Search Service Providers Research Articles

Understanding user switch of information seeking: From search engines to generative AI

Generative AI products such as ChatGPT have changed the way people seek and obtain information. Users may gradually switch from search engines to generative AI for information seeking. This may lead to their defection from search services. Based on the push-pull-mooring (PPM) model, this research examined user switch intention from search engines to generative AI. The results indicated that low information-task fit and information overload lead to user dissatisfaction, whereas AI generated content (AIGC) information quality and perceived interactivity affect perceived value. Dissatisfaction, perceived value, and social influence determine user switch intention. The fsQCA identified three configurations leading to switch intention. The results suggest that search service providers need to enhance information-task fit and reduce information overload in order to retain users and achieve a sustainable development.

ANNProof: Building a verifiable and efficient outsourced approximate nearest neighbor search system on blockchain

Data-as-a-service is increasingly prevalent, with outsourced K-approximate nearest neighbors search (K-ANNS) gaining popularity in applications like similar image retrieval and anti-money laundering. However, malicious search service providers and dataset providers in current outsourced query systems cause incorrect user query results. To address this, we propose ANNProof, a novel framework supporting verifiable outsourced K-ANNS on the blockchain. ANNProof utilizes two innovative authenticated data structures (ADS), the Merkle HNSW node tree, and the Merkle vector identifier tree, for efficient K-ANNS query verification. Additionally, we employ the Merkle sharding tree as an ADS optimization technique, reducing the overhead of delivering verifiable queries. We implement the ADS construction protocol based on blockchain smart contracts to ensure tamper-evident datasets and enhance execution efficiency via a contract state consistency checking scheme. Extensive evaluations show that ANNProof reduces VO generation time, result verification time, and VO size by 160, 120, and 28×, respectively, compared to the state-of-the-art systems. Moreover, ADS construction using ANNProof takes at most 2% of the index construction time, resulting in a negligible overhead for implementing verifiable queries. Meanwhile, the sharding optimization accelerates ADS updates by 53×.

Can Positive Online Social Cues Always Reduce User Avoidance of Sponsored Search Results?

Online social cues that utilize user-generated data, such as user reviews and product ratings, have become one of the key factors influencing online user behavior and decisions. Online users who shared their reviews and ratings about a product (or a seller) become an abstract reference group to a focal user interested in the same product. This study focuses on sponsored search results (SSRs), a type of unsolicited information that matches users’ search queries and receives high evaluations from prior consumers. We investigate the effects of positive social cues on alleviating users’ avoidance responses toward an encountered SSR when searching for a product in a C2C e-commerce context. We synthesize the avoidance literature and identify three forms of SSR avoidance, namely, cognitive, behavioral, and affective avoidance. We apply users’ implicit concerns on SSRs to explain users’ avoidance of an encountered SSR. In addition, we extend social influence theory to online settings where abstract reference groups are posited to trigger social influence. We examine how and under what conditions the three forms of SSR avoidance can be reduced by various positive online social cues (i.e., product- and seller-related). We conduct three laboratory experiments. Results attest to users’ implicit concerns on SSRs and their avoidance of SSRs and reveal different effects of various social cues on reducing the three forms of SSR avoidance. This study uncovers the theoretical mechanisms of social influence on reducing SSR avoidance in online settings. It also offers practical implications for online search service providers to help online users’ decision making in their search process.

