网络检索用户行为可靠性分析

基于大规模真实网络用户的行为日志,对用户与网络搜索引擎系统的交互过程和用户决策过程展开研 究.通过比较具有相关信息的用户点击和普通点击的分布,对用户点击的3 类上下文背景特征进行分析,从而实现对 用户点击的可靠性评估.实验结果表明,通过对用户点击的上下文背景的特征分析,能够发现用户检索行为中的思维 决策过程,并进而对用户点击的可靠性进行有效的评估.     

Learning to rank: new approach with the layered multi-population genetic programming on click-through features

Users’ click-through data is a valuable source of information about the performance of Web search engines, but it is included in few datasets for learning to rank. In this paper, inspired by the click-through data model, a novel approach is proposed for extracting the implicit user feedback from evidence embedded in benchmarking datasets. This process outputs a set of new features, named click-through features. Generated click-through features are used in a layered multi-population genetic programming framework to find the best possible ranking functions. The layered multi-population genetic programming framework is fast and provides more extensive search capability compared to the traditional genetic programming approaches. The performance of the proposed ranking generation framework is investigated both in the presence and in the absence of explicit click-through data in the utilized benchmark datasets. The experimental results show that click-through features can be efficiently extracted in both cases but that more effective ranking functions result when click-through features are generated from benchmark datasets with explicit click-through data. In either case, the most noticeable ranking improvements are achieved at the tops of the provided ranked lists of results, which are highly targeted by the Web users.     

How do users describe their information need: Query recommendation based on snippet click model

Query recommendation helps users to describe their information needs more clearly so that search engines can return appropriate answers and meet their needs. State-of-the-art researches prove that the use of users’ behavior information helps to improve query recommendation performance. Instead of finding the most similar terms previous users queried, we focus on how to detect users’ actual information need based on their search behaviors. The key idea of this paper is that although the clicked documents are not always relevant to users’ queries, the snippets which lead them to the click most probably meet their information needs. Based on analysis into large-scale practical search behavior log data, two snippet click behavior models are constructed and corresponding query recommendation algorithms are proposed. Experimental results based on two widely-used commercial search engines’ click-through data prove that the proposed algorithms outperform practical recommendation methods of these two search engines. To the best of our knowledge, this is the first time that snippet click models are proposed for query recommendation task.     

Herd behavior in social media: The role of Facebook likes,strength of ties,and expertise

When do social media users click on sponsored content or intend to visit the website at a later time? A qualitative comparative analysis (QCA) using arguments based on herd theory, strength of ties, and social distance shows that only “likes” from socially close and knowledgeable users can consistently generate click-through or view-through intentions. Considering social tie strength in a herd behavior context, the analysis of sufficient configurations for click- and view-through intentions provides a nuanced perspective on social media user behavior and social influence. For instance, click-through intention requires observing a “like” from a close person, while view-through intentions can also develop after observing “likes” from less close acquaintances, yet in the last case only if the user assumes the acquaintance is better informed regarding the sponsored content. In addition, a “like” from a close friend deemed better informed can even make a user click on a sponsored content that was not considered valuable before.     

Aggregation of web search engines based on users’ preferences in WebFusion

The required information of users is distributed in the databases of various search engines. It is inconvenient and inefficient for an ordinary user to invoke multiple search engines and identify useful documents from the returned results. Meta-search engines could provide a unified access for their users. In this paper, a novel meta-search engine, named as WebFusion, is introduced. WebFusion learns the expertness of the underlying search engines in a certain category based on the users’ preferences. It also uses the “click-through data concept” to give a content-oriented ranking score to each result page. Click-through data concept is the implicit feedback of the users’ preferences, which is also used as a reinforcement signal in the learning process, to predict the users’ preferences and reduces the seeking time in the returned results list. The decision lists of underling search engines have been fused using ordered weighted averaging (OWA) approach and the application of optimistic operator as weightening function has been investigated. Moreover, the results of this approach have been compared with those achieve by some popular meta-search engines such as ProFusion and MetaCrawler. Experimental results demonstrate a significant improvement on average click rate, and the variance of clicks as well as average relevancy criterion.     

