WO2013044560A1 - 网站推荐方法和***以及网络服务器 - Google Patents
网站推荐方法和***以及网络服务器 Download PDFInfo
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- WO2013044560A1 WO2013044560A1 PCT/CN2011/083681 CN2011083681W WO2013044560A1 WO 2013044560 A1 WO2013044560 A1 WO 2013044560A1 CN 2011083681 W CN2011083681 W CN 2011083681W WO 2013044560 A1 WO2013044560 A1 WO 2013044560A1
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- website
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/535—Tracking the activity of the user
Definitions
- the present invention relates to communication technologies, and in particular, to a website recommendation method and system and a network server. Background technique
- the Internet has changed people's lifestyles. For example, people can use the Internet to get books, movies, music, and even products that they are interested in. Therefore, the Internet has brought us efficient and convenient life. People have become accustomed to using computers, mobile phones and other Internet-enabled devices to learn, entertain, and shop by browsing the web pages they are interested in to meet their multi-faceted needs.
- the web server will recommend the same type of related website to the user for reference according to the type of website visited by the user, for example, the user accesses information technology. For a type of website, the web server will recommend other websites in the information technology type for users to refer to.
- the web server records the websites that users frequently visit and obtains related website recommendations to users, so that users can obtain more information of interest. .
- the network server in the prior art only recommends the relevant website to the user for reference according to the user's own network access behavior, so that the information obtained by the user is limited, and has certain limitations. Summary of the invention
- the foregoing embodiments of the present invention provide a website recommendation method and system, and a network server.
- An embodiment of the present invention provides a website recommendation method, including:
- the network server acquires the feature information of the website accessed by the user who accesses the network within the preset time period according to the locally stored online information;
- the network server performs cluster analysis on the user according to the feature information to obtain a plurality of user clusters, so as to determine the network access request when the user terminal sends the network access request including the user identifier. Determining whether the user includes the first user corresponding to the user identifier, and if yes, determining a website recommended to the first user according to the feature information of the user in the user cluster where the first user is located, and The URL of the recommended website is embedded in the network access response and returned to the user terminal.
- An embodiment of the present invention provides a network server, including:
- the first ear block is configured to acquire, according to the locally stored Internet information, feature information of a website accessed by a user who accesses the network within a preset time period;
- a second acquiring module configured to perform cluster analysis on the user according to the feature information to obtain multiple user clusters
- a determining module configured to: when receiving a network access request that includes a user identifier sent by the user terminal, determining whether the user includes a first user corresponding to the user identifier;
- a processing module configured to determine, according to the feature information of the user in the user cluster where the first user is located, to the first user, if the user is determined to be the first user corresponding to the user identifier a website, and embedding the URL of the recommended website into a network access response and returning to the user terminal.
- Embodiments of the present invention provide a website recommendation system, including the foregoing network server and user terminal.
- the website recommendation method and system and the network server provided by the embodiment of the present invention obtain the feature information corresponding to the website accessed by the user according to the online information of the user in the preset time period, and cluster the user according to the feature information to obtain multiple users.
- a cluster in order to receive a network access request including a user identifier sent by the user terminal, if it is determined that the user includes the first user corresponding to the user identifier, determining, according to the feature information of the remaining users in the user cluster where the first user is located, The website recommended by the first user, and embedding the website address of the recommended website into the network access response and returning to the user terminal, thereby realizing that the network server can recommend more websites to the user who performs network access based on the global user network access behavior, thereby Enable users to get more information of interest.
- FIG. 1 is a flowchart of an embodiment of a website recommendation method according to the present invention
- FIG. 2 is a flowchart of another embodiment of a website recommendation method according to the present invention.
- FIG. 3 is a schematic structural diagram of an embodiment of a network server according to the present invention.
- FIG. 4 is a schematic structural diagram of an embodiment of a website recommendation system according to the present invention.
- the technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention.
- the embodiments are a part of the embodiments of the invention, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without departing from the inventive scope are the scope of the present invention.
- FIG. 1 is a flowchart of an embodiment of a website recommendation method according to the present invention. As shown in FIG. 1, the method includes:
- Step 100 The network server acquires, according to the locally stored online information, feature information of a website visited by a user who accesses the network within a preset time period;
- the user can send a network access request to the network server for network access through a user terminal having a network function such as a mobile phone or a computer, and the network server can store the online information of the user who accesses the network for a period of time according to a preset refresh time.
- the refresh time of the network server is set according to a specific application requirement, for example, three days or one week.
- the online information of the user stored by the network server specifically includes: a user identifier of the user, a website start time and an end time of each visit.
- the user identifier in this embodiment is used to distinguish different users, and there are many forms of user identifiers that can be understood by those skilled in the art according to different application scenarios and different information processing methods.
- the example does not limit the specific form of the user identification.
- the user terminal used by each user has a fixed IP address to identify different users, and the user identifier in the online information of the user stored on the network server of the local area network is the IP address of the user terminal; or the local area network
- the user is required to perform the identity information authentication through the external electronic device when accessing the network. Therefore, the user identifier in the online information of the user stored on the network server of the local area network is used. It can be the identity information of the user.
- the network server obtains the feature information corresponding to the website accessed by the user who accesses the network in the preset time period according to the information stored in the local network.
