Keyword length and matching options as indicators of search intent in sponsored search

Sponsored search is an online advertising channel that has gained momentum worldwide. The key challenge is deciding on the types of keywords to bid on and matching options to utilise for the keywords. In this paper, we address this problem by providing a broad analysis on how the various traffic search metrics (length, CTR, average cost per click (CPC), average position, and quality score) influence the bidding results as the keyword matching option becomes broader, that is, from exact, to phrase to broad. Drawing on the shopping goals theory, we also establish the profile of the metrics associated with a more focused search intent across the matching options. Using a random sample of keywords selected from 9 640 keywords that online advertisers have bid on, spanning a variety of markets, the results indicate that as the matching option becomes narrower, that is, from broad to exact, the keyword traffic metrics increase in general, except for cost, which does not differ significantly across the matching options. Longer keywords, which are typically associated with a more focussed search intent, generate more clicks and have a higher quality measure on average across all matching options. The longer keywords are cheaper for the exact match option, but more expensive for the other matching options. The results are inconclusive with regard to the position that longer keywords occupy on a results page across the matching options. Thus, the narrower matching options and longer keywords matching those that a customer would typically use to search for a company's goods and services need to be targeted to ensure higher visits to a company's website.     

The impact of webpage content characteristics on webpage visibility in search engine results (Part I)

Content characteristics of a webpage include factors such as keyword position in a webpage, keyword duplication, layout, and their combination. These factors may impact webpage visibility in a search engine. Four hypotheses are presented relating to the impact of selected content characteristics on webpage visibility in search engine results lists. Webpage visibility can be improved by increasing the frequency of keywords in the title, in the full-text and in both the title and full-text.     

Keyword decisions in sponsored search advertising: A literature review and research agenda

In sponsored search advertising (SSA), keywords serve as the basic unit of business model, linking three stakeholders: consumers, advertisers and search engines. This paper presents an overarching framework for keyword decisions that highlights the touchpoints in search advertising management, including four levels of keyword decisions, i.e., domain-specific keyword pool generation, keyword targeting, keyword assignment and grouping, and keyword adjustment. Using this framework, we review the state-of-the-art research literature on keyword decisions with respect to techniques, input features and evaluation metrics. Finally, we discuss evolving issues and identify potential gaps that exist in the literature and outline novel research perspectives for future exploration.     

Impact of response latency on sponsored search

Recent research in the human computer interaction and information retrieval areas has revealed that search response latency exhibits a clear impact on the user behavior in web search. Such impact is reflected both in users’ subjective perception of the usability of a search engine and in their interaction with the search engine in terms of the number of search results they engage with. However, a similar impact analysis has been missing so far in the context of sponsored search. Since the predominant business model for commercial search engines is advertising via sponsored search results (i.e., search advertisements), understanding how response latency influences the user interaction with the advertisements displayed on the search engine result pages is crucial to increase the revenue of a commercial search engine. To this end, we conduct a large-scale analysis using query logs obtained from a commercial web search. We analyze the short-term and long-term impact of search response latency on the querying and clicking behaviors of users using desktop and mobile devices to access the search engine, as well as the corresponding impact on the revenue of the search engine. This analysis demonstrates the importance of serving sponsored search results with low latency and provides insight into the ad serving policy of commercial search engines to ensure long-term user engagement and search revenue.     

基于时间序列聚类的科研成果关键词分析方法

   鉴于传统方法对科研成果关键词研究存在较强主观影响和较少考虑时间因素等问题,提出基于时间序列聚类的科研成果关键词分析方法。该方法通过统计分析方法验证关键词出现顺序在一定程度上反映了关键词反映主题思想的重要性,将关键词的重要度转化为时间序列数据,分别从重要度的数值和趋势两个角度出发,使用动态时间弯曲方法度量关键词重要度时间序列数据之间的相似性,结合近邻传播方法对关键词时间序列数据之间的相似性矩阵进行聚类分析,实现科研成果的关键词分析研究。通过对某科研管理类重要期刊2008—2017年期间刊发的科研成果论文关键词研究发现：新方法不仅可以对科研成果中关键词的关注热度和趋势进行聚类划分,自适应地找到中心关键词作为相应类别的特征代表对象,还能为科研成果关键词的主题分析提供理论方法和决策支持。     

Using lexical chains for keyword extraction

Keywords can be considered as condensed versions of documents and short forms of their summaries. In this paper, the problem of automatic extraction of keywords from documents is treated as a supervised learning task. A lexical chain holds a set of semantically related words of a text and it can be said that a lexical chain represents the semantic content of a portion of the text. Although lexical chains have been extensively used in text summarization, their usage for keyword extraction problem has not been fully investigated. In this paper, a keyword extraction technique that uses lexical chains is described, and encouraging results are obtained.     

