词和关注点对应的任一关注词的组合推荐为广告投放的关键词，在保障投放精准度的同时，通过关注点对应的同义或近义的关注词保障投放的覆盖面，从而实现了有效保障广告投放的效果。
图3为图2所示方法的一种优选实施方式的流程图。如图3所示，步骤S10包括步骤S11和S12中的至少一项，即，在一些优选实施例中，可根据实际需求将步骤S10配置为通过上述两种方式中的任一种方式或两种方式获取品牌词。
图4为图3所示方法的一种优选实施方式中步骤S11的流程图。如图4所示，在一优选实施例中，步骤S11具体包括：
S115：对公司名称进行切词，获得分词序列；
S116：标注分词序列中的地名词和机构词；
S117：从前往后遍历分词序列，获得第一个地名词和第一个机构词之间的分词子序列；
S118：从后往前遍历分词子序列，获得词频小于第一阈值且与后一相邻词的紧密度小于第二阈值的分隔词；其中，紧密度根据两个相邻词的词频以及相邻词组频率确定；
S119：将分词子序列中，分隔词之前的词和分隔词标记为品牌词，分隔词之后的词标记为行业词。
具体地，在本实施例中，两个相邻词word 1、word 2的紧密度Aff(word 1,word 2)的计算方式为：
Figure PCTCN2017116229-appb-000001
其中，Freq(word 1)为word 1的词频，Freq(word 2)为word 2的词频，Freq adj(word 1,word 2)为word 1、word 2的相邻词组频率，σ为平滑参数。上述词频和相邻词组频率通过统计获得，平滑参数根据经验进行设置和调节。在更多实施例中，还可将紧密度配置为其它不同的计算方式，只要两个词的紧密度和相邻词组频率呈正相关，即可实现相同的技术效果。
例如，对于公司名称“北京盛达防火设备有限公司”依次执行上述步骤S115-S119：
在步骤S115中，通过切词获得分词序列“北京-盛达-防火-设备-有限-公司”(并通过修正将“有限-公司”合并为“有限公司”)；
在步骤S116中，标注分词序列中的地名词“北京”和机构词“有限公司”；
在步骤S117中，从前往后遍历分词序列，获得第一个地名词“北京”和第一个机构词“有限公司”之间的分词子序列“盛达-防火-设备”；
在步骤S118中，从后往前遍历分词子序列，“设备”的词频大于第一阈值，不符合条件；“防火”的词频小于第一阈值，但通过计算得到“防火”与“设备”的紧密度 大于第二阈值，也不符合条件；“盛达”的词频小于第一阈值，且“盛达”与“防火”的紧密度小于第二阈值，因此判断“盛达”为分隔词；
在步骤S119中，将分词子序列“盛达-防火-设备”中分隔词“盛达”与其之前的词标记为品牌词，将“盛达”之后的“防火”与“设备”标记为行业词。
更进一步地，上述步骤还包括对于特殊类型的公司名称进行修正，例如：
在步骤S115的切词过程中，还包括对特殊类型的公司名称进行修正，例如，去除在公司名称中的“(中国)”，等；
在步骤S117的遍历过程中，若未获得地名词，则将第一个机构词之前的分词序列作为分词子序列；以及，其它针对不同特殊类型的公司名称所配置的修正手段。
上述实施例进一步实现了自动精准识别公司名称中的品牌词，从而无需用户手动设置品牌词。
图5为图4所示方法的一种优选实施方式的流程图。如图5所示，在一优选实施例中，步骤S11进一步还包括：
S111：获取若干公司名称并进行切词，获得若干分词序列；
S112：统计若干分词序列中每个词的词频，以及每对相邻词的相邻词组频率；
S113：计算出每对相邻词的紧密度。
具体地，步骤S111中，获取注册信息表中所有的公司名称，也可以进一步通过其它本领域常用的渠道获取更多的公司名称(并进行去重)，分别对获取的每一公司名称进行切词以获得分词序列；
步骤S112中，通过统计获得每个词的词频和每对相邻词的相邻词组频率；
步骤S113中，根据上述式(1)计算出每对相邻词的紧密度。当步骤S11识别的公司名称在注册信息表中时，步骤S118只需调用步骤S113提前计算出的紧密度，而无需在判断时再作计算。在更多实施例中，同样可以根据所配置的不同计算方式计算紧密度。
图6为图5所示方法的一种优选实施方式的流程图。如图6所示，在一优选实施例中，步骤S11进一步还包括：
S114：将若干分词序列中各末尾词中的高频词标记为机构词。
具体地，在本实施例中，步骤S114中高频词的筛选方式为：高频词的词频不小于第三阈值，以及，各高频词的词频之和与各末尾词的词频之和的比例不小于第四阈值。在更多实施例中，还可采用机器训练、人工标记、获取现有的机构词库进行标记等不同手段标记机构词，可实现相同技术效果。
