面向个性化服务的User Profile研究及应用
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摘要
个性化服务可以根据用户需求、习惯、偏好等特征，形成有针对性的呈现信息检索结果和推荐，从而有效提升用户体验和服务满意度。互联网服务供应商正逐步以提供个性化服务作为企业的核心竞争力。识别和理解用户特征是开展差异化服务的依据，创建User Profile则是个性化服务的关键因素。随着协同标签系统、社交网络和移动互联网的迅速发展，用户特征信息来源越来越丰富，如何从结构不同、潜在语义不同、规模不同的各种信息数据中根据应用需求创建User Profile，从而提供高品质的个性化服务正成为学术界和产业界共同的研究目标。
针对个性化服务的User Profile构建是当前一个研究热点，但现有的方法在用户特征描述的准确度上还存在一些不足之处。本文针对User Profile创建及个性化服务应用中的三个问题进行了研究，包括理解用户多标签标注行为，识别用户使用标签的情感倾向以及考虑社交关系对个性化服务的影响。本文的创新性研究成果主要有：
（1）现有协同标签系统应用中，User Profile基于标签个体创建，当用户同时使用多个标签标注资源时，User Profile难以描述标签集合所反映的用户偏好特征。针对该问题，本文提出了基于标签群组的用户向量模型，基于此模型提出了两种创建User Profile的方法：第一种是基于标签群组频率的Tag-Group Based User Profiling（以下简称TGB）方法，该方法应用于个性化检索的实验证明，TGB方法相对于对比算法在多个指标上有更好的表现；第二种是基于标签集合粘合度的Tag-Group Integration Based User Profiling（以下简称TGIB）方法，该方法应用于推荐系统的实验证明，TGIB方法相对于对比算法在MAE指标上有更好的表现。
（2）现有的User Profile创建方法仅考虑采用用户标注的标签来创建User Profile，它们都是基于如下假设：用户标注的资源是其关注/喜欢的资源。在实际应用中，这个假设是不合理的，部分标签反映的是用户对资源的嫌恶态度。针对该问题，本文提出了标签的情感倾向分类方法和基于标签情感的三层用户向量模型，基于此模型提出了一种创建User Profile的Three-Level User Profiling（以下简称TUP）方法。然后本文进一步地将三层用户向量模型一般化为多层用户向量模型，提出了创建User Profile的Multi-LevelUser Profiling（以下简称MUP）方法。这两种方法应用于个性化检索的实验证明，MUP方法取三层时（即TUP方法）有最佳表现，TUP方法相对于对比算法在多个指标上有更好的表现。
（3）传统交互式问答系统正面临答案质量下降和答案平均等待时间增加两个问题，结合社交网络与问答系统，本文提出了基于社交关系的问答咨询系统（以下简称“社交问答系统”），将检索信息/资源的问题转变为检索可以提供信息/资源的用户的问题。在此基础上，本文提出了基于关系传递的关系强度模型和基于社交关系的用户模型，基于此模型提出了一种在社交问答系统中创建User Profile的方法。实验证明，与传统的交互式问答系统相比，本文提出的社交问答系统在经验性强、实时性强等分类问题中有更好的表现。
Personalized services on the Internet can obtain a personalized search results accordingto users’ needs, habits, preference and other characteristics, thus can improve the userexperience and service satisfaction effectively. Nowadays, Internet service providers tend toprovide personalized service, which is regard as the core competitiveness of enterprises. Thebasis of differentiated services is user feature recognition and understanding, and the key ofpersonalized service is to create User Profiles. With the rapid development of CollaborativeTagging Systems, Social Networks and Mobile Internet, users characteristic informationbecome more and more rich. How to create user profiles from different structures and allkinds of information data so as to provide high quality personalized service is a commonresearch goal of academia and industry.
In this article, we explore three important problems of personalized service and userprofile construction, including understanding user labeling behavior, identifying users’sentiment tendency of using labels and considering the influence of the social relations inpersonalized service. The contributions of our research work mainly include:
(1)In view of the Tag-group effect, we propose User Vector Model based on Tag-group.Then we put forward two methods to create User Profile base on this model. The first one isTag-Group Based User Profiling (TGB) method. The experimental results of personalizedinformation retrieval show that TGB method has better performance on multiple indicesrelative to the baseline algorithms. The second is Tag-Group Integration Based User Profiling(TGIB) method. TGIB filter tag-groups based on the user characteristic expressions. Theexperimental results of recommendation show that TGIB method has better performance onMAE metric than the baseline algorithms.
(2)We put forward the label sentiment classification standards and Three-Level VectorModel based on the sentiment polarity of labels. We propose a Three-Level User Profiling(TUP) method to construct user profiles. What’s more, we generalize the Three-Level VectorModel to Multi-Level Vector Model. The experimental results of personalized retrieval showthat TUP method has the best performance in various levels of MUP approach. Besides, TUP method has better performance on multiple indices than other baseline algorithms.
