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Article
Publication date: 5 September 2017

Tami Oliphant and Ali Shiri

The purpose of this paper is to elaborate the long tail of topical search queries, including the influence of current events, posed to a large, urban public library…

Abstract

Purpose

The purpose of this paper is to elaborate the long tail of topical search queries, including the influence of current events, posed to a large, urban public library discovery system.

Design/methodology/approach

Search queries from the months of June, July, August and September 2014 (1,488,339 total queries) were collected from the Edmonton Public Library’s BiblioCommons database using Google Analytics and exported to Excel. The data were then analyzed using descriptive statistics, frequency counts and textual analysis to explicate the long tail of search, (including the most popular searches) and to explore the relationship between topical search queries and current events.

Findings

The findings support the long tail theory, as the aggregate tail of topical search queries comprised the vast majority of the total searches and current events exert some influence on the nature and frequency of topical searches.

Research limitations/implications

Data collection was limited to four months of the year; thus, comparisons across the year cannot be made. There are practical implications for public libraries in terms of marketing and collections, as well as for improving catalogue functionality, to support user search behaviour.

Originality/value

Not much research attention has been focused on the nature of topical search queries in public libraries compared to academic libraries and the Web. The findings contribute to developing insight into the divergent interests of intergenerational public library users and the topics of materials they are searching for.

Details

Library Review, vol. 66 no. 6/7
Type: Research Article
ISSN: 0024-2535

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Article
Publication date: 4 October 2011

Jonathon Day, Liz Ward, Suh‐hee Choi and Chen (Zara) Zhao

The purpose of this paper is to examine the demand curve for information on tourism destinations and accommodation. The current study compares the demand curves for this…

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Abstract

Purpose

The purpose of this paper is to examine the demand curve for information on tourism destinations and accommodation. The current study compares the demand curves for this information to trends described by Chris Anderson as the “long tail”.

Design/methodology/approach

The current study examines the demand for information about accommodation establishments and destinations in Australia through the Australian Tourism Data Warehouse (ATDW). The study examines the demand for information received through the ATDW in 2009 for 5,600 Australian destinations and over 33,200 accommodation listings. Demand for information was measured by page impressions (PIs). Over 10 million PIs were received for destinations and more than 17 million PIs were received for accommodation listings, all of which were examined.

Findings

The current research shows that both accommodation and destination demand curves display the extended demand curve typical of the long tail phenomenon. The analysis also shows that demand curves within the aggregate demand curve also follow “long tail” demand curves. The study contributes to understanding of the demand curve for tourism information for Australian product using the ATDW.

Originality/value

The paper provides analysis of tourism information demand in the context of the “long tail” phenomenon.

Details

Journal of Hospitality and Tourism Technology, vol. 2 no. 3
Type: Research Article
ISSN: 1757-9880

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Article
Publication date: 18 May 2020

Xiang Chen, Yaohui Pan and Bin Luo

One challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the…

Abstract

Purpose

One challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data.

Design/methodology/approach

Using Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs.

Findings

The comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation.

Originality/value

TRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors’ work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors’ work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.

Details

Industrial Management & Data Systems, vol. 121 no. 6
Type: Research Article
ISSN: 0263-5577

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Article
Publication date: 16 May 2016

Steve W. Heim, Mostafa Ajallooeian, Peter Eckert, Massimo Vespignani and Auke Jan Ijspeert

The purpose of this paper is to explore the possible roles of active tails for steady-state legged locomotion, focusing on a design principle which simplifies control by…

Abstract

Purpose

The purpose of this paper is to explore the possible roles of active tails for steady-state legged locomotion, focusing on a design principle which simplifies control by decoupling different control objectives.

Design/methodology/approach

A series of simple models are proposed which capture the dynamics of an idealized running system with an active tail. These models suggest that the overall control problem can be simplified and effectively decoupled via a proper tail design. This design principle is further explored in simulation using trajectory optimization. The results are then validated in hardware using a one degree-of-freedom active tail mounted on the quadruped robot Cheetah-Cub.

Findings

The results of this paper show that an active tail can greatly improve both forward velocity and reduce body-pitch per stride while adding minimal complexity. Further, the results validate the design principle of using long, light tails compared to shorter heavier ones.