A Blockchain-Based Framework for Secure Storage and Sharing of Resumes

In response to problems in the centralized storage of personal resumes on third-party recruitment platforms, such as inadequate privacy protection, inability of individuals to accurately authorize downloads, and inability to determine who downloaded the resume and when, this study proposes a blockchain-based framework for secure storage and sharing of resumes. Users can employ an authorized access mechanism to protect their privacy rights. The proposed framework uses smart contracts, interplanetary file system, symmetric encryption, and digital signatures to protect, verify, and share resumes. Encryption keys are split and stored in multiple depositories through secret-sharing technology to improve the security of these keys. Corresponding key escrow incentives are implemented using smart contracts to automatically verify the correctness of keys and encourage the active participation of honest key escrow parties. This framework combines blockchain and searchable symmetric encryption technology to realize multi-keyword search using inverted indexing and Bloom filters and verify search results on the chain. Escrow search service fees are charged through contracts. Only after the search results are verified can the search service provider obtain the search fee, thus ensuring fair and efficient search for encrypted resumes. The framework is decentralized, secure, and tamper-evident, and achieves controlled sharing while protecting personal privacy and information security.

Parallel Test Prioritization

Although regression testing is important to guarantee the software quality in software evolution, it suffers from the widely known cost problem. To address this problem, existing researchers made dedicated efforts on test prioritization, which optimizes the execution order of tests to detect faults earlier; while practitioners in industry leveraged more computing resources to save the time cost of regression testing. By combining these two orthogonal solutions, in this article, we define the problem of parallel test prioritization, which is to conduct test prioritization in the scenario of parallel test execution to reduce the cost of regression testing. Different from traditional sequential test prioritization, parallel test prioritization aims at generating a set of test sequences, each of which is allocated in an individual computing resource and executed in parallel. In particular, we propose eight parallel test prioritization techniques by adapting the existing four sequential test prioritization techniques, by including and excluding testing time in prioritization. To investigate the performance of the eight parallel test prioritization techniques, we conducted an extensive study on 54 open-source projects and a case study on 16 commercial projects from Baidu , a famous search service provider with 600M monthly active users. According to the two studies, parallel test prioritization does improve the efficiency of regression testing, and cost-aware additional parallel test prioritization technique significantly outperforms the other techniques, indicating that this technique is a good choice for practical parallel testing. Besides, we also investigated the influence of two external factors, the number of computing resources and time allowed for parallel testing, and find that more computing resources indeed improve the performance of parallel test prioritization. In addition, we investigated the influence of two more factors, test granularity and coverage criterion, and find that parallel test prioritization can still accelerate regression testing in parallel scenario. Moreover, we investigated the benefit of parallel test prioritization on the regression testing process of continuous integration, considering both the cumulative acceleration performance and the overhead of prioritization techniques, and the results demonstrate the superiority of parallel test prioritization.

Comprehensive Review and Future Research Directions on Dynamic Faceted Search

In modern society, the increasing number of web search operations on various search engines has become ubiquitous due to the significant number of results presented to the users and the incompetent result-ranking mechanism in some domains, such as medical, law, and academia. As a result, the user is overwhelmed with a large number of misranked or uncategorized search results. One of the most promising technologies to reduce the number of results and provide desirable information to the users is dynamic faceted filters. Therefore, this paper extensively reviews related research articles published in IEEE Xplore, Web of Science, and the ACM digital library. As a result, a total of 170 related research papers were considered and organized into five categories. The main contribution of this paper is to provide a detailed analysis of the faceted search’s fundamental attributes, as well as to demonstrate the motivation from the usage, concerns, challenges, and recommendations to enhance the use of the faceted approach among web search service providers.

The Branding Effect of Sponsored Search Advertising: Value-per-Click vs. Value-per-Impression

Advertising efforts mainly aim to build broader brand image and increase sales. In the online context, widely-successful sponsored search advertising (SSA), which predominantly employs performance-based schemes (e.g., pay-per-click), has been largely treated as a direct-selling mechanism. Recent empirical studies, however, have revealed the significant branding effect in SSA. Research investigating the branding effect and its interplay with the well-studied direct response effect is urgently called for. This paper is among the first attempts to formalize both types of advertising effects and the related budget decisions. One key element of our optimal control-based formulation is extending classical advertising response functions to capture the direct response and branding effects. Based on this formulation, we have derived the closed-form optimal budget policy. Both theoretical analyses and computational experiments have been performed. Our analytical results indicate that the advertiser's optimal budget decisions can be substantially different when the branding effect is explicitly considered. We also reveal experimentally that the optimal budgets for the advertisers with varying degrees of emphasis on branding can be substantially different. The reported work has important managerial implications. For online advertisers intending to promote (either exclusively or in combination with direct response) brand awareness and image, our work provides an analytical framework to facilitate their budgeting decisions and provide a closed-form solution. For search service providers such as Google, the reported work points to interesting opportunities in developing new business models and refining sponsored search market designs.