(3)We put forward Social Q&A System and its general framework, exploring theproblem form retrieving the relevant information or resources into recommending users whocan provide information or resource. Based on Social Q&A System, we present a RelationshipIntensity Model which is based on the transfer of relationships and a User Model which isbased on social relations. Then we propose a method to construct user profiles in Social Q&ASystem. The experimental results show that this method can discover suitable respondentswho are willing to answer and are familiar with related field, by comparing with thetraditional Q&A System.
针对个性化服务的User Profile构建是当前一个研究热点，但现有的方法在用户特征描述的准确度上还存在一些不足之处。本文针对User Profile创建及个性化服务应用中的三个问题进行了研究，包括理解用户多标签标注行为，识别用户使用标签的情感倾向以及考虑社交关系对个性化服务的影响。本文的创新性研究成果主要有：
（1）现有协同标签系统应用中，User Profile基于标签个体创建，当用户同时使用多个标签标注资源时，User Profile难以描述标签集合所反映的用户偏好特征。针对该问题，本文提出了基于标签群组的用户向量模型，基于此模型提出了两种创建User Profile的方法：第一种是基于标签群组频率的Tag-Group Based User Profiling（以下简称TGB）方法，该方法应用于个性化检索的实验证明，TGB方法相对于对比算法在多个指标上有更好的表现；第二种是基于标签集合粘合度的Tag-Group Integration Based User Profiling（以下简称TGIB）方法，该方法应用于推荐系统的实验证明，TGIB方法相对于对比算法在MAE指标上有更好的表现。
（2）现有的User Profile创建方法仅考虑采用用户标注的标签来创建User Profile，它们都是基于如下假设：用户标注的资源是其关注/喜欢的资源。在实际应用中，这个假设是不合理的，部分标签反映的是用户对资源的嫌恶态度。针对该问题，本文提出了标签的情感倾向分类方法和基于标签情感的三层用户向量模型，基于此模型提出了一种创建User Profile的Three-Level User Profiling（以下简称TUP）方法。然后本文进一步地将三层用户向量模型一般化为多层用户向量模型，提出了创建User Profile的Multi-LevelUser Profiling（以下简称MUP）方法。这两种方法应用于个性化检索的实验证明，MUP方法取三层时（即TUP方法）有最佳表现，TUP方法相对于对比算法在多个指标上有更好的表现。
（3）传统交互式问答系统正面临答案质量下降和答案平均等待时间增加两个问题，结合社交网络与问答系统，本文提出了基于社交关系的问答咨询系统（以下简称“社交问答系统”），将检索信息/资源的问题转变为检索可以提供信息/资源的用户的问题。在此基础上，本文提出了基于关系传递的关系强度模型和基于社交关系的用户模型，基于此模型提出了一种在社交问答系统中创建User Profile的方法。实验证明，与传统的交互式问答系统相比，本文提出的社交问答系统在经验性强、实时性强等分类问题中有更好的表现。
Personalized services on the Internet can obtain a personalized search results accordingto users’ needs, habits, preference and other characteristics, thus can improve the userexperience and service satisfaction effectively. Nowadays, Internet service providers tend toprovide personalized service, which is regard as the core competitiveness of enterprises. Thebasis of differentiated services is user feature recognition and understanding, and the key ofpersonalized service is to create User Profiles. With the rapid development of CollaborativeTagging Systems, Social Networks and Mobile Internet, users characteristic informationbecome more and more rich. How to create user profiles from different structures and allkinds of information data so as to provide high quality personalized service is a commonresearch goal of academia and industry.
In this article, we explore three important problems of personalized service and userprofile construction, including understanding user labeling behavior, identifying users’sentiment tendency of using labels and considering the influence of the social relations inpersonalized service. The contributions of our research work mainly include:
(1)In view of the Tag-group effect, we propose User Vector Model based on Tag-group.Then we put forward two methods to create User Profile base on this model. The first one isTag-Group Based User Profiling (TGB) method. The experimental results of personalizedinformation retrieval show that TGB method has better performance on multiple indicesrelative to the baseline algorithms. The second is Tag-Group Integration Based User Profiling(TGIB) method. TGIB filter tag-groups based on the user characteristic expressions. Theexperimental results of recommendation show that TGIB method has better performance onMAE metric than the baseline algorithms.
(2)We put forward the label sentiment classification standards and Three-Level VectorModel based on the sentiment polarity of labels. We propose a Three-Level User Profiling(TUP) method to construct user profiles. What’s more, we generalize the Three-Level VectorModel to Multi-Level Vector Model. The experimental results of personalized retrieval showthat TUP method has the best performance in various levels of MUP approach. Besides, TUP method has better performance on multiple indices than other baseline algorithms.
(3)We put forward Social Q&A System and its general framework, exploring theproblem form retrieving the relevant information or resources into recommending users whocan provide information or resource. Based on Social Q&A System, we present a RelationshipIntensity Model which is based on the transfer of relationships and a User Model which isbased on social relations. Then we propose a method to construct user profiles in Social Q&ASystem. The experimental results show that this method can discover suitable respondentswho are willing to answer and are familiar with related field, by comparing with thetraditional Q&A System.
引文
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