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1.
《Information processing & management》2023,60(1):103142
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. 相似文献
2.
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. 相似文献
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《Information processing & management》2022,59(3):102913
Abstractive summarization aims to generate a concise summary covering salient content from single or multiple text documents. Many recent abstractive summarization methods are built on the transformer model to capture long-range dependencies in the input text and achieve parallelization. In the transformer encoder, calculating attention weights is a crucial step for encoding input documents. Input documents usually contain some key phrases conveying salient information, and it is important to encode these phrases completely. However, existing transformer-based summarization works did not consider key phrases in input when determining attention weights. Consequently, some of the tokens within key phrases only receive small attention weights, which is not conducive to encoding the semantic information of input documents. In this paper, we introduce some prior knowledge of key phrases into the transformer-based summarization model and guide the model to encode key phrases. For the contextual representation of each token in the key phrase, we assume the tokens within the same key phrase make larger contributions compared with other tokens in the input sequence. Based on this assumption, we propose the Key Phrase Aware Transformer (KPAT), a model with the highlighting mechanism in the encoder to assign greater attention weights for tokens within key phrases. Specifically, we first extract key phrases from the input document and score the phrases’ importance. Then we build the block diagonal highlighting matrix to indicate these phrases’ importance scores and positions. To combine self-attention weights with key phrases’ importance scores, we design two structures of highlighting attention for each head and the multi-head highlighting attention. Experimental results on two datasets (Multi-News and PubMed) from different summarization tasks and domains show that our KPAT model significantly outperforms advanced summarization baselines. We conduct more experiments to analyze the impact of each part of our model on the summarization performance and verify the effectiveness of our proposed highlighting mechanism. 相似文献
4.
A.T. Arampatzis T. Tsoris C.H.A. Koster Th.P. van der Weide 《Information processing & management》1998,34(6):693-707
In this article we describe a retrieval schema which goes beyond the classical information retrieval keyword hypothesis and takes into account also linguistic variation. Guided by the failures and successes of other state-of-the-art approaches, as well as our own experience with the Irena system, our approach is based on phrases and incorporates linguistic resources and processors. In this respect, we introduce the phrase retrieval hypothesis to replace the keyword retrieval hypothesis. We suggest a representation of phrases suitable for indexing, and an architecture for such a retrieval system. Syntactical normalization is introduced to improve retrieval effectiveness. Morphological and lexico-semantical normalizations are adjusted to fit in this model. 相似文献
5.
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. 相似文献
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This work aims to extract possible causal relations that exist between noun phrases. Some causal relations are manifested by lexical patterns like causal verbs and their sub-categorization. We use lexical patterns as a filter to find causality candidates and we transfer the causality extraction problem to the binary classification. To solve the problem, we introduce probabilities for word pair and concept pair that could be part of causal noun phrase pairs. We also use the cue phrase probability that could be a causality pattern. These probabilities are learned from the raw corpus in an unsupervised manner. With this probabilistic model, we increase both precision and recall. Our causality extraction shows an F-score of 77.37%, which is an improvement of 21.14 percentage points over the baseline model. The long distance causal relation is extracted with the binary tree-styled cue phrase. We propose an incremental cue phrase learning method based on the cue phrase confidence score that was measured after each causal classifier learning step. A better recall of 15.37 percentage points is acquired after the cue phrase learning. 相似文献
9.
Bernard J. Jansen Zhe Liu Courtney Weaver Gerry Campbell Matthew Gregg 《Information processing & management》2011
Real time search is an increasingly important area of information seeking on the Web. In this research, we analyze 1,005,296 user interactions with a real time search engine over a 190 day period. Using query log analysis, we investigate searching behavior, categorize search topics, and measure the economic value of this real time search stream. We examine aggregate usage of the search engine, including number of users, queries, and terms. We then classify queries into subject categories using the Google Directory topical hierarchy. We next estimate the economic value of the real time search traffic using the Google AdWords keyword advertising platform. Results shows that 30% of the queries were unique (used only once in the entire dataset), which is low compared to traditional Web searching. Also, 60% of the search traffic comes from the search engine’s application program interface, indicating that real time search is heavily leveraged by other applications. There are many repeated queries over time via these application program interfaces, perhaps indicating both long term interest in a topic and the polling nature of real time queries. Concerning search topics, the most used terms dealt with technology, entertainment, and politics, reflecting both the temporal nature of the queries and, perhaps, an early adopter user-based. However, 36% of the queries indicate some geographical affinity, pointing to a location-based aspect to real time search. In terms of economic value, we calculate this real time search stream to be worth approximately US $33,000,000 (US $33 M) on the online advertising market at the time of the study. We discuss the implications for search engines and content providers as real time content increasingly enters the main stream as an information source. 相似文献
10.