Understanding the topic evolution of scientific literatures like an evolving city: Using Google Word2Vec model and spatial autocorrelation analysis

Topic evolution has been described by many approaches from a macro level to a detail level, by extracting topic dynamics from text in literature and other media types. However, why the evolution happens is less studied. In this paper, we focus on whether and how the keyword semantics can invoke or affect the topic evolution. We assume that the semantic relatedness among the keywords can affect topic popularity during literature surveying and citing process, thus invoking evolution. However, the assumption is needed to be confirmed in an approach that fully considers the semantic interactions among topics. Traditional topic evolution analyses in scientometric domains cannot provide such support because of using limited semantic meanings. To address this problem, we apply the Google Word2Vec, a deep learning language model, to enhance the keywords with more complete semantic information. We further develop the semantic space as an urban geographic space. We analyze the topic evolution geographically using the measures of spatial autocorrelation, as if keywords are the changing lands in an evolving city. The keyword citations (keyword citation counts one when the paper containing this keyword obtains a citation) are used as an indicator of keyword popularity. Using the bibliographical datasets of the geographical natural hazard field, experimental results demonstrate that in some local areas, the popularity of keywords is affecting that of the surrounding keywords. However, there are no significant impacts on the evolution of all keywords. The spatial autocorrelation analysis identifies the interaction patterns (including High-High leading, High-Low suppressing) among the keywords in local areas. This approach can be regarded as an analyzing framework borrowed from geospatial modeling. Moreover, the prediction results in local areas are demonstrated to be more accurate if considering the spatial autocorrelations.     

Evaluating the performance of demographic targeting using gender in sponsored search

In this research, we evaluate the effect of gender targeted advertising on the performance of sponsored search advertising. We analyze nearly 7,000,000 records spanning 33 consecutive months of a keyword advertising campaign from a major US retailer. In order to determine the effect of demographic targeting, we classify the campaign’s key phrases by a probability of being targeted for a specific gender, and we then compare the key performance indicators among these groupings using the critical sponsored search metrics of impressions, clicks, cost-per-click, sales revenue, orders, and items, and return on advertising. Findings from our research show that the gender-orientation of the key phrase is a significant determinant in predicting behaviors and performance, with statistically different consumer behaviors for all attributes as the probability of a male or female keyword phrase changes. However, gender neutral phrases perform the best overall, generating 20 times the return of advertising than any gender targeted category. Insight from this research could result in sponsored advertising efforts being more effectively targeted to searchers and potential consumers.     

低价中标的发展情况的分析——以赣州市为例

在当今中国的市场经济中,招投标活动已然成为一种很普遍的竞争方式,并且逐步走向完善。从2000年1月1日我国实施第一部《中华人民共和国招标投标法》以来,建筑市场的招投标活动正在沿着公开、公正、公平的方向健康发展,然而低价中标作为国际通行的评标原则,经历了许多不同的阶段。本文采用对比的方法对赣州市的低价评标方法的发展情况进行分析,展望赣州市评标方法的未来。     

利用Google进行专题信息检索

Google是当今一个具有强大功能和独到特点的优秀搜索引擎，本文研究Google基本检索和高级检索语法规则;探讨利用Google的语法规则增强Google的关键词检索功能、提高查准率，正确构建检索式，实施专题信息检索的策略。     

Practical and effective IR-style keyword search over semantic web

This paper presents a novel IR-style keyword search model for semantic web data retrieval, distinguished from current retrieval methods. In this model, an answer to a keyword query is a connected subgraph that contains all the query keywords. In addition, the answer is minimal because any proper subgraph can not be an answer to the query. We provide an approximation algorithm to retrieve these answers efficiently. A special ranking strategy is also proposed so that answers can be appropriately ordered. The experimental results over real datasets show that our model outperforms existing possible solutions with respect to effectiveness and efficiency.     

搜索引擎优化(SEO)技术探讨

分析了搜索引擎的算法和排名原理,对网站结构、关键字、单页的优化,搜索引擎的惩罚、被搜索引擎惩罚后的挽救办法等技术进行了论述。     

标签与关键词相似度对比分析

本文从人工和搜索引擎两个角度来评判标签和关键词的相似性,并尝试给出了优化搜索引擎的方案.     