Originality/value

This paper builds on previous results, with a new focus on steady-state locomotion and in particular deals directly with stance phase dynamics. A novel design principle for tails is proposed and validated.

Details

Industrial Robot: An International Journal, vol. 43 no. 3
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 4 December 2019

Shuanping Dai and Markus Taube

This paper aims to explore the functionality of long tail markets (LTM), where the consumers cannot be reached or are ignored by the traditional mainstream businesses, in…

Abstract

Purpose

This paper aims to explore the functionality of long tail markets (LTM), where the consumers cannot be reached or are ignored by the traditional mainstream businesses, in new products and business development.

Design/methodology/approach

First, the authors review two Chinese entrepreneurial practices in the Fintech sector and low-speed electric vehicles (LSEV) and describe their stylized facts; second, they explore a possible theoretical LTM framework to underscore these practices; third, they make a connection between LTM and existing business models and analyze its significance and practical implications in business, in particular, in developing economies.

Findings

The LTM business approach has helped Chinese companies in the Fintech sector and LSEVs gain global attention. The success factors of LTM for businesses are identifying a specific customer base, being aware of localization products and playing skillfully with regulations; the LTM approach has several overlaps with existing studies on niche products and base of the pyramid market.

Originality/value

Based on some emerging and attractive business practices in China, this paper offers a valuable attempt to theorize them as long tail phenomenon. The LTM thesis provides a potential framework to reference for similar methods elsewhere and may illuminate entrepreneurship to be explored in similar markets.

Details

Chinese Management Studies, vol. 14 no. 2
Type: Research Article
ISSN: 1750-614X

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Article
Publication date: 14 May 2018

Daifeng Li, Andrew Madden, Chaochun Liu, Ying Ding, Liwei Qian and Enguo Zhou

Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one…

Abstract

Purpose

Internet technology allows millions of people to find high quality medical resources online, with the result that personal healthcare and medical services have become one of the fastest growing markets in China. Data relating to healthcare search behavior may provide insights that could lead to better provision of healthcare services. However, discrepancies often arise between terminologies derived from professional medical domain knowledge and the more colloquial terms that users adopt when searching for information about ailments. This can make it difficult to match healthcare queries with doctors’ keywords in online medical searches. The paper aims to discuss these issues.

Design/methodology/approach

To help address this problem, the authors propose a transfer learning using latent factor graph (TLLFG), which can learn the descriptions of ailments used in internet searches and match them to the most appropriate formal medical keywords.

Findings

Experiments show that the TLLFG outperforms competing algorithms in incorporating both medical domain knowledge and patient-doctor Q&A data from online services into a unified latent layer capable of bridging the gap between lay enquiries and professionally expressed information sources, and make more accurate analysis of online users’ symptom descriptions. The authors conclude with a brief discussion of some of the ways in which the model may support online applications and connect offline medical services.

Practical implications

The authors used an online medical searching application to verify the proposed model. The model can bridge users’ long-tailed description with doctors’ formal medical keywords. Online experiments show that TLLFG can significantly improve the searching experience of both users and medical service providers compared with traditional machine learning methods. The research provides a helpful example of the use of domain knowledge to optimize searching or recommendation experiences.

Originality/value

The authors use transfer learning to map online users’ long-tail queries onto medical domain knowledge, significantly improving the relevance of queries and keywords in a search system reliant on sponsored links.

Details

Industrial Management & Data Systems, vol. 118 no. 4
Type: Research Article
ISSN: 0263-5577

Keywords

Open Access
Article
Publication date: 12 September 2016

Judith Moeller, Damian Trilling, Natali Helberger, Kristina Irion and Claes De Vreese

This paper aims to shed light on the impact of personalized news media on the shared issue agenda that provides democracies with a set of topics that structure the public…

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Abstract

Purpose

This paper aims to shed light on the impact of personalized news media on the shared issue agenda that provides democracies with a set of topics that structure the public debate. The advent of personalized news media that use smart algorithms to tailor the news offer to the user challenges the established way of setting the agenda of such a common core of issues.

