Recomended for You: The Impact of Profit Incentives on the Relevance of Online Recommendations

Recommender systems are commonly used by Internet firms to improve consumers’ shopping experience and increase firm sales and profits. A large stream of work on recommender design has studied the problem of identifying the most relevant items to recommend to users. In parallel, recent empirical work has started to provide evidence that real-world recommenders contribute to increased sales and profitability for the firms. However, maximizing consumer welfare and firm profit are not the same. Given that recommenders impact sales and profits, a natural question is what is the impact of firm’s profit incentives on recommender design? This paper studies optimal recommender design in a profit-maximizing framework to answer the question and identifies the conditions under which a profit-maximizing recommender recommends the item with highest margins and those under which it recommends the most relevant item. We further elaborate on the social cost of the mismatch between consumer and firm incentives

Attracting Whom? - Managing User-Generated-Content Communities for Monetization

Successful monetization of user-generated-content (UGC) business calls for attracting enough users, and the right users. The defining characteristic of UGC is users are also content contributors. In this study, we analyze the impact of a UGC firm’s quality control decision on user community composition. We model two UGC firms in competition, with one permitting only high quality content while the other not controlling quality. Users differ in their valuations and the content quality they contribute. Through analyzing various equilibrium situations, we find that higher reward value generally benefits the firm without quality control. However, when the intrinsic value of contribution is low, higher reward value may surprisingly drive high valuation users away from that firm. Also somewhat interestingly, we find that higher cost of contribution may benefit the firm that does not control quality. Our work is among the first to study the business impact of quality control of UGC

Voluntary Open-Source - The Effect Appropriability, Externality, and Uncertainty

Over the past decade, many profit-seeking technology firms voluntarily made their proprietary software open-source. In this paper, we study firm’s motivation of such voluntary open-source and its implications. Through analyzing the tradeoff between supply-side externality and demand-side appropriability, we identify the conditions under which firms will voluntarily open source. Our result highlights the importance of supply-side benefit in open-source decisions. We also find that though open-source increases product quality, it may surprisingly reduce social welfare because of over-investment in product quality. The role of loss of quality control is also investigated, where we show that cost concern may limit a firm’s incentive to open source. Finally, different firms’ incentives to open source in competition are contrasted. We find that one firm’s open-source encourages the other to follow, yet both firms could make lower profit in equilibrium. Our work deepens our understanding of this nascent phenomenon and offers advices to practitioners

The Natural Environmental Factors Influencing the Spatial Distribution of Marathon Events：A Case Study from China

Purpose: The purpose of this study was to investigate the influence of natural environmental factors on the spatial distribution of marathon events in China, and to identify the suitable natural environmental factors for the marathon events. Methods: Geographic information system (GIS) spatial analysis tools were used to perform coupling analysis, e.g. overlap, neighborhood, intersection and buffer for terrain, climate, air quality, mountains and water resources with 342 marathon events held in China in 2018. Results: The results indicate that the spatial distribution of marathon events in China is negatively correlated with the elevation of the terrain (plain > hill > plateau > mountain > basin); climate (subtropical monsoon climate > temperate monsoon climate > temperate continental climate > tropical monsoon climate > plateau alpine climate), air quality (level 3 > level 2 > level 4 > level 1). Results indicate that buffer zones can protect water resources: there are 24 items in the buffer zone of river 0.5 km and lake 1 km, 131 items in the buffer zone of river 3 km and lake 5 km, 191 items in the buffer zone of river 5 km and lake 10 km, 298 items in the buffer zone of river 10 km and lake 20 km. Results indicate for mountain range buffer: 13 items in the 20 km buffer and 39 items in the 50 km buffer. Conclusions: Marathon events are more likely to be held on the third rung of China’s topography where a city has a typical landform (plains, basins, hills, or mountain) with good climate and air quality. Meanwhile a city with water and mountain resources for recreational events such as cross-country or obstacle course are essential. The contribution of this study is to systematically and intuitively reflect the influence of natural environment factors on the distribution of marathon events in China, and to provide evidence for the medium and long-term planning of marathon events in China, the selection of venues for different types of marathon events and how to attract participants