- the feature information in this embodiment includes each user within a preset time period. Access frequency characteristics of each website and / or each time you visit The server performs setting. For example, if the preset time period is from zero to 24 o'clock every day, that is, the web server collects statistics based on the stored Internet information at 24 o'clock every day to obtain each user who has visited the network within the previous 24 hours. Access the frequency characteristics of each website and/or the time period characteristics of each visit. In a specific implementation process, the network server performs analog-to-digital conversion on the obtained frequency feature or time period feature.
- the feature information includes the frequency feature and the time period feature
- the digital quantity of the two is weighted to obtain the corresponding feature information.
- the feature information in this embodiment is not limited to the frequency feature and the time zone feature, and may be adjusted according to the obtained specific access information to obtain other feature information.
- the specific processing procedure is as above, and details are not described herein. .
- Table 1 shows the feature information corresponding to the website visited by the user who performs network access within a preset time period, and the feature information is for each user.
- the values obtained by performing the analog-to-digital conversion weighting of the frequency characteristics and the time period features of each website during the time period are set.
- Step 101 The network server clusters the user according to the feature information to obtain a plurality of user clusters; corresponding feature information, and performs cluster analysis on all users who perform network access on the network server in the time period.
- Multiple user clusters Cluster Analysis, also known as group analysis, is a process of classifying data into different classes or clusters, so objects in the same cluster have great similarities, and objects between different clusters are very large. The difference.
- the calculation methods of cluster analysis mainly include partitioning methods, hierarchical methods, density-based methods, grid-based methods and model-based models. Model-based methods flick The specific implementation process of each clustering method belongs to the prior art. In order to explain the process of clustering analysis more clearly, the K-mean value in the splitting method is used as an example to illustrate the rest.
- the clustering method is no longer - repeat.
- Step (2) Calculate the distance to each centroid for each remaining user: Assume that the calculation result of user 3 to user 1 is a; assume that the calculation result of user 3 to user 1 is b, if a ⁇ b, Then, the distance from user 3 to user 1 is closer, and user 3 and user 1 are divided into one class; and the remaining users are analogized in turn, and finally all users can be divided into two with user 1 and user 2 as the center of mass.
- the calculation result of user 3 to user 1 is a
- the calculation result of user 3 to user 1 is b, if a ⁇ b, Then, the distance from user 3 to user 1 is closer, and user 3 and user 1 are divided into one class; and the remaining users are analogized in turn, and finally all users can be divided into two with user 1 and user 2 as the center of mass.
- Step (3) Recalculate the centroid for each class.
- the calculation method is to average the weights of each user. After calculating the new centroid of each class, calculate the distance to each centroid for all users, and so on. Until the center of mass no longer changes.
- Step (4) For each class, calculate the mean square error within the class, that is, the distance from all users in the class to the centroid, and compare their mean square error. The trend should be gradually reduced. When the mean square error value drops significantly to no Then the significantly degraded K value can be used as the final K, which is the number of user clusters.
- Step 102 When receiving a network access request that includes the user identifier sent by the user terminal, determining whether the user includes a first user corresponding to the user identifier, and if yes, according to the user cluster in which the first user is located The feature information of the remaining users determines a website recommended to the first user, and embeds the website address of the recommended website into the network access response and returns it to the user terminal.
- the network server When receiving the network access request that is sent by the user terminal, including the user identifier, the network server queries the clustered user according to the user identifier to determine whether the first user corresponding to the user identifier is included. If it is determined that the first user is included in the clustered user, the first user is also subjected to cluster analysis, and the user cluster acquired in step 101 is queried according to the user identifier, and the user cluster where the first user is located is determined. It can be known that the user in the user cluster has similar network access behavior as the first user. Obtaining the feature information of the user in the user cluster where the first user is located, and determining the website recommended to the first user according to the set recommendation rule according to the feature information.
- the user may be obtained according to the feature information of the user in the user cluster.
- the website visited during the preset time period will be divided
- the website visited by the remaining users other than the first user and not visited by the first user is recommended to the first user.
- the recommendation rule is specifically set according to a specific application scenario, and the specific recommendation rule is not limited in this embodiment.
- the web server embeds the URL of the website recommended by the first user into the network access response and returns it to the user terminal.
- the website address includes a domain name and/or an IP address, and the website address of the website accessed by the user in the online information stored on the network server is represented by a domain name or an IP address, and if it is determined according to the online information, the website recommended to the first user is obtained.
- the URL is an IP address.
- the network server can directly embed the IP address into the network access response and return it to the user terminal. It can also send a domain name anti-query request including the IP address to the domain name server.
- the domain name server resolves to the network server through the PTR type domain name resolution.
- the network server embeds the IP address of the website and the corresponding domain name into the network access response and returns it to the user terminal for reference by the first user, and returns the domain name to the user terminal, so that the user can memorize and write. Make it easier for users to retrieve and access recommended websites. If it is determined according to the online information that the website name recommended by the first user is a domain name, the network server may directly embed the domain name into the network access response and return it to the user terminal, or may send a domain name query request including the domain name to the domain name server, the domain name server.
- the IP address corresponding to the domain name is returned to the network server by the domain name resolution of the A type, and the network server embeds the IP address of the website and the corresponding domain name into the network access response, and returns it to the user terminal for reference by the first user, and returns to the user terminal.