Online Advertisement, Optimization and Stochastic Networks

In this paper, we propose a stochastic model to describe how search service providers charge client companies based on users' queries for the keywords related to these companies' ads by using certain advertisement assignment strategies. We formulate an optimization problem to maximize the long-term average revenue for the service provider under each client's long-term average budget constraint and design an online algorithm which captures the stochastic properties of users' queries and click-through behaviors. We solve the optimization problem by making connections to scheduling problems in wireless networks, queueing theory and stochastic networks. Unlike prior models, we do not assume that the number of query arrivals is known. Due to the stochastic nature of the arrival process considered here, either temporary “free” service, i.e., service above the specified budget (which we call “overdraft”) or under-utilization of the budget (which we call “underdraft”) is unavoidable. We prove that our online algorithm can achieve a revenue that is within <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (ε) of the optimal revenue while ensuring that the overdraft or underdraft is <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (1/ε) , where ε can be arbitrarily small. With a view towards practice, we can show that one can always operate strictly under the budget. In addition, we extend our results to a click-through rate maximization model and also show how our algorithm can be modified to handle non-stationary query arrival processes and clients with short-term contracts. Our algorithm also allows us to quantify the effect of errors in click-through rate estimation on the achieved revenue. We show that we lose at most Δ/1+Δ fraction of the revenue if Δ is the relative error in click-through rate estimation. We also show that in the long run, an expected overdraft level of Ω(log(1/ε)) is unavoidable (a universal lower bound) under any stationary ad assignment algorithm which achieves a long-term average revenue within <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (ε) of the offline optimum.

Search Advertising: Is There a Feedback Effect?

Like many other media services, internet search services provide two-sided platforms that coordinate interactions between media consumers and media advertisers. For some media services, it has been argued that positive feedback between the consumer/audience and advertiser sides of their businesses are a force contributing to high market concentration and for newspapers, yellow pages and magazines, empirical evidence for positive feedback effects has been produced. As with these other media services, the possibility as been raised that positive feedback may contribute to high concentration in search markets. However, whether feedback from the consumer/searcher side of a search service to the advertiser side is positive or negative depends on whether consumers prefer more or fewer paid ads on search results screens and is not a priori obvious. To our knowledge, this paper presents the first formal empirical evidence regarding the directionality of consumer/searcher-advertiser feedback effects for internet search services. Because the consumer/searcher and advertiser sides of a search service may influence each other, the two sides of the market cannot be studied independently if one wants to understand search market dynamics. The study reported in this paper employed a simultaneous equations model to test for a feedback relationship between consumer usage of an Internet search engine’s paid advertisements from local advertisers and local advertisers’ demand for paid search placements, using data for Yahoo’s local search service for metropolitan markets corresponding to broadcast television markets across the United States. Use of metropolitan markets data for search terms employed by local businesses for a single search service was necessary because, while their services are very similar, the different major independent search services in the U.S. (Google, Yahoo and Microsoft at the time our data was collected) differed sufficiently in the types of data and geographic options they offered advertisers that econometrically sound direct comparisons were not feasible. Use of local markets within a single large country as a source of cross-sectional variation was also deemed preferable to constructing an international cross-section because it would have been difficult to adequately control for country-specific differences in culture, internet use, and regulations affecting the larger advertising market. Our three-stage-least squares estimates support the existence at the local level of positive and quite large two-way feedback effects between the consumer and advertiser sides of Yahoo’s local internet search service. We use estimates for the model’s parameters to show how feedback between the consumer and advertiser sides of a search service increases the financial return to resources invested in efforts to increase the number of consumers using the service and how large this effect might be. Promotion through other media and investments to improve search accuracy are examples of such investments. We also discuss the importance of positive two-way feedback as a mechanism that may contribute to the observed pattern of high concentration in national internet search markets. While our understanding of any media service can be improved through further research, search services are still relatively new that we have much more to do before we have a knowledge base sufficient to be fairly confident in our understanding of the economic logic driving competition in search markets and, especially, the extremely high levels of concentration in just about all national search markets. Nevertheless, the impacts of search services on societies, the markets for products advertised on search engines, and other media (through competition for advertisers) is potentially quite large and policymakers and competition authorities have already felt compelled to take actions against dominant search service providers. The empirical findings presented in this paper contribute toward a more complete understanding of search service economics and this in turn should contribute to development of policies that better address the challenges raised by dominant search engines.