Design/methodology/approach

This paper tests the effects of personalized news use on perceived importance of these issues in the common core. In particular, the authors study whether personalized news use leads to a concentration at the top of the issue agenda or to a more diverse issue agenda with a long tail of topics.

Findings

Based on a cross-sectional survey of a representative population sample (n = 1,556), we find that personalized news use does not lead to a small common core in which few topics are discussed extensively, yet there is a relationship between personalized news use and a preference for less discussed topics. This is a result of a specific user profile of personalized news users: younger, more educated news users are more interested in topics at the fringes of the common core and also make more use of personalized news offers.

Research limitations/implications

The results are discussed in the light of media diversity and recent advances in public sphere research.

Originality/value

This paper contributes to the ongoing debate about algorithmic news dissemination. While, currently, much attention is reserved for the role of platforms as information gatekeepers in relationship to the news media, maybe their ability to enable or hinder the audience in discovering and distributing news content is part of what really characterizes their influence on the market place of ideas.

Details

info, vol. 18 no. 6
Type: Research Article
ISSN: 1834-7649

Keywords

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Article
Publication date: 12 January 2018

Lee-Ann Fenge, Kip Jones and Camilla Gibson

Lack of understanding of the needs of older LGBT individuals is a global issue and their needs are often ignored by health and social care providers who adopt…

Abstract

Purpose

Lack of understanding of the needs of older LGBT individuals is a global issue and their needs are often ignored by health and social care providers who adopt sexuality-blind approaches within their provision. As a result, public services can find it difficult to push the LGBT equalities agenda forward due to resistance to change and underlying discrimination. The aim of this paper is to discuss these issues.

Design/methodology/approach

This report considers how a body of participatory research concerning the needs and experiences of older LGBT people was used to create innovatory dissemination tools, which then engaged communities through public engagement to learn about the needs and experiences of older LGBT citizens. Good research has a “long tail” – (in statistics, “a large number of occurrences far from the ‘head’ or central part of the distribution”). The report considers how a film and a method deck of cards, presented to service providers in several workshops over time, offered opportunities to learn and critically reflect upon an informed practice.

Findings

Because of the on-going feedback from our workshops, the authors, in turn, learned the importance of having a champion within a community organisation to take forward the LGBT agenda. A report of one such outreach champion is included here.

Originality/value

Consideration is given to challenges involved in creating impact through research, and how participatory community processes may enhance impact to develop over time.

Details

Qualitative Research Journal, vol. 18 no. 1
Type: Research Article
ISSN: 1443-9883

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Article
Publication date: 2 October 2017

Dilip Kumar and Srinivasan Maheswaran

This paper aims to propose a framework based on the unbiased extreme value volatility estimator (namely, the AddRS estimator) to compute and predict the long position and…

Abstract

Purpose

This paper aims to propose a framework based on the unbiased extreme value volatility estimator (namely, the AddRS estimator) to compute and predict the long position and the short position value-at-risk (VaR) and stressed expected shortfall (ES). The precise prediction of VaR and ES measures has important implications toward financial institutions, fund managers, portfolio managers, regulators and business practitioners.

Design/methodology/approach

The proposed framework is based on the Giot and Laurent (2004) approach and incorporates characteristics like long memory, fat tails and skewness. The authors evaluate its VaR and ES forecasting performance using various backtesting approaches for both long and short positions on four global indices (S&P 500, CAC 40, Indice BOVESPA [IBOVESPA] and S&P CNX Nifty) and compare the results with that of various alternative models.

Findings

The findings indicate that the proposed framework outperforms the alternative models in predicting the long and the short position VaR and stressed ES. The findings also indicate that the VaR forecasts based on the proposed framework provide the least total loss for various long and short position VaR, and this supports the superior properties of the proposed framework in forecasting VaR more accurately.

Originality/value

The study contributes by providing a framework to predict more accurate VaR and stressed ES measures based on the unbiased extreme value volatility estimator.

Details

Studies in Economics and Finance, vol. 34 no. 4
Type: Research Article
ISSN: 1086-7376

Keywords

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Book part
Publication date: 13 December 2017

Qiongwei Ye and Baojun Ma

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and…

Abstract

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

Details

Internet+ and Electronic Business in China: Innovation and Applications
Type: Book
ISBN: 978-1-78743-115-7

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