- the IP address so that the user can directly search and access the recommended website, and does not need to initiate a domain name query request to the domain name server.
- the web server obtains feature information corresponding to the website accessed by the user who accesses the network in the preset time period according to the online information of the user, and performs cluster analysis on the user according to the feature information to obtain multiple users.
- a cluster when the network server receives the network access request including the user identifier, if it is determined that the clustered user includes the first user corresponding to the user identifier, determining, according to the feature information of the user in the user cluster where the first user is located, The website recommended by the first user, and embedding the URL of the recommended website into the network access response and returning to the user terminal, thereby realizing that the network server can recommend more websites to the user who performs network access based on the global user network access behavior, thereby Enable users to get more information of interest.
- FIG. 2 is a flowchart of another embodiment of a website recommendation method according to the present invention. As shown in FIG. 2, the method includes:
- Step 200 The network server performs the preset time period according to the locally stored Internet access information.
- Step 201 The network server clusters the user according to the feature information to obtain multiple user clusters.
- Step 202 The network server, when receiving the network access request that is sent by the user terminal, including the user identifier, determines whether the user includes the first user corresponding to the user identifier, and if not, broadcasts the information to the remaining network servers.
- the user information and the online information query request of the preset time period if the online information of the first user returned by the remaining network servers in the preset time period is received, according to the online information acquisition
- the network server When receiving the network access request, including the user identifier, sent by the user terminal, the network server queries the clustered user according to the user identifier to determine whether the first user corresponding to the user identifier is included. If it is determined that the first user is not included in the clustered user, the first user does not perform network access through the network server within a preset time period, that is, the network server is in a preset time period. The user who performs network access within does not include the first user.
- the network server broadcasts the online information query request including the user identifier of the first user and the preset time period to the remaining network servers in the Internet system, and the remaining network servers query the request according to the received Internet information, and each network server is based on the first user.
- the user identifier is queried from the online information stored in the preset time period of the local storage to include the online information of the first user, and if the network server can receive the Internet access of the first user returned by the remaining network servers within a preset time period
- the information of the website that is accessed by the first user is obtained according to the information about the first user. For the process of obtaining the specific feature information, refer to step 100 in the first embodiment, and details are not described herein.
- Step 203 The network server acquires corresponding aggregated contour information according to the feature information of the user in each user cluster, and determines, according to the feature information of the first user and the aggregated contour information, by using a similarity measure. Describe the user cluster to which the first user belongs;
- the network server obtains the corresponding aggregated profile information according to the feature information of the user in each user cluster obtained in the foregoing step 201, and the aggregated profile information, that is, the average weight of the feature information corresponding to the website visited by the user in each user cluster;
- the network server performs the similarity measurement according to the feature information of the first user and the acquired aggregated contour information.
- the method of the similarity measure is, for example, a Pearson correlation coefficient or a cosine coefficient, etc., which is not specifically limited in this embodiment.
- Get the website visited by the first user through the similarity measure A matching score of the corresponding feature information and each of the aggregated contour information is used to determine a user cluster to which the first user belongs. The greater the matching score, the higher the similarity between the first user and the user in the user cluster, and the user who selects the maximum matching score.
- the cluster is determined to be the user cluster to which the first user belongs.
- Step 204 Determine, according to the feature information of the user in the user cluster where the first user is located, a website recommended to the first user, and embed the URL of the recommended website into a network access response and return it to the website.
- User terminal Determine, according to the feature information of the user in the user cluster where the first user is located, a website recommended to the first user, and embed the URL of the recommended website into a network access response and return it to the website.
- the feature information is weighted and averaged to obtain the recommended scores of the websites visited by the remaining users, and the recommended scores for each website are determined according to the recommended recommendation criteria of each website accessed by the remaining users, for example, according to the recommended scores of each website.
- the ranking is high and the sites are not accessed by the first user, and the selected website is used as the website recommended to the first user until the number of preset recommended websites.
- the network server embeds the URL of the recommended website into the network access response and returns it to the user terminal for reference by the first user.
- step 201 and step 202 in this embodiment For the specific implementation process of step 201 and step 202 in this embodiment, refer to the embodiment shown in FIG. 1, and details are not described herein again.
- the web server obtains feature information corresponding to the website accessed by the user who accesses the network in the preset time period according to the online information of the user, and performs cluster analysis on the user according to the feature information to obtain multiple users.
- the network server When the network server receives the network access request including the user identifier, if it is determined that the clustered user does not include the first user corresponding to the user identifier, the network server performs a broadcast query to the remaining network servers, and if the remaining network servers are received, Returning the online information of the first user, determining the user cluster where the first user is located, and determining the website recommended to the first user according to the feature information of the user in the user cluster where the first user is located, and embedding the URL of the recommended website Returning to the user terminal in the network access response, the network server can further recommend more websites to users who perform network access based on the global user network access behavior, thereby enabling the user to obtain more information of interest.
- the storage medium includes: a ROM, a RAM, a magnetic disk, or an optical disk, and the like, which can store program codes.