图7为本发明一实施例提供的一种广告投放方法的流程图。如图7所示，本实施 例提供的一种广告投放方法，包括上述任一实施例提供的广告投放关键词推荐方法，以及：
S70：采用步骤S50推荐的品牌词和关注点对应的任一关注词的组合作为关键词，进行广告投放。
图8为本发明一实施例提供的一种广告投放关键词推荐装置的结构示意图。图8所示的装置可对应执行图1所示的方法。
如图8所示，在本实施例中，本发明提供一种广告投放关键词推荐装置10，包括品牌词获取单元11、关注点挖掘单元13和关键词推荐单元15。
其中，品牌词获取单元11配置用于获取品牌的品牌词；
关注点挖掘单元13配置用于获取搜索信息，根据品牌词挖掘搜索信息中关注该品牌的关注词，并标注关注词的关注点；
关键词推荐单元15配置用于将品牌词和关注点对应的任一关注词的组合推荐为广告投放的关键词。
具体推荐原理参见图1所示的方法，此处不再赘述。
图9为图8所示装置的一种优选实施方式的结构示意图。图9所示的装置可对应执行图2所示的方法。
如图9所示，在一优选实施例中，关注点挖掘单元13包括过滤子单元131、关注词挖掘子单元133和关注点标注子单元135。
其中，过滤子单元131配置用于获取若干搜索信息，过滤不包括所述品牌词的搜索信息；
关注词挖掘子单元133配置用于对过滤后的搜索信息进行切词，去除品牌词后统计词频并排序以选取若干高频的关注词；
关注点标注子单元135配置用于标注各关注词的关注点。
图10为图8所示装置的一种优选实施方式的结构示意图。图10所示的装置可对应执行图3所示的方法。
如图10所示，在一优选实施例中，品牌词获取单元11包括以下至少一项：
品牌词识别单元111，配置用于识别该品牌的公司名称中的品牌词；
品牌词设置单元113，配置用于获取用户设定的该品牌的品牌词。
图11为图10所示装置的一种优选实施方式的结构示意图。图11所示的装置可对应执行图4所示的方法。
如图11所示，在一优选实施例中，品牌词识别单元111包括预处理子单元1113、划分子单元1115和标记子单元1117。
其中，预处理子单元1113配置用于对公司名称进行切词，获得分词序列，标注分词序列中的地名词和机构词，以及，从前往后遍历分词序列，获得第一个地名词和第一个机构词之间的分词子序列(对应于图4所示方法的步骤S115-S117)；
划分子单元1115配置用于从后往前遍历分词子序列，获得词频小于第一阈值且与后一相邻词的紧密度小于第二阈值的分隔词；其中，紧密度根据两个相邻词的词频以及相邻词组频率确定(对应于图4所示方法的步骤S118)；
标记子单元1117配置用于将分词子序列中，分隔词之前的词和分隔词标记为品牌词，分隔词之后的词标记为行业词(对应于图4所示方法的步骤S119)。
图12为图11所示装置的一种优选实施方式的结构示意图。图12所示的装置可对应执行图5-6所示的方法。
如图12所示，在一优选实施例中，品牌词识别单元111进一步还包括数据支撑子单元1111。数据支撑子单元1111配置用于获取若干公司名称并进行切词，获得若干分词序列，统计若干分词序列中每个词的词频，以及每对相邻词的相邻词组频率，以及，计算出每对相邻词的紧密度。
在一优选实施例中，品牌词识别单元111还进一步配置用于将若干分词序列中各末尾词中的高频词标记为机构词。
图13为本发明一实施例提供的一种广告投放装置的结构示意图。图13所示的装置可对应执行图7所示的方法。
如图13所示，本发明还提供一种广告投放装置20，包括上述任一实施例所提供的广告投放关键词推荐装置10，以及投放单元21。
其中，投放单元21配置用于采用关键词推荐单元15所推荐的品牌词和关注点对应的任一关注词的组合作为关键词，进行广告投放。
图14为本发明一实施例提供的一种设备的结构示意图。
如图14所示，作为另一方面，本申请还提供了一种设备1400，包括一个或多个中央处理单元(CPU)1401，其可以根据存储在只读存储器(ROM)1402中的程序或者从存储部分1408加载到随机访问存储器(RAM)1403中的程序而执行各种适当的动作和处理。在RAM1403中，还存储有设备1400操作所需的各种程序和数据。CPU1401、ROM1402以及RAM1403通过总线1404彼此相连。输入/输出(I/O)接口1405也连接至总线1404。
以下部件连接至I/O接口1405：包括键盘、鼠标等的输入部分1406；包括诸如阴极射线管(CRT)、液晶显示器(LCD)等以及扬声器等的输出部分1407；包括硬盘等的存储部分1408；以及包括诸如LAN卡、调制解调器等的网络接口卡的通信部分1409。通信部分1409经由诸如因特网的网络执行通信处理。驱动器1410也根据需要连接至 I/O接口1405。可拆卸介质1411，诸如磁盘、光盘、磁光盘、半导体存储器等等，根据需要安装在驱动器1410上，以便于从其上读出的计算机程序根据需要被安装入存储部分1408。