科技查新检索中的关键词选择

科技查新检索不同于一般文献检索,查新检索者要针对课题的查新点检索一系列的文献数据库,并对以此出具的查新报告的客观性、公正性承担法律责任。在浩如烟海的国内外文献中针对特定检索课题力求做到查准、查全并非易事,单就检索过程中的关键词选择就往往令人困惑!科技文献中普遍存在的一物多名（或同义词）是查新检索中的永恒难题。为了尽量减少漏检,查新者应对检索用关键词进行扩展,并以适当的检索策略覆盖关键词表达的盲区,必要时还应采用分类法检索作为主题法检索的补充,通过多种途径设法化解一物多名给科技文献资源共享带来的难题!科技查新本身是一种创造性的劳动。查新者应充满自信地敞开思路,用创造性的思维,深入自如地施展文献检索才艺。     

An efficient substring search method by using delayed keyword extraction

In the information retrieval systems, one of the most important and difficult operations is to extract appropriate keywords from documents. This paper proposes an effective substring search method by extending a pattern matching machine for multi-keyword based on Aho and Corasick (AC) called AC machine. The proposed method enables us to extract keyword candidates as much as possible and to select the suitable keywords for users' purpose at a retrieval stage. This method contains four types of substring search methods (exact, prefix, suffix and proper substring search). This paper also proposes a construction algorithm of the retrieval structure for speeding up the substring search. From the simulation results, it is shown that the retrieval time of the presented method is as fast as the key retrieval method based on the trie.     

国际力学论文关键词的共现分析

本文运用科学计量学方法对近20万条力学SCI专业期刊论文数据进行统计和分析,得到了前20个高频关键词的共现矩阵。通过对前20个高频关键词的共词分析,利用Jaccard指数考察了前20个高频关键词之间的关系,并用知识图谱的方法映射出固体力学和流体力学研究内容之间的复杂网络关系。     

自动问答系统设计与实现

自动问答系统在搜索引擎的基础上融入了自然语言的知识与应用,与传统的依靠关键字匹配的搜索引擎相比,能够更好地满足用户的检索需求。介绍了计算机操作系统自动问答系统模型,阐述了具体开发过程,设计并实现了基于计算机操作系统领域的自动问答系统,实践表明该系统能够较为准确地回答用户问题。     

Search tactics used in solving everyday how-to technical tasks: Repertoire,selection and tenacity

With greater access to computational resources, people use search to address many everyday challenges in their lives, including solving technology problems. Although there are now many useful ‘how-to’ resources online (especially videos on YouTube), it can still be difficult to identify, understand, and resolve certain kinds of technical problem. While research tasks have been studied for many years and we know the tactics people use, we know far less about searchers’ tactics for how-to technical tasks that involve actually being able to apply found information to resolve a problem. Crucial to our study was developing and studying a highly realistic, how-to technical task, for which there was no single guidance resource: making a phone safe for a child. After providing 39 participants with an actual phone to fix, and a search engine to perform the task, we analysed their search tactics using retrospective cued think aloud interviews. Our primary contribution is a set of 77 tactics used, in three categories, along with detail of how common they were. We conclude that people had a lot of tactics in their repertoire. Although it was not hard for participants to find relevant information, what was hard was for participants to find information they could use; indeed only 23% of participants successfully completed the entire task. Domain knowledge affected the choice of tactics used (although not necessarily towards better task success). We discuss these influences and make design recommendations for how future search systems can support those in resolving how-to technical tasks.     

A distance measure for automatic document classification by sequential analysis

This research has investigated the feasibility of using a distance measure, called the Bayesian distance, for automatic sequential document classification. It has been shown that by observing the variation of this distance measure as keywords are extracted sequentially from a document, the occurrence of noisy keywords may be detected. This property of the distance measure has been utilized to design a sequential classification algorithm which works in two phases. In the first phase keywords extracted from a document are partitioned into two groups—the good keyword group and the noisy keyword group. In the second phase these two groups of keywords are analyzed separately to assign primary and secondary classes to a document. The algorithm has been applied to several data bases of documents and very encouraging results have been obtained.     

面向用户评论的主题挖掘研究——以美团为例

【目的/意义】从海量自助餐用户评论数据中抽取有效关键词构建主题和主题词,协助商家了解用户口碑, 进而更好的改善餐饮行业的管理水平。【方法/过程】通过融合TF-IDF、TextRank和LMKE三种不同的关键词抽取 方法获取最优关键词,再对抽取的关键词进行语义聚类、主题识别、主题词挖掘和主题权重计算,最后在采集的美 团数据集上进行验证方法的有效性。【结果/结论】实验结果表明,三种关键词抽取方法的融合比单个关键词算法效 要好,文本评论聚类后的主题分别是：味道、菜品、环境、服务、价格,主题的重要程度依次是：味道 36.2%、服务 22.9%、价格15.1%、环境13.6%、菜品12.2%。实验结果证实,通过该方法能够有效识别和构建主题及主题词,并计算 出用户对于不同主题关注的重点内容,同时为餐饮行业主题及主题词挖掘和应用研究提供了一定的理论和技术基 础。【创新/局限】提出一种半监督语义聚类的主题识别、主题词构建和主题权重评估方法;不足之处在于本次实验 仅以武汉地区的美食自助餐评论为主,其构建的主题适用性范围有限。