- the network server includes: a first obtaining module 11, a second obtaining module 12, a determining module 13, and a processing module 14, wherein the first obtaining The ear module 11 is configured to acquire, according to the locally stored Internet access information, feature information of a website visited by a user who accesses the website in a preset time period; and the second obtained module 12 is configured to perform cluster analysis on the user according to the feature information to obtain multiple The user cluster; the determining module 13 is configured to: when receiving the network access request that includes the user identifier sent by the user terminal, determine whether the user includes the first user corresponding to the user identifier; and the processing module 14 is configured to determine, if the learned user includes the user identifier The first user determines the website recommended to the first user according to the feature information of the user in the user cluster where the first user is located, and embeds the website address of the recommended website into the network access response to return to the user terminal.
- the second obtaining module 12 can perform cluster analysis on the user according to the feature information by a split method, a hierarchical method, a density-based method, a grid-based method, and a model-based method.
- the processing module 14 is further configured to: if it is determined that the user does not include the first user corresponding to the user identifier, broadcast the online information including the user identifier and the preset time period to the remaining network servers.
- the query request if receiving the online information of the first user returned by the remaining network servers in the preset time period, acquiring the feature information corresponding to the website accessed by the first user according to the online information; obtaining the feature information of the user in each user cluster Corresponding aggregated profile information, and determining, by the similarity measure, the user cluster to which the first user belongs according to the feature information of the first user and the aggregated profile information.
- FIG. 4 is a schematic structural diagram of an embodiment of a website recommendation system according to the present invention.
- the system includes: a network server 1 and a user terminal 2, wherein the network server 1 can be a network server provided by an embodiment of the present invention.