特别地，根据本公开的实施例，上述任一实施例描述的广告投放关键词推荐方法或广告投放方法可以被实现为计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括有形地包含在机器可读介质上的计算机程序，所述计算机程序包含用于执行广告投放关键词推荐方法或广告投放方法的程序代码。在这样的实施例中，该计算机程序可以通过通信部分1409从网络上被下载和安装，和/或从可拆卸介质1411被安装。
作为又一方面，本申请还提供了一种计算机可读存储介质，该计算机可读存储介质可以是上述实施例的装置中所包含的计算机可读存储介质；也可以是单独存在，未装配入设备中的计算机可读存储介质。计算机可读存储介质存储有一个或者一个以上程序，该程序被一个或者一个以上的处理器用来执行描述于本申请的广告投放关键词推荐方法或广告投放方法。
附图中的流程图和框图，图示了按照本发明各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这根据所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以通过执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以通过专用硬件与计算机指令的组合来实现。
描述于本申请实施例中所涉及到的单元或模块可以通过软件的方式实现，也可以通过硬件的方式来实现。所描述的单元或模块也可以设置在处理器中，例如，各所述单元可以是设置在计算机或移动智能设备中的软件程序，也可以是单独配置的硬件装置。其中，这些单元或模块的名称在某种情况下并不构成对该单元或模块本身的限定。
以上描述仅为本申请的较佳实施例以及对所运用技术原理的说明。本领域技术人员应当理解，本申请中所涉及的发明范围，并不限于上述技术特征的特定组合而成的技术方案，同时也应涵盖在不脱离本申请构思的情况下，由上述技术特征或其等同特征进行任意组合而形成的其它技术方案。例如上述特征与本申请中公开的(但不限于)具有类似功能的技术特征进行互相替换而形成的技术方案。

Claims (20)

  1. 一种广告投放关键词推荐方法，其特征在于，包括：
    获取品牌的品牌词；
    获取搜索信息，根据所述品牌词挖掘所述搜索信息中关注所述品牌的关注词，并标注所述关注词的关注点；
    将所述品牌词和所述关注点对应的任一关注词的组合推荐为广告投放的关键词。
  2. 根据权利要求1所述的方法，其特征在于，所述获取品牌的品牌词包括以下至少一项：
    识别所述品牌的公司名称中的品牌词；
    获取用户设定的所述品牌的品牌词。
  3. 根据权利要求2所述的方法，其特征在于，所述识别所述品牌的公司名称中的品牌词包括：
    对公司名称进行切词，获得分词序列；
    标注所述分词序列中的地名词和机构词；
    从前往后遍历所述分词序列，获得第一个地名词和第一个机构词之间的分词子序列；
    从后往前遍历所述分词子序列，获得词频小于第一阈值且与后一相邻词的紧密度小于第二阈值的分隔词；其中，所述紧密度根据两个相邻词的词频以及相邻词组频率确定；
    将所述分词子序列中，所述分隔词之前的词和所述分隔词标记为品牌词，所述分隔词之后的词标记为行业词。
  4. 根据权利要求3所述的方法，其特征在于，两个相邻词word 1、word 2的紧密度Aff(word 1,word 2)的计算方式为：
    Figure PCTCN2017116229-appb-100001
    其中，Freq(word 1)为word 1的词频，Freq(word 2)为word 2的词频，Freq adj(word 1,word 2)为word 1、word 2的相邻词组频率，σ为平滑参数。
  5. 根据权利要求2或3所述的方法，其特征在于，所述识别所述品牌的公司名称中的品牌词还包括：
    获取若干公司名称并进行切词，获得若干分词序列；
    统计所述若干分词序列中每个词的词频，以及每对相邻词的相邻词组频率；
    计算出每对相邻词的紧密度。
  6. 根据权利要求2-5任一项所述的方法，其特征在于，所述识别所述品牌的公司名称中的品牌词还包括：
    将所述若干分词序列中各末尾词中的高频词标记为机构词。