理论分析表明,广告支出、研发支出与企业绩效正相关,并且广告和研发支出的作用会受到企业规模和控股股东的影响。基于中国上市公司样本的实证研究发现,研发支出与企业绩效显著正相关,而广告支出与企业绩效没有统计上显著的关系。就规模而言,研发支出与企业绩效之间显著的正相关关系仅存在于小企业中;在大企业里,广告支出与企业绩效显著正相关。就控股股东而言,在非控股企业里,研发支出与企业绩效显著正相关;在控股企业里,广告支出与企业绩效显著正相关。稳健性检验进一步证实了这些结论。 相似文献
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[目的/意义]查询式搜索适用于目标明确的提问应答式信息问题，探索式搜索更注重搜索过程的人机交互性、动态性与多面性，两者表现出不同的行为特征。作为搜索行为研究的基本问题之一，相关研究还比较缺乏。论文旨在探究查询式搜索与探索式搜索行为特征的差异，这对于信息搜索系统的功能优化以及指导用户高效获取信息都具有重要的实践意义。[方法/过程]论文以健康信息搜索为例，采用搜索行为实验的方法，通过对录屏数据的分析，从检索策略、学习行为、深度搜索和搜索绩效4个维度对两种搜索行为进行比较。[结果/结论]查询式搜索与探索式搜索在关键词变换数、访问网页数目等6个指标上存在显著性差异，在检索工具选择、查询串长度、搜索结果集的翻页和相关链接搜索4个指标上不存在显著性差异。 相似文献
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在激烈的市场竞争中,品牌资产成为企业的重要竞争手段之一,广告以及研发这两种差异化战略都会对品牌资产产生重要影响。新创企业在成立之初往往面临着"新进入缺陷",资源的匮乏使得它们很难同时采用多种差异化战略来发展品牌资产。为了探明广告投入和RD投入对新创企业品牌资产的不同影响效果和作用,本文基于1621家私营创业企业的大型调查问卷数据,运用二元logistic回归方法,分析了新创企业广告投入和RD投入对其品牌资产的影响,并针对新创企业发展的不同阶段进行了分别验证。结果表明:广告和RD投入对于新创企业的品牌资产都有着正向的影响作用,且在新创企业的初创期,RD对于品牌资产的影响作用大于广告投入;与此同时,初创期的新创企业获得融资后,广告失去了对品牌资产的影响作用。 相似文献
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Diana Gavilan Maria AvelloAuthor VitaeCarmen AbrilAuthor Vitae 《International Journal of Information Management》2014
The future of communication and advertising is mobile. There is, however, little empirical evidence as to exactly how mobile advertising works. This research examines the role of mental imagery elicited by mobile advertising and its mediating effect on trust in the advertising message (ad trust) and purchase intention. Using a 2 × 2 factorial experimental design, we examined the influence of the type of message – SMS vs. MMS – and type of content – informational vs. transformational – on the three dimensions of mental imagery: vividness, quantity and elaboration. Results show a greater impact of MMS mobile ads and transformational ads on vividness and elaboration, while SMS mobile ads have a greater impact on the quantity dimension. The study also suggests that ad trust can be improved by enhancing the mental imagery elicited by mobile ads. Vivid and elaborate mental imagery mediates the effect of the type of ad on ad trust and exerts a positive influence on purchase intention. These findings have important implications for mobile advertisers since overcoming trust issues remains a major obstacle in the adoption of this medium. In addition, practical aspects of mobile ads’ conception and design are discussed and directions for future research are suggested. 相似文献
14.
In sponsored search, many advertisers have not achieved their expected performances while the search engine also has a large room to improve their revenue. Specifically, due to the improper keyword bidding, many advertisers cannot survive the competitive ad auctions to get their desired ad impressions; meanwhile, a significant portion of search queries have no ads displayed in their search result pages, even if many of them have commercial values. We propose recommending a group of relevant yet less-competitive keywords to an advertiser. Hence, the advertiser can get the chance to win some (originally empty) ad slots and accumulate a number of impressions. At the same time, the revenue of the search engine can also be boosted since many empty ad shots are filled. Mathematically, we model the problem as a mixed integer programming problem, which maximizes the advertiser revenue and the relevance of the recommended keywords, while minimizing the keyword competitiveness, subject to the bid and budget constraints. By solving the problem, we can offer an optimal group of keywords and their optimal bid prices to an advertiser. Simulation results have shown the proposed method is highly effective in increasing ad impressions, expected clicks, advertiser revenue, and search engine revenue. 相似文献
15.