- the terminal 2 is a user terminal according to an embodiment of the present invention.
- the functions and processing procedures of the devices in the website recommendation system provided in this embodiment can be referred to the foregoing method and device embodiment. The implementation principle and technical effects are similar. Let me repeat.
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Abstract
本发明提供一种网站推荐方法和***以及网络服务器，其中，该方法包括：网络服务器根据预设时间段内用户的上网信息获取用户访问的网站的特征信息，根据特征信息对用户进行聚类获取多个用户簇，以便在接收用户终端发送的包括用户标识的网络访问请求时，判断用户是否包括与用户标识对应的第一用户，若是，则根据第一用户所在的用户簇中的其余用户的特征信息确定向第一用户推荐的网站，并将推荐的网站的网址嵌入到网络访问响应中返回给用户终端，实现了网络服务器能够基于全局的用户网络访问行为向进行网络访问的用户推荐更多的网站，从而使用户获取更多感兴趣的资讯。
Description
网站推荐方法和***以及网络服务器
技术领域
本发明涉及通信技术， 尤其涉及一种网站推荐方法和***以及网络服务 器。 背景技术
随着电子信息技术的发展， 网络已经改变了人们的生活方式， 举例来说， 人们可以利用网络获取自己感兴趣的书籍、 电影、 音乐、 甚至商品， 因此， 网络带给了我们高效便捷的生活， 人们已经习惯利用计算机、 手机等具有上 网功能的设备， 通过浏览自己感兴趣的网页进行学习、 娱乐、 购物来满足自 身多方位的需求。
人们利用网络可以更加高效的获取丰富的信息进行学习和娱乐，具体地， 网络服务器会根据用户访问的网站的类型向其推荐同一种类型的相关网站供 用户参考， 比如用户访问的是属于信息技术类型的网站， 网络服务器会向用 户推荐信息技术类型中的其他网站供用户参考， 网络服务器会记录用户经常 访问的网站并获取相关的网站推荐给用户， 从而使用户可以获取更多感兴趣 的资讯。
但是， 现有技术中的网络服务器只是根据用户自身的网络访问行为向用 户推荐相关的网站供用户参考， 使用户获得的信息有限， 具有一定的局限性。 发明内容
针对现有技术的上述缺陷， 本发明实施例提供一种网站推荐方法和*** 以及网络服务器。
本发明实施例提供一种网站推荐方法， 包括：
网络服务器根据本地存储的上网信息获取预设时间段内进行网络访问的 用户访问网站的特征信息；
所述网络服务器根据所述特征信息对所述用户进行聚类分析获取多个用 户簇， 以便在接收用户终端发送的包括用户标识的网络访问请求时， 判断所
述用户是否包括与所述用户标识对应的第一用户， 若是， 则根据所述第一用 户所在的用户簇中用户的所述特征信息确定向所述第一用户推荐的网站， 并 将所述推荐的网站的网址嵌入到网络访问响应中返回给所述用户终端。
本发明实施例提供一种网络服务器， 包括：
第一获耳 莫块， 用于根据本地存储的上网信息获取预设时间段内进行网 络访问的用户访问网站的特征信息；
第二获取模块， 用于根据所述特征信息对所述用户进行聚类分析获取多 个用户簇；
判断模块，用于在接收用户终端发送的包括用户标识的网络访问请求时， 判断所述用户是否包括与所述用户标识对应的第一用户；
处理模块， 用于若判断获知所述用户包括与所述用户标识对应的第一用 户， 则根据所述第一用户所在的用户簇中用户的所述特征信息确定向所述第 一用户推荐的网站， 并将所述推荐的网站的网址嵌入到网络访问响应中返回 给所述用户终端。
本发明实施例提供一种网站推荐***， 包括上述的网络服务器以及用户 终端。
本发明实施例提供的网站推荐方法和***以及网络服务器， 通过网络服 务器根据预设时间段内用户的上网信息获取用户访问的网站对应的特征信 息， 根据特征信息对用户进行聚类获取多个用户簇， 以便在接收用户终端发 送的包括用户标识的网络访问请求时， 若判断获知用户包括与用户标识对应 的第一用户， 则根据第一用户所在的用户簇中的其余用户的特征信息确定向 第一用户推荐的网站， 并将推荐的网站的网址嵌入到网络访问响应中返回给 用户终端， 实现了网络服务器能够基于全局的用户网络访问行为向进行网络 访问的用户推荐更多的网站， 从而使用户获取更多感兴趣的资讯。 附图说明 图 1为本发明网站推荐方法一个实施例的流程图；
图 2为本发明网站推荐方法另一实施例的流程图；
图 3为本发明网络服务器一个实施例的结构示意图；
图 4为本发明网站推荐***一个实施例的结构示意图。
具体实施方式 为使本发明实施例的目的、 技术方案和优点更加清楚， 下面将结合本发 明实施例中的附图， 对本发明实施例中的技术方案进行清楚、 完整地描述， 显然， 所描述的实施例是本发明一部分实施例， 而不是全部的实施例。 基于 本发明中的实施例， 本领域普通技术人员在没有做出创造性劳动前提下所获 得的所有其他实施例， 都属于本发明保护的范围。
图 1为本发明网站推荐方法一个实施例的流程图， 如图 1所示， 该方法 包括：
步骤 100, 网络服务器根据本地存储的上网信息获取预设时间段内进行 网络访问的用户访问网站的特征信息；