Search personalization through query and page topical analysis

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user’s topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user’s current query and the query-matching pages, in order to identify the user’s current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.     

基于学习自动机和用户兴趣的PageRank算法研究

针对传统PageRank算法存在的平分链接权重和忽略用户兴趣等问题,提出一种基于学习自动机和用户兴趣的页面排序算法LUPR。在所提方法中,给每个网页分配学习自动机,其功能是确定网页之间超链接的权重。通过对用户行为进一步分析,以用户的浏览行为衡量用户对网页的兴趣度,从而获得兴趣度因子。该算法根据网页间的超链接和用户对网页的兴趣度衡量网页权重计算每个网页的排名。最后的仿真实验表明,较传统的PageRank算法和WPR算法,改进后的LUPR算法在一定程度上提高了信息检索的准确度和用户满意度。     

Understanding the role of human-inspired heuristics for retrieval models

Relevance estimation is one of the core concerns of information retrieval(IR)studies.Although existing retrieval models gained much success in both deepening our understanding of information seeking behavior and building effective retrieval systems,we have to admit that the models work in a rather different manner from how humans make relevance judgments.Users’information seeking behaviors involve complex cognitive processes,however,the majority of these behavior patterns are not considered in existing retrieval models.To bridge the gap between practical user behavior and retrieval model,it is essential to systematically investigate user cognitive behavior during relevance judgement and incorporate these heuristics into retrieval models.In this paper,we aim to formally define a set of basic user reading heuristics during relevance judgement and investigate their corresponding modeling strategies in retrieval models.Further experiments are conducted to evaluate the effectiveness of different reading heuristics for improving ranking performance.Based on a large-scale Web search dataset,we find that most reading heuristics can improve the performance of retrieval model and establish guidelines for improving the design of retrieval models with human-inspired heuristics.Our study sheds light on building retrieval model from the perspective of cognitive behavior.     

User click prediction for personalized job recommendation

Major job search engines aggregate tens of millions of job postings online to enable job seekers to find valuable employment opportunities. Predicting the probability that a given user clicks on jobs is crucial to job search engines as the prediction can be used to provide personalized job recommendations for job seekers. This paper presents a real-world job recommender system in which job seekers subscribe to email alert to receive new job postings that match their specific interests. The architecture of the system is introduced with the focus on the recommendation and ranking component. Based on observations of click behaviors of a large number of users in a major job search engine, we develop a set of features that reflect the click behavior of individual job seekers. Furthermore, we observe that patterns of missing features may indicate various types of job seekers. We propose a probabilistic model to cluster users based on missing features and learn the corresponding prediction models for individual clusters. The parameters in this clustering-prediction process are jointly estimated by EM algorithm. We conduct experiments on a real-world testbed by comparing various models and features. The results demonstrate the effectiveness of our proposed personalized approach to user click prediction.

     

利用ε-贪婪学习和用户行为反馈的搜索引擎网页排序算法

为了提高网页排序的准确性,提出一种基于ε-贪婪学习和用户点击行为的网页排序算法。首先,根据用户查询,通过轮盘赌策略向用户推荐相关网页列表;然后,根据用户点击网页的行为进行ε-贪婪学习,计算得到排序系统中的强化信号,通过奖励和惩罚机制为每个网页计算相关性程度值;最后,根据相关性程度对网页进行重新排序。随着用户反馈的信息越来越多,相关网页会排列在列表的最高等级上。实验结果表明,提出的算法能够准确地推荐出相关网页,在P@n、NDCG和MAP性能指标上都获得了较优的性能。     

面向用户互联网访问日志的异常点击分析

随着互联网用户人数的日益增长,用户行为分析已经成为互联网技术领域重要的研究方法之一。在日志中去除异常点击,对于准确挖掘用户行为的意图和习惯十分重要。该文采用某公司提供的真实用户互联网访问日志,对日志中的连续点击,单IP多用户以及单用户多IP等可能的异常点击,从访问集中度,用户平均访问量等方面进行了分析。我们认为对于连续点击,用户行为分析研究人员可以分情况滤去多余点击或该用户所有点击,而对于单IP多用户和单用户多 IP的点击,我们建议不做处理。     