The natural environmental factors influencing the spatial distribution of marathon event: A case study from China

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Purpose: The purpose of this study was to investigate the influence of natural environmental factors on the spatial distribution of marathon events in China, and to identify the suitable natural environmental factors for the marathon events. Methods: Geographic information system (GIS) spatial analysis tools were used to perform coupling analysis, e.g. overlap, neighborhood, intersection and buffer for terrain, climate, air quality, mountains and water resources with 342 marathon events held in China in 2018. Results: The results indicate that the spatial distribution of marathon events in China is negatively correlated with the elevation of the terrain (plain \u3e hill \u3e plateau \u3e mountain \u3e basin); climate (subtropical monsoon climate \u3e temperate monsoon climate \u3e temperate continental climate \u3e tropical monsoon climate \u3e plateau alpine climate), air quality (level 3 \u3e level 2 \u3e level 4 \u3e level 1). Results indicate that buffer zones can protect water resources: there are 24 items in the buffer zone of river 0.5 km and lake 1 km, 131 items in the buffer zone of river 3 km and lake 5 km, 191 items in the buffer zone of river 5 km and lake 10 km, 298 items in the buffer zone of river 10 km and lake 20 km. Results indicate for mountain range buffer: 13 items in the 20 km buffer and 39 items in the 50 km buffer. Conclusions: Marathon events are more likely to be held on the third rung of China’s topography where a city has a typical landform (plains, basins, hills, or mountain) with good climate and air quality. Meanwhile a city with water and mountain resources for recreational events such as cross-country or obstacle course are essential. The contribution of this study is to systematically and intuitively reflect the influence of natural environment factors on the distribution of marathon events in China, and to provide evidence for the medium and long-term planning of marathon events in China, the selection of venues for different types of marathon events and how to attract participants

Crumbs of the cookie: user profiling in customer-base analysis and behavioral targeting

User profile is a summary of a consumer’s interests and preferences revealed through the consumer’s online activity. It is a fundamental component of numerous applications in digital marketing. McKinsey & Company view online user profiling as one of the promising opportunities companies should take advantage of to unlock “big data’s” potential. This paper proposes a modeling approach that uncovers individual user profiles from online surfing data and allows online businesses to make profile predictions when limited information is available. The approach is easily parallelized and scales well for processing massive records of user online activity. We demonstrate application of our approach to customer-base analysis and display advertising. Our empirical analysis uncovers easy-to-interpret behavior profiles and describes the distribution of such profiles. Furthermore, it reveals that even for information-rich online firms profile inference that is based solely on their internal data may produce biased results. We find that although search engines cover smaller portions of consumer Web visits than major advertising networks, their data is of higher quality. Thus, even with the smaller information set, search engines can effectively recover consumer behavioral profiles. We also show that temporal limitations imposed on individual-level tracking abilities are likely to have a differential impact across major online businesses, and that our approach is particularly effective for temporally limited data. Using economic simulation we demonstrate potential gains the proposed model may offer a firm if used in individual-level targeting of display ads

Statistical Analysis and Anomaly Detection of SMS Social Networks

Social network analysis has attracted intensive interests by researchers from multiple disciplines. However most of the existing work is descriptive nature, and statistical network analysis remains an active area of research. In this paper, we model and study two facets of the social networks in short message services (SMS). One is the structure of the contact networks of mobile users, the other is users\u27 messaging behavior pattern. We want to account for the heterogeneity in behavior so that to identify abusive usage such as spamming through the study. We use power-law mixture model to capture community formation behaviors, the first facet, and use Poisson-panel mixture models to uncover abnormal behaviors in text messaging. Our results show heterogeneity of the consumers\u27 sending behavior, also there are two major types of community formation behavior in SMS network