用户可以通过手机、 计算机等具有上网功能的用户终端向网络服务器发 送网络访问请求进行网络访问， 网络服务器能够按照预设的刷新时间存储一 段时间内进行网络访问的用户的上网信息。 可以理解的是， 本实施例中网络 服务器的刷新时间是根据具体的应用需要进行设置的比如三天或者一个星 期。 网络服务器存储的用户的上网信息具体包括： 用户的用户标识、 每次访 问的网站和^应的开始时间和结束时间。
需要说明的是， 本实施例中的用户标识用于对不同的用户进行区别， 本 领域的技术人员可以理解的根据不同的应用场景和不同的信息处理手段用户 标识的表现形式有很多， 本实施例对用户标识的具体表现形式不作限制。 比 如在一个局域网中，每个用户所用的用户终端具有固定的 IP地址可以标识不 同的用户， 该局域网的网络服务器上存储的用户的上网信息中的用户标识就 是用户终端的 IP地址； 或者该局域网为了确保用户进行网络访问的安全性， 要求用户进行网络访问时需要通过外插的电子设备进行身份信息认证后才能 进行访问， 因此， 该局域网的网络服务器上存储的用户的上网信息中的用户 标识可以是用户的身份信息。
网络服务器根据本地存储的上网信息获取预设时间段内进行网络访问的 用户访问的网站对应的特征信息， 需要说明的是， 本实施例中的特征信息反 包括每个用户在预设时间段内访问每个网站的频率特征和 /或每次访问的时
服务器进行设置， 举例来说， 若预先设置的时间段为每天的零点到 24点， 即 网络服务器在每天的 24点根据存储的上网信息进行统计获取在之前 24小时 内进行网络访问的每个用户访问每个网站的频率特征和 /或每次访问的时段 特征。 在具体的实现过程中， 网络服务器会将获取的频率特征或时段特征进 行模数转换， 若特征信息包括频率特征和时段特征， 则对两者的数字量进行 加权获取对应的特征信息。 值得注意的是， 本实施例中的特征信息并不局限 于频率特征和时段特征， 还可以根据获取的具体上网信息进行调整从而获取 其他的特征信息， 具体的处理过程如上， 此处不再赘述。 为了更清楚的说明 特征信息含义， 举例说明如表 1所示， 表 1表示了在预设的时间段内进行网 络访问的用户访问的网站对应的特征信息， 特征信息是对每个用户在预设时 间段内访问每个网站的频率特征和时段特征的进行模数转换加权后获取的数 值。
表 1
结合上述表 1中介绍 K-均值的算法如下：
步骤（1 ) : 当用户簇 k=2为例作说明， 在用户 1至用户 4中随机选择 2 个用户作为初始质心 （类别的中心） ， 假设选择用户 1和用户 2 ;
步骤（2 ) : 对于剩下的每一个用户计算其到每个质心的距离： 假设用户 3到用户 1的计算结果为 a;假设用户 3到用户 1的计算结果为 b, 若 a<b,则 用户 3到用户 1的距离更近， 用户 3和用户 1被划分到一个类中； 对剩下的 用户依次类推， 最终可以将所有的用户划分到以用户 1和用户 2为质心的两 个类中；
步骤（3 ) : 对每一个类重新计算质心， 计算方法为将各用户的权重求平 均， 计算出每个类的新质心后，对于所有的用户，计算其到每个质心的距离， 如此反复， 直到质心不再发生变化。
步骤（4 ) : 对于每一个类计算类内均方误差， 即类内所有用户到质心的 距离， 比较它们的均方误差， 趋势应该为逐渐减小， 当均方误差值由显著下 降到不那么显著下降的 K值就可以作为最终的 K,即用户簇的个数。
步骤 102, 在接收用户终端发送的包括用户标识的网络访问请求时， 判 断所述用户是否包括与所述用户标识对应的第一用户， 若是， 则根据所述第 一用户所在的用户簇中的其余用户的所述特征信息确定向所述第一用户推荐 的网站， 并将所述推荐的网站的网址嵌入到网络访问响应中返回给所述用户 终端。
网络服务器接收到用户终端发送的包括用户标识的网络访问请求时， 根 据用户标识查询经过聚类的用户判断是否包括与该用户标识对应的第一用 户。 若判断获知经过聚类的用户中包括该第一用户， 说明该第一用户也经过 了聚类分析， 根据用户标识查询步骤 101 中获取的用户簇并确定第一用户所 在的用户簇， 基于上述可以获知该用户簇中的用户与第一用户具有相似的网 络访问行为。 获取第一用户所在的用户簇中的用户的特征信息， 并根据特征 信息按照设置的推荐规则确定向第一用户推荐的网站， 举例来说， 可以根据 该用户簇中用户的特征信息获取用户在预设的时间段内所访问的网站， 将除
第一用户之外的其余用户所访问过且第一用户没有访问过的网站推荐给第一 用户。 需要说明的是， 推荐规则根据具体的应用场景进行具体设置， 本实施 例不对具体的推荐规则作限制。
网络服务器将向第一用户推荐的网站的网址嵌入到网络访问响应中返回 给用户终端。 其中， 网站的网址包括域名和 /或 IP地址， 网络服务器上存储的 上网信息中用户访问的网站的网址是用域名或 IP地址来表示的， 若根据上网 信息判断获知向第一用户推荐的网站的网址是 IP地址， 网络服务器可以直接 将 IP地址嵌入到网络访问响应中返回给用户终端， 也可以向域名服务器发送 包括 IP地址的域名反查询请求， 域名服务器通过 PTR类型的域名解析向网 络服务器返回与 IP地址对应的域名， 网络服务器将网站的 IP地址和对应的 域名都嵌入到网络访问响应中返回给用户终端供第一用户进行参考， 向用户 终端返回域名， 方便用户记忆和书写， 从而使用户更加方便的对推荐的网站 进行检索和访问。 若根据上网信息判断获知向第一用户推荐的网站的网址是 域名， 网络服务器可以直接将域名嵌入到网络访问响应中返回给用户终端， 也可以向域名服务器发送包括域名的域名查询请求， 域名服务器通过 A类型 的域名解析向网络服务器返回与域名对应的 IP地址，网络服务器将网站的 IP 地址和对应的域名都嵌入到网络访问响应中返回给用户终端供第一用户进行 参考， 向用户终端返回 IP地址， 从而使用户更加直接的对推荐的网站进行检 索和访问， 不需要向域名服务器发起域名查询请求。