基于用户反馈的搜索引擎排名算法

以Web 2.0中用户行为作为研究对象,通过发掘用户反馈方式,提出用户反馈分值的概念,对用户反馈影响搜索结果排名的具体方法以及相应实现进行研究,提出了一种基于神经网络的网页排序算法。该算法引入BP神经网络模型,根据用户反馈分值选择样本训练神经网络。将传统搜索结果输入到经过训练的神经网络进行计算,根据计算出的结果所表示的网页相关性强弱判断后进行二次排序。该算法利用了神经网络具有的模式识别能力,有效地将用户反馈和搜索引擎结合起来,使得搜索结果更加符合用户的搜索要求。     

基于日志挖掘的搜索引擎用户行为分析

随着网络搜索用户的大规模增加,网络用户行为分析已成为网络信息检索系统进行架构分析、性能优化和系统维护的重要基石,是网络信息检索和知识挖掘的重要研究领域之一。为更好理解网络用户的搜索行为,该文基于7.56亿条真实网络用户行为日志,对用户行为进行分析和研究。我们主要考察了用户搜索行为中的查询长度、查询修改率、相关搜索点击率、首次/最后一次点击位置分布以及查询内点击数分布等信息。该文还基于不同类型的查询集合,考察用户在不同查询需求下的行为差异性。相关分析结果对搜索引擎算法优化和系统改进等都具有一定的参考意义。     

基于用户查询日志的网络搜索主题分析

网络搜索分析在优化搜索引擎方面具有举足轻重的作用,而且对用户个人搜索特性进行分析能够提高搜索引擎的精准度。目前,大多数已有模型（比如点击图模型及其变体）,注重研究用户群体的共同特点。然而,关于如何做到既可以获取用户群体共同特点又可以获取用户个人特点方面的研究却非常少。本文研究了基于个人用户网络搜索分析新问题,即通过研究用户搜索的突发性现象,获取个人用户搜索查询的主题分布情况。提出了两个搜索主题模型,即搜索突发性模型（SBM）和耦合敏感搜索突发性模型（CS-SBM）。SBM假设查询词和URL主题是无关的,CS-SBM假设查询词和URL之间是有主题关联的,得到的主题分布信息存储在偏Dirichlet先验中,采用Beta分布刻画用户搜索的时间特性。实验结果表明,每一个用户的网络搜索轨迹都有多种基于用户的独有特点。同时,在使用大量真实用户查询日志数据情况下,与LDA、DCMLDA、TOT相比,本文提出的模型具有明显的泛化性能优势,并且有效地描绘了用户搜索查询主题在时间上的变化过程。     

基于用户评价的查询串与搜索结果特征权重计算

提出了利用大量用户评价结果来进行特征权重的计算方法,用于解决搜索引擎中查询串与搜索结果的相似度分析。该方法完全利用用户对搜索结果的“潜在评价”来进行。用户对输入查询串所做的点击反映了其内部的关联性,该文提出的方法可获取这种关联性,对该问题建立了数学模型,利用EM算法解决了特征权重的计算。由于模型的函数比较复杂,难于计算其收敛性,因此,使用了模拟退火算法作为EM算法的补充,用于验证算法的收敛性。实验使用百度搜索引擎在竞价广告上进行,提取的测试数据样本为100个广告和144 132个query,获得的数据结果显示,所有特征收敛到全局最优解,抽样部分数据获得检索相似准确率为93.32%,召回率为87.43%。     

基于用户行为分析的本地搜索排序算法优化

随着本地搜索的发展,通用排序算法得出的排序结果已不能完全满足用户的需要,根据本地搜索的特点,可以更好地利用用户的搜索特征。文中提出通过对用户的行为分析,提取用户行为特征值,再运用排序学习的SVM（支持向量机）方法将分析得到的用户行为特征值融入本地搜索算法当中,以此实现对排序算法的优化。融人了用户行为特征后,本地搜索的排序结果平均准确率和前十名文档的相关性都有了一定的提高。实验结果显示,用户行为特征使得排序结果可以更容易、准确地反映用户的兴趣,提升了用户的搜索体验。     