  7. 根据权利要求6所述的方法，其特征在于，所述高频词的筛选方式为：高频词的词频不小于第三阈值，以及，各高频词的词频之和与各末尾词的词频之和的比例不小于第四阈值。
  8. 根据权利要求1-7任一项所述的方法，其特征在于，所述获取搜索信息，根据所述品牌词挖掘所述搜索信息中关注所述品牌的关注词，并标注所述关注词的关注点包括：
    获取若干搜索信息，过滤不包括所述品牌词的搜索信息；
    对过滤后的搜索信息进行切词，去除品牌词后统计词频并排序以选取若干高频的关注词；
    标注各所述关注词的关注点。
  9. 一种广告投放方法，其特征在于，采用如权利要求1-8任一项所述的广告投放关键词推荐方法所推荐的品牌词和关注点对应的任一关注词的组合作为关键词，进行广告投放。
  10. 一种广告投放关键词推荐装置，其特征在于，包括：
    品牌词获取单元，配置用于获取品牌的品牌词；
    关注点挖掘单元，配置用于获取搜索信息，根据所述品牌词挖掘所述搜索信息中关注所述品牌的关注词，并标注所述关注词的关注点；
    关键词推荐单元，配置用于将所述品牌词和所述关注点对应的任一关注词的组合推荐为广告投放的关键词。
  11. 根据权利要求10所述的装置，其特征在于，所述品牌词获取单元包括以下至少一项：
    品牌词识别单元，配置用于识别所述品牌的公司名称中的品牌词；
    品牌词设置单元，配置用于获取用户设定的所述品牌的品牌词。
  12. 根据权利要求11所述的装置，其特征在于，所述品牌词识别单元包括：
    预处理子单元，配置用于对公司名称进行切词，获得分词序列，标注所述分词序列中的地名词和机构词，以及，从前往后遍历所述分词序列，获得第一个地名词和第一个机构词之间的分词子序列；
    划分子单元，配置用于从后往前遍历所述分词子序列，获得词频小于第一阈值且与后一相邻词的紧密度小于第二阈值的分隔词；其中，所述紧密度根据两个相邻词的词频以及相邻词组频率确定；
    标记子单元，配置用于将所述分词子序列中，所述分隔词之前的词和所述分隔词标记为品牌词，所述分隔词之后的词标记为行业词。
  13. 根据权利要求12所述的装置，其特征在于，两个相邻词word 1、word 2的紧密度Aff(word 1,word 2)的计算方式为：
    Figure PCTCN2017116229-appb-100002
    其中，Freq(word 1)为word 1的词频，Freq(word 2)为word 2的词频，Freq adj(word 1,word 2)为word 1、word 2的相邻词组频率，σ为平滑参数。
  14. 根据权利要求11或12所述的装置，其特征在于，所述品牌词识别单元还包括：
    数据支撑子单元，配置用于获取若干公司名称并进行切词，获得若干分词序列，统计所述若干分词序列中每个词的词频，以及每对相邻词的相邻词组频率，以及，计算出每对相邻词的紧密度。
  15. 根据权利要求11-14任一项所述的装置，其特征在于，所述数据支撑子单元进一步配置用于将所述若干分词序列中各末尾词中的高频词标记为机构词。
  16. 根据权利要求15所述的装置，其特征在于，所述高频词的筛选方式为：高频词的词频不小于第三阈值，以及，各高频词的词频之和与各末尾词的词频之和的比例不小于第四阈值。
  17. 根据权利要求10-16任一项所述的装置，其特征在于，所述关注点挖掘单元包括：
    过滤子单元，配置用于获取若干搜索信息，过滤不包括所述品牌词的搜索信息；
    关注词挖掘子单元，配置用于对过滤后的搜索信息进行切词，去除品牌词后统计词频并排序以选取若干高频的关注词；
    关注点标注子单元，配置用于标注各所述关注词的关注点。
  18. 一种广告投放装置，其特征在于，包括如权利要求10-17任一项所述的广告投放关键词推荐装置，以及，
    投放单元，配置用于采用所推荐的品牌词和关注点对应的任一关注词的组合作为关键词，进行广告投放。
  19. 一种设备，其特征在于，所述设备包括：
    一个或多个处理器；
    存储器，用于存储一个或多个程序，
    当所述一个或多个程序被所述一个或多个处理器执行时，使得所述一个或多个处理器执行如权利要求1-9中任一项所述的方法。
  20. 一种存储有计算机程序的计算机可读存储介质，其特征在于，该程序被处理器执行时实现如权利要求1-9中任一项所述的方法。
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