本实施例提供的网站推荐方法， 通过网络服务器根据用户的上网信息获 取预设时间段内进行网络访问的用户访问的网站对应的特征信息， 并根据特 征信息对用户进行聚类分析获取多个用户簇， 当网络服务器接收到包括用户 标识的网络访问请求时， 若判断获知经过聚类的用户包括与用户标识对应的 第一用户， 则根据第一用户所在的用户簇中用户的特征信息确定向第一用户 推荐的网站，并将推荐的网站的网址嵌入到网络访问响应中返回给用户终端， 实现了网络服务器能够基于全局的用户网络访问行为向进行网络访问的用户 推荐更多的网站， 从而使用户获取更多感兴趣的资讯。
图 2为本发明网站推荐方法另一实施例的流程图， 如图 2所示， 该方法 包括：
步骤 200, 网络服务器根据本地存储的上网信息获取预设时间段内进行
网络访问的用户访问网站的特征信息；
步骤 201 , 所述网络服务器根据所述特征信息对所述用户进行聚类获取 多个用户簇；
步骤 202 , 所述网络服务器在接收用户终端发送的包括用户标识的网络 访问请求时， 判断所述用户是否包括与所述用户标识对应的第一用户， 若不 是， 则向其余网络服务器广播包括所述用户标识和所述预设时间段的上网信 息查询请求， 若接收到所述其余网络服务器返回的所述第一用户在所述预设 时间段内的上网信息， 根据所述上网信息获取所述第一用户访问的网站对应 的特征信息；
网络服务器接收到用户终端发送的包括用户标识的网络访问请求时， 根 据用户标识查询经过聚类的用户判断是否包括与该用户标识对应的第一用 户。 若判断获知经过聚类的用户中不包括该第一用户， 说明该第一用户没有 在预设的时间段内通过该网络服务器进行过网络访问， 也就是说该网络服务 器在预设的时间段内的进行网络访问的用户不包括该第一用户。
网络服务器向互联网***中的其余网络服务器广播包括第一用户的用户 标识和预设时间段的上网信息查询请求， 其余的网络服务器根据接收到的上 网信息查询请求， 各网络服务器均根据第一用户的用户标识从本地存储的预 设时间段内的上网信息中查询是否包括该第一用户的上网信息， 若该网络服 务器能够接收到其余网络服务器返回的第一用户在预设时间段内的上网信 息， 根据第一用户的上网信息获取该第一用户访问的网站对应的特征信息， 具体的特征信息获取过程参见上述实施例一中的步骤 100, 此处不再赘述。
步骤 203 , 所述网络服务器根据每个用户簇中用户的所述特征信息获取 对应的聚集轮廓信息， 并根据所述第一用户的所述特征信息和所述聚集轮廓 信息通过相似性度量确定所述第一用户所属的用户簇；
网络服务器根据上述步骤 201 中获取的每个用户簇中用户的特征信息获 取对应的聚集轮廓信息， 聚集轮廓信息即每一个用户簇中的用户访问的网站 对应的特征信息的平均权重；
网络服务器根据第一用户的特征信息和获取的聚集轮廓信息进行相似性 度量， 值得注意的是， 相似性度量的方法很多例如皮尔森相关系数或者余弦 系数等， 本实施例不作具体限制。 通过相似性度量获取第一用户访问的网站
对应的特征信息与各个聚集轮廓信息的匹配分数以确定第一用户所属的用户 簇， 匹配分数越大， 说明第一用户与该用户簇中的用户的相似度越高， 选择 最大匹配分数的用户簇确定为第一用户所属的用户簇。
步骤 204 , 根据所述第一用户所在的用户簇中的用户的所述特征信息确 定向所述第一用户推荐的网站， 并将所述推荐的网站的网址嵌入到网络访问 响应中返回给所述用户终端。
获取第一用户所在的用户簇中的用户的特征信息， 并根据特征信息按照 设置的推荐规则确定向第一用户推荐的网站， 具体地， 可以对第一用户所在 的用户簇中的其余用户的特征信息进行加权平均获取其余用户访问的网站的 推荐分数， 根据其余用户访问的每个网站的推荐分数按照预设的推荐准则确 定向第一用户推荐的网站， 比如根据每个网站的推荐分数从高往低进行排列 并且这些网站没有被第一用户访问过， 直到预设的推荐网站的数量为止， 将 选出来的网站作为向第一用户推荐的网站。 网络服务器将推荐的网站的网址 嵌入到网络访问响应中返回给用户终端供第一用户进行参考， 具体过程参见 上述实施例， 此处不再赘述。
本实施例中的步骤 201和步骤 202的具体实施过程参见图 1所示的实施 例， 此处不再赘述。
本实施例提供的网站推荐方法， 通过网络服务器根据用户的上网信息获 取预设时间段内进行网络访问的用户访问的网站对应的特征信息， 并根据特 征信息对用户进行聚类分析获取多个用户簇， 当网络服务器接收到包括用户 标识的网络访问请求时， 若判断获知经过聚类的用户不包括与用户标识对应 的第一用户， 则向其余网络服务器进行广播查询， 若接收到其余网络服务器 返回的第一用户的上网信息， 则确定第一用户所在的用户簇， 并根据第一用 户所在的用户簇中用户的特征信息确定向第一用户推荐的网站， 并将推荐的 网站的网址嵌入到网络访问响应中返回给用户终端， 实现了网络服务器能够 进一步地基于全局的用户网络访问行为向进行网络访问的用户推荐更多的网 站， 从而使用户获取更多感兴趣的资讯。
本领域普通技术人员可以理解： 实现上述方法实施例的全部或部分步骤 可以通过程序指令相关的硬件来完成， 前述的程序可以存储于一计算机可读 取存储介质中， 该程序在执行时， 执行包括上述方法实施例的步骤； 而前述
的存储介质包括： ROM、 RAM, 磁碟或者光盘等各种可以存储程序代码的介 质。
图 3为本发明网络服务器一个实施例的结构示意图， 如图 3所示， 该网 络服务器包括： 第一获取模块 11、 第二获取模块 12、 判断模块 13和处理模 块 14, 其中， 第一获耳 莫块 11用于根据本地存储的上网信息获取预设时间 段内进行网络访问的用户访问网站的特征信息；第二获耳 莫块 12用于根据特 征信息对用户进行聚类分析获取多个用户簇；判断模块 13用于在接收用户终 端发送的包括用户标识的网络访问请求时， 判断用户是否包括与用户标识对 应的第一用户；处理模块 14用于若判断获知用户包括与用户标识对应的第一 用户， 则根据第一用户所在的用户簇中用户的特征信息确定向第一用户推荐 的网站，并将推荐的网站的网址嵌入到网络访问响应中返回给所述用户终端。