RANKING AUCTIONS: A COOPERATIVE APPROACH

In this paper, we deal with situations arising from markets where an Internet search service provider offers a service of listing firms in decreasing order according to what they have bid. We call these ranking auction situations and introduce the corresponding TU-games. The core, as well as the two friendly solutions for the corners of the market, in this class of games can be easily described using a related assignment game. We study the Alexia value and the Shapley value of this type of games. Using these solutions, we show which circumstances in the game are in favor of the provider and which are beneficial to the bidders.

키워드 검색 광고 운영 DB 데이터 분석을 통한 CPM와 CPC방식의 광고효과 연구

최근 키워드 검색 서비스를 제공하는 검색매체들은 CPM 광고방식에서 CPC 광고방식으로 전환하고 있는 실정이다. 그러나 CPC 광고방식이 CPM 광고방식에 비해 어떠한 측면에 서 효과가 있는가에 대한 체계적 연구는 미흡한 편이다. 이에 본 연구는 CPM 광고방식과 CPC 광고방식의 효과를 DB(Database)로부터 실 사례 데이터를 수집하여 두 광고방식 간 차이가 있는지에 대해 비교분석을 시도하였다. CPM 광고방식과 CPC 광고방식의 광고노출 영역은 상이하여 두 방식을 직접적으로 비교하기 어려우므로 분석단위를 동일하게 하기 위해 원본 데이터에 대한 전처리 과정을 수행하였다. 대상 데이터에 대한 분석결과 CPC 광고방식이 CPM 광고방식보다 총 노출 수 대비 클릭률이 약 1.3% 가량 높게 나타났으며, 1광고에 대한 클릭 당 단가가 약 51원 저렴한 것으로 나타났다. 즉 CPC 광고방식이 CPM 광고방식에 비해 광고효과 측면(CTR)과 광고비용 측면(CPC)에서 보다 우수한 것으로 나타났다. 향후 본 연구를 통해 키워드 검색 광고를 이용하고자 하는 광고주 및 마케팅 담당자에게 근거 있는 자료로서 마케팅 의사결정, 전략수립 및 예산편성에 도움이 되길 기대해 본다. Recently Internet keyword search service providers tends to use CPC advertizement method rather than CPM method. However researches how much the CPC method is beneficial to advertisers than CPM method in certain perspectives are insufficient and not performed systematically. So this paper tries to do comparative analysis about the two methods by analyzing the real transactional DB data from an advertizement agency. Due to the difficulties of direct comparison between the two methods because of their different expose positions on the Web and different types of attributes in DB, we did some preprocessing step for the transactional data. From the result of analysis, the click rate of CPC is higher than CPM by 1.3% and the unit cost for the CPC per one click is lower than CPM method by 51 Won. It shows the CPC method is more effective than CPM method for advertizement from the point of advertizement effectiveness (CTR) and advertizement cost (CPC). We hope this research would give useful information to advertisers and marketing managers in making advertizement strategy, marketing decision and budgeting.

