Endangered but not too late : the state of digital news preservation

"What we found in this research is that news organizations are saving digital news content to at least a limited extent, one that often depends on the kind of technologies where news content resides, their purpose, and other key factors. We found that the degree to which your existing content is accessible and useful depends not only on the technologies used, but also on your policies, if any, about what is saved. Other factors that affect access to content include the workflows used to assemble and store content, the metadata that’s saved with your content--or missing depending on how it is managed--whether or not you have staff dedicated to preservation work, and how well content translates when you undergo a transition from one technology platform to another, an inevitable fact of life in today’s publishing industry. News organizations that use either an archive or digital asset management (DAM) system of some kind have the most control of the content used to post, publish, broadcast or stream the news. They are also in the best position to find and access past content, understand its origins and licensing rights, reuse it for new products, tap it for newsroom research, publish it in related content links, and take full advantage of the long-tail phenomenon by reselling to the public or research community. ..."--Findings summar

Research on power-law distribution of long-tail data and its application to tourism recommendation

PurposeOne 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/approachUsing 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.FindingsThe 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/valueTRSs 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.

The long tail thesis

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.

Network Analysis of Open Innovation

The way people innovate and create new ideas and bring them to the market is undergoing a fundamental change from closed innovation to open innovation. Why and how do firms perform open innovation? Firms’ open innovation is measured through the levels of firms’ joint patent applications. Next, we analyze network structures and characters of firms’ joint patent applications such as betweenness and degree centrality, structure hole, and closure. From this research, we drew conclusions as follows. First, the structure of collaboration networks has both direct and indirect effects on firms’ innovative performance. Second, in the process of joint patent applications, there is a long tail phenomenon in networks of joint patent applications. Third, the number of patents and International Patent Classification (IPC) subclasses together constitute a meaningful measure of the innovation performance of firms.

Network Analysis of Open Innovation

The way people innovate and create new ideas and bring them to the market is undergoing a fundamental change from closed innovation to open innovation. Why and how do firms perform open innovation? Firms’ open innovation is measured through the levels of firms’ joint patent applications. Next, we analyze network structures and characters of firms’ joint patent applications such as betweenness and degree centrality, structure hole, and closure. From this research, we drew four conclusions. First, the structure of collaboration networks has both direct and indirect effects on firms’ innovative performance. Second, in the process of joint patent applications, there is a long tail phenomenon in networks of joint patent applications. Third, the number of patents and IPC subclasses together constitute a meaningful measure of the innovation performance of firms.