针对图 3所示的实施例，第二获取模块 12可以根据特征信息通过***法、 层次法、 基于密度的方法、 基于网格的方法和基于模型的方法对用户进行聚 类分析。
本实施例提供的网络服务器中各模块的功能和处理流程， 可以参见上述 图 1所示的方法实施例， 其实现原理和技术效果类似， 此处不再赘述。
基于图 3所示的实施例， 进一步地， 处理模块 14还用于若判断获知用户 没有包括与用户标识对应的第一用户， 则向其余网络服务器广播包括用户标 识和预设时间段的上网信息查询请求， 若接收到其余网络服务器返回的第一 用户在预设时间段内的上网信息， 根据上网信息获取第一用户访问的网站对 应的特征信息；根据每个用户簇中用户的特征信息获取对应的聚集轮廓信息， 并根据第一用户的特征信息和聚集轮廓信息通过相似性度量确定第一用户所 属的用户簇。
本实施例提供的网络服务器中各模块的功能和处理流程， 可以参见上述 图 2所示的方法实施例， 其实现原理和技术效果类似， 此处不再赘述。
图 4为本发明网站推荐***一个实施例的结构示意图， 如图 4所示， 该 ***包括： 网络服务器 1以及用户终端 2, 其中， 网络服务器 1 可以为本发 明实施例提供的网络服务器，用户终端 2为本发明实施例涉及到的用户终端， 本实施例提供的网站推荐***中各装置的功能和处理流程， 可以参见上述方 法和装置实施例， 其实现原理和技术效果类似， 此处不再赘述。
最后应说明的是： 以上实施例仅用以说明本发明的技术方案， 而非对其 限制； 尽管参照前述实施例对本发明进行了详细的说明， 本领域的普通技术 人员应当理解： 其依然可以对前述各实施例所记载的技术方案进行修改， 或 者对其中部分技术特征进行等同替换； 而这些修改或者替换， 并不使相应技 术方案的本质脱离本发明各实施例技术方案的精神和范围。
Claims
1、 一种网站推荐方法， 其特征在于， 包括：
网络服务器根据本地存储的上网信息获取预设时间段内进行网络访问的 用户访问网站的特征信息；
所述网络服务器根据所述特征信息对所述用户进行聚类分析获取多个用 户簇， 以便在接收用户终端发送的包括用户标识的网络访问请求时， 判断所 述用户是否包括与所述用户标识对应的第一用户， 若是， 则根据所述第一用 户所在的用户簇中的用户的所述特征信息确定向所述第一用户推荐的网站， 并将所述推荐的网站的网址嵌入到网络访问响应中返回给所述用户终端。
2、 根据权利要求 1所述的网站推荐方法， 其特征在于， 若判断获知所述 用户没有包括与所述用户标识对应的第一用户， 所述方法还包括：
所述网络服务器向其余网络服务器广播包括所述用户标识和所述预设时 间段的上网信息查询请求， 若接收到所述其余网络服务器返回的所述第一用 户在所述预设时间段内的上网信息， 根据所述上网信息获取所述第一用户访 问的网站对应的特征信息；
所述网络服务器根据每个用户簇中用户的所述特征信息获取对应的聚集 轮廓信息， 并根据所述第一用户的所述特征信息和所述聚集轮廓信息通过相 似性度量确定所述第一用户所属的用户簇。
3、 根据权利要求 1所述的网站推荐方法， 其特征在于， 所述根据所述特 征信息对所述用户进行聚类分析包括：
根据所述特征信息通过***法、 层次法、 基于密度的方法、 基于网格的 方法和基于模型的方法对所述用户进行聚类分析。
4、 根据权利要求 1所述的网站推荐方法， 其特征在于， 所述根据所述第 一用户所在的用户簇中的其余用户的所述特征信息确定向所述第一用户推荐 的网站包括：
根据所述第一用户所在的用户簇中的其余用户的所述特征信息获取所述 其余用户访问的网站的推荐分数；
根据所述推荐分数按照预设的推荐准则确定向所述第一用户推荐的网 站。
5、 根据权利要求 1-4任一项所述的网站推荐方法， 其特征在于， 所述特 征信息包括： 预设时间段内所述用户访问网站的频率特征和 /或每次访问的时 段特征。
6、 根据权利要求 1-4任一项所述的网站推荐方法， 其特征在于， 所述网 址包括： 域名和 /或 IP地址。
7、 一种网络服务器， 其特征在于， 包括：
第一获耳 莫块， 用于根据本地存储的上网信息获取预设时间段内进行网 络访问的用户访问网站的特征信息；
第二获取模块， 用于根据所述特征信息对所述用户进行聚类分析获取多 个用户簇；
判断模块，用于在接收用户终端发送的包括用户标识的网络访问请求时， 判断所述用户是否包括与所述用户标识对应的第一用户；
处理模块， 用于若判断获知所述用户包括与所述用户标识对应的第一用 户， 则根据所述第一用户所在的用户簇中用户的所述特征信息确定向所述第 一用户推荐的网站， 并将所述推荐的网站的网址嵌入到网络访问响应中返回 给所述用户终端。
8、 根据权利要求 7所述的网络服务器， 其特征在于， 所述处理模块， 还 用于：
若判断获知所述用户没有包括与所述用户标识对应的第一用户， 则向其 余网络服务器广播包括所述用户标识和所述预设时间段的上网信息查询请 求， 若接收到所述其余网络服务器返回的所述第一用户在所述预设时间段内 的上网信息， 根据所述上网信息获取所述第一用户访问的网站对应的特征信 息；
根据每个用户簇中用户的所述特征信息获取对应的聚集轮廓信息， 并根 据所述第一用户的所述特征信息和所述聚集轮廓信息通过相似性度量确定所 述第一用户所属的用户簇。
9、 根据权利要求 8所述的网络服务器， 其特征在于， 所述第二获取模块 具体用于：
根据所述特征信息通过***法、 层次法、 基于密度的方法、 基于网格的 方法和基于模型的方法对所述用户进行聚类分析。
10、 一种网站推荐***， 其特征在于， 包括如权利要求 7或 8或 9任一 项所述的网络服务器， 以及用户终端。
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