Automated functional testing of online search services

SUMMARYSearch services are the main interface through which people discover information on the Internet. A fundamental challenge in testing search services is the lack of oracles. The sheer volume of data on the Internet prohibits testers from verifying the results. Furthermore, it is difficult to objectively assess the ranking quality because different assessors can have very different opinions on the relevance of a Web page to a query. This paper presents a novel method for automatically testing search services without the need of a human oracle. The experimental findings reveal that some commonly used search engines, including Google, Yahoo!, and Live Search, are not as reliable as what most users would expect. For example, they may fail to find pages that exist in their own repositories, or rank pages in a way that is logically inconsistent. Suggestions are made for search service providers to improve their service quality. Copyright © 2010 John Wiley &amp; Sons, Ltd.

Empirical Research on User Acceptance of Mobile Searches

The mobile search, a combination of a web search engine and a mobile communication system, is viewed as the most influential application in the 3G era. Therefore, mobile search service providers are eager to know which factors most influence user acceptance of mobile searches. Based on the characteristics of mobile searches and a review of previous information technology acceptance research, this study integrates the task technology fit model and the unified theory of acceptance and use of technology model to develop a mobile search acceptance model and empirically tests this model. This study finds that, for mobile searches, the performance expectancy, social influence, and perceived cost all significantly influence use intention and the performance expectancy increases with the increasing user's experience and higher task-technology fit degree. The effort expectancy is found to not affect the use intention of mobile searches and the users' gender does not have a significant moderating effect on the use intention. The results are then used to develop suggestions for mobile search providers to promote their application and development.

Internet pharmacy: a web of mistrust?

Internet pharmacy: a web of mistrust?

Stakeholder identifying and positioning (SIP) models: From Google's operation in China to a general case-analysis framework

Through examining online search service provider Google's much-talked about business operation in China over the past several years, this research introduced a series of practical stakeholder identifying and positioning (SIP) models that can be used for various PR case analysis, issue management and decision-making process. Stakeholder, though an important concept that is at the epicenter for understanding and practicing public relations, has nonetheless received insufficient scholarly scrutiny from the public relations’ perspective, and has been so loosely defined that it often leads to confusion rather than clarification. This study categorized all stakeholders into three broad “PR” dimensions, namely, (1) Product and Revenue; (2) Policy and Regulation; and (3) Perception and Reputation. The dynamic interactions and interrelations of these stakeholders were further streamlined and illustrated through several commonly applicable models.

An auction model arising from an Internet search service provider

Most search service providers such as Lycos and Google either produce irrelevant search results or unstructured company listings to the consumers. To overcome these two shortcomings, search service providers such as GoTo.com have developed mechanisms for firms to advertise their services and for consumers to search for the right services. To provide relevant search results, each firm who wishes to advertise at the GoTo site must specify a set of keywords. To develop structured company listings, each firm bids for priority listing in the search results that appear on the GoTo site. Since the search results appear in descending order of bid price, each firm has some control over the order in which the firm appears on the list resulting from the search. In this paper, we present a one-stage game for two firms that captures the advertising mechanism of a search service provider (such as GoTo). This model enables us to examine the firm’s optimal bidding strategy and evaluate the impact of various parameters on the firm’s strategy. Moreover, we analyze the conditions under which all firms would increase their bids at the equilibrium. These conditions could be helpful to the service provider when developing mechanisms to entice firms to submit higher bids.

Portales de internet: concepto, tipología básica y desarrollo

This paper shows the evolution of the first internet search engines, indexes and service providers towards their conversion into portals. Thesecan be grouped into three classifications: general, vertical, and corporate. Some distinguishing characteristics of portals are analyzed as well as the reasons behind their existence in internet. Also discussed are the main elements which favour their success, including: generation of contents, creation of virtual communities, promotion of user loyalty and development of e-commerce.