处于困境中的中国高等广告教育需要改革。而改革的唯一方向是实行广告实战教育。它要求高等广告教育必须在内容方面进行革新。广告专业学习地图模型就是内容革新的重要模式,它是将以往相对分散的理论学习、专业实践和心智成长等相关内容有机综合为系统的规范化学习模式,它坚持专业理论知识是基础、专业实战是保障、心智成长是关键的教育思想;强调专业教师引导的重要性、学习方法的技巧性、自主学习的必要性和学生个性成长的必须性。唯有此,才能将学生培养成专业合格、能力出众、人格完善的社会公民。 相似文献
16.
《International Journal of Information Management》2016,36(3):360-373
As social network services become more pervasive, social media advertising emerges as an attractive vehicle for augmenting advertising effectiveness. To leverage this new means of marketing, one must understand what engages SNS users in a favorable online behavior (i.e., overtly indicating personal interest in, or support for, the exposed message by clicking the Like or Share button in Facebook), thereby resulting in an effective advertising campaign. This research conceptualizes SNS ad effectiveness as a concept encompassing emotional appeal, informativeness and creativity that all have a potential to contribute to a positive online behavior. It empirically investigates the antecedents of positive user behavior for a SNS ad based on the theory of reasoned action, the social influence theory, and a persuasion theory. It proposes and tests a conceptual model of the formation of online user’s behavioral responses with regards to SNS advertising. The results of our empirical tests of the model reveal that informativeness and advertising creativity were key drivers of favorable behavioral responses to an SNS ad and that intention to engage in favorable user responses was positively associated with purchase intention. Based on these findings, the paper suggests further research directions and offers implications for harnessing the full potential of the new SNS advertising platform. 相似文献
17.
In this paper, we propose a common phrase index as an efficient index structure to support phrase queries in a very large text database. Our structure is an extension of previous index structures for phrases and achieves better query efficiency with modest extra storage cost. Further improvement in efficiency can be attained by implementing our index according to our observation of the dynamic nature of common word set. In experimental evaluation, a common phrase index using 255 common words has an improvement of about 11% and 62% in query time for the overall and large queries (queries of long phrases) respectively over an auxiliary nextword index. Moreover, it has only about 19% extra storage cost. Compared with an inverted index, our improvement is about 72% and 87% for the overall and large queries respectively. We also propose to implement a common phrase index with dynamic update feature. Our experiments show that more improvement in time efficiency can be achieved. 相似文献
18.
Chengsen Ru Jintao Tang Shasha Li Songxian Xie Ting Wang 《Information processing & management》2018,54(4):593-608
Distant supervision (DS) has the advantage of automatically generating large amounts of labelled training data and has been widely used for relation extraction. However, there are usually many wrong labels in the automatically labelled data in distant supervision (Riedel, Yao, & McCallum, 2010). This paper presents a novel method to reduce the wrong labels. The proposed method uses the semantic Jaccard with word embedding to measure the semantic similarity between the relation phrase in the knowledge base and the dependency phrases between two entities in a sentence to filter the wrong labels. In the process of reducing wrong labels, the semantic Jaccard algorithm selects a core dependency phrase to represent the candidate relation in a sentence, which can capture features for relation classification and avoid the negative impact from irrelevant term sequences that previous neural network models of relation extraction often suffer. In the process of relation classification, the core dependency phrases are also used as the input of a convolutional neural network (CNN) for relation classification. The experimental results show that compared with the methods using original DS data, the methods using filtered DS data performed much better in relation extraction. It indicates that the semantic similarity based method is effective in reducing wrong labels. The relation extraction performance of the CNN model using the core dependency phrases as input is the best of all, which indicates that using the core dependency phrases as input of CNN is enough to capture the features for relation classification and could avoid negative impact from irrelevant terms. 相似文献
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Both general and domain-specific search engines have adopted query suggestion techniques to help users formulate effective queries. In the specific domain of literature search (e.g., finding academic papers), the initial queries are usually based on a draft paper or abstract, rather than short lists of keywords. In this paper, we investigate phrasal-concept query suggestions for literature search. These suggestions explicitly specify important phrasal concepts related to an initial detailed query. The merits of phrasal-concept query suggestions for this domain are their readability and retrieval effectiveness: (1) phrasal concepts are natural for academic authors because of their frequent use of terminology and subject-specific phrases and (2) academic papers describe their key ideas via these subject-specific phrases, and thus phrasal concepts can be used effectively to find those papers. We propose a novel phrasal-concept query suggestion technique that generates queries by identifying key phrasal-concepts from pseudo-labeled documents and combines them with related phrases. Our proposed technique is evaluated in terms of both user preference and retrieval effectiveness. We conduct user experiments to verify a preference for our approach, in comparison to baseline query suggestion methods, and demonstrate the effectiveness of the technique with retrieval experiments. 相似文献