User demographics prediction based on mobile data

Demographics prediction is an important component of user profile modeling. The accurate prediction of users’ demographics can help promote many applications, ranging from web search, personalization to behavior targeting. In this paper, we focus on how to predict users’ demographics, including “gender”, “job type”, “marital status”, “age” and “number of family members”, based on mobile data, such as users’ usage logs, physical activities and environmental contexts. The core idea is to build a supervised learning framework, where each user is represented as a feature vector and users’ demographics are considered as prediction targets. The most important component is to construct features from raw data and then supervised learning models can be applied. We propose a feature construction framework, CFC (contextual feature construction), where each feature is defined as the conditional probability of one user activity under the given contexts. Consequently, besides employing standard supervised learning models, we propose a regularized multi-task learning framework to model different kinds of demographics predictions collectively. We also propose a cost-sensitive classification framework for regression tasks, in order to benefit from the existing dimension reduction methods. Finally, due to the limited training instances, we employ ensemble to avoid overfitting. The experimental results show that the framework achieves classification accuracies on “gender”, “job” and “marital status” as high as 96%, 83% and 86%, respectively, and achieves Root Mean Square Error (RMSE) on “age” and “number of family members” as low as 0.69 and 0.66 respectively, under the leave-one-out evaluation.     

基于点击日志的搜索引擎用户满意度评价研究

针对传统的搜索引擎人工评价方法效率低、成本大的问题,提出一种利用用户点击日志来评价搜索引擎用户满意度的方法。通过分析搜索引擎的用户点击日志,选择网页搜索结果排名、网页点击率、网页平均浏览时间作为用户满意度特征,分别运用多元线性回归分析、多元对数回归分析和BP神经网络方法,建立了基于用户点击日志的搜索引擎用户满意度评价模型。结合具体的实验数据集,通过实验对线性回归模型、对数回归模型和BP神经网络模型的结果进行了比较与分析,验证了模型的有效性。     

基于用户搜索行为的query-doc关联挖掘

query和doc之间的关联关系是搜索引擎期望获取的一类有价值的信息. query和doc间准确的关联分析不仅可以帮助搜索结果排序,也在query和doc之间的桥接中起到重要作用,以实现相关query和doc之间的信息传递,有利于更深入的query理解和doc理解,并在此基础上开展相关应用.本文提出了一种基于用户搜索行为的query和doc关联关系挖掘算法,该方法首先对用户搜索点击日志中的数据进行整理与分析,构建query与doc间的二部图,再通过采用马尔可夫随机游走模型对二部图数据进行建模,挖掘二部图中的点击数据和session数据,最终挖掘出点击日志中用户没有点击到的doc数据,从而预测出query和doc间的隐含关联关系,同时也可以利用该算法得到query和query潜在的关联关系.基于以上理论基础,我们实现了一套完整的日志挖掘系统,通过大量的实验对比,该系统在各方面均取得了优异的表现,其中对检索结果相关性的性能提升可以达到71.23%,这充分表明,本文所提出的理论和算法能够很好地解决query和doc之间的隐含关系挖掘问题,为提高搜索结果的召回率、实现查询推荐和检索结果聚类奠定了良好的前提基础.     

基于Web日志的煤矿企业网站个性化推荐服务研究

为了解决煤矿企业网站用户查找信息难的问题,提出了一种基于Web日志的煤矿企业网站个性化推荐服务模型。该模型应用关联规则对新用户进行页面推荐,应用聚类算法对老用户进行页面推荐;并结合点击网页的次数、网页的浏览时间、雅可系数与最长公共路径系数来度量用户兴趣度的方法,可为用户准确地推荐其感兴趣的页面。测试结果表明,该模型能够有效地对网页资源进行分类并进行个性化推荐。