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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.

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.

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.

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.

The purpose of this paper is to study the influence of investor sentiment on the supply of trade credit, and further explores the difference of the effect of investor sentiment on the supply of trade credit in the environment of strong market competition and weak market competition.

The authors use panel estimation techniques to examine the impact of investor sentiment in the Chinese securities market on the supply of corporate trade credit.

This paper finds that investor sentiment has positive impact on trade credit through three channels of motivation, willingness and ability. At the same time, this paper finds that investor sentiment has stronger impact on enterprises in strong market competition than enterprises in weak market competition.

This paper expands the research on the influence of virtual economy on the real economy, analyzes the difference of the influence of investor sentiment on the supply of trade credit under different market competition conditions.

The paper perfects the mechanism of trade credit decision-making at this stage, and provides more evidence for the virtual economy to act on the real economy.

This paper provides a theoretical basis for the government functional departments to use the investor sentiment to play a positive role in trade credit to improve the market competition and guide the development of China’s capital market in the direction of rationalization and health.

In combination with market competition environment and industry characteristics, this paper investigates external irrational factors and studies how investor sentiment affects trade credit supply.