HOW TO ADVERTISE APPROAPRIATELY ON THE WORLD WIDE WEB? A MULTI-CONGRUITY ANALYSIS APPROACH

As a popular and important advertising style, Internet advertising has drawn substantial amount of scholarly attention. Previous studies focus on the independent effects of various factors, such as product, consumer, website and ad per se, but few studies consider the impacts of the congruities between these factors on consumer’s attitude toward the ads. In this paper, we propose an integrative model, product-consumer-website-ad model, to articulate how the congruity between factors exerts its effect. We propose that ad appeal (emotional vs. informational) should be designed consistent with the nature of the advertised product (hedonic vs. utilitarian), the nature of the website (hedonic vs. utilitarian) and the thinking styles of consumer (intuitive vs. rational). Personalization plays an important role in the process to achieve the congruity. We also propose that the ad on the website with high reputation will generate more favourable attitude toward it. Implications and future research are also discussed in the paper

PEER REVIEWER RECOMMENDATION IN ONLINE SOCIAL LEARNING CONTEXT: INTEGRATING INFORMATION OF LEARNERS AND SUBMISSIONS

With the rapid development of massive open online courses, peer assessment has played an important role in promoting social learning. According to social learning theory, peer assessment in online courses provides students a chance to learn from each other when they review other students’ submissions, which motivates their participation in online social learning. However, existing peer assessment cannot generate satisfactory results in that a systematic approach to find peer reviewers for submissions is lacked. To address this problem, this paper proposes a reviewer recommendation system which can suggest appropriate reviewers for submissions during the process of peer assessment. This recommendation system is built by integrating information of learners and submissions. The integration of this reviewer recommendation system in the process of peer assessment may help to improve learners’ satisfaction and their learning performance. Additionally, the reviewer recommendation system can also be used in many other fields such as enterprise training or language learning online

Stream Aggregation Through Order Sampling

This is paper introduces a new single-pass reservoir weighted-sampling stream aggregation algorithm, Priority-Based Aggregation (PBA). While order sampling is a powerful and e cient method for weighted sampling from a stream of uniquely keyed items, there is no current algorithm that realizes the benefits of order sampling in the context of stream aggregation over non-unique keys. A naive approach to order sample regardless of key then aggregate the results is hopelessly inefficient. In distinction, our proposed algorithm uses a single persistent random variable across the lifetime of each key in the cache, and maintains unbiased estimates of the key aggregates that can be queried at any point in the stream. The basic approach can be supplemented with a Sample and Hold pre-sampling stage with a sampling rate adaptation controlled by PBA. This approach represents a considerable reduction in computational complexity compared with the state of the art in adapting Sample and Hold to operate with a fixed cache size. Concerning statistical properties, we prove that PBA provides unbiased estimates of the true aggregates. We analyze the computational complexity of PBA and its variants, and provide a detailed evaluation of its accuracy on synthetic and trace data. Weighted relative error is reduced by 40% to 65% at sampling rates of 5% to 17%, relative to Adaptive Sample and Hold; there is also substantial improvement for rank queriesComment: 10 page

"Keeping a Safe Confession Distance between Strangers": The Spatial Structure of Social Accessibility in Networked Communities and Risk Avoidance of Stranger Confessions

This study focuses on the spatial structure of stranger social accessibility in networked communities and risk avoidance when socializing with strangers. Using Confession Wall as an example, this study contends that community social media fosters new connections by compensating for accessibility issues between strangers in local communities through flexible mediation of online and offline social interactions. This connectivity minimizes the costs and risks of emotional interactions between strangers in semi-acquaintance communities. This compensation for social accessibility highlights the value of digital media, especially community social media as a geomedia, in energizing communities and facilitating interactions between strangers. This is crucial in understanding the emotional interactions between strangers and how they are mediated by community social media in settings where acquaintances and complete strangers intermingle

Unraveling the Relationship between Co-Authorship and Research Interest

Co-authorship in scientific research is increasing in the past decades. There are lots of researches focusing on the pattern of co-authorship by using social network analysis. However, most of them merely concentrated on the properties of graphs or networks rather than take the contribution of authors to publications and the semantic information of publications into consideration. In this paper, we employ a contribution index to weight word vectors generated from publications so as to represent authors’ research interest, and try to explore the relationship between research interest and co-authorship. Result of curve estimation indicates that research interest couldn’t be employed to predict co-authorship. Therefore, graph-based researcher recommendation needs further examination

Scholar-Friend Recommendation in Online Academic Community

The research project proposes a scholar-friend recommendation approach to help researchers find their scholar-friends by integrating multi-dimensional social networks

Load Balance and Resource Efficiency in Communication Networks

Network management is critical for today’s network. This study investigates both load balancing and resource efficiency in network management. For load balancing, one unfavorable situation is that the active traffic uses a portion of the equal-cost paths instead of all. The root causes of load imbalance are not easily identified and located by network operators. Most research work related in this area concerns the design of load balancing mechanisms or network-wide troubleshooting that does not specify the causes of load imbalance. In this study, we describe a computational framework based on network measurements to identify the correlation mechanism causing the load imbalance. We also describe a novel framework based on Coprime to mitigate the load imbalance brought by hash correlations. In evaluation based on real network trace data and topologies, we have proved that we can reduces the error (CV or K-S statistic) by at least one magnitude. For resource efficiency, today’s network demands an increasing switch memory to support the essential functions, such as forwarding, monitoring, etc. However, the cache memory is restricted when processing data streams in which the input is presented as a sequence of items and can be examined in only a few passes (typically just one). This study introduces a new single-pass reservoir weighted-sampling stream aggregation algorithm, Priority-Based Aggregation (PBA). A naive approach to order sample regardless of key then aggregate the results is hopelessly inefficient. In distinction, our proposed algorithm uses a single persistent random variable across the lifetime of each key in the cache and maintains unbiased estimates of the key aggregates that can be queried at any point in the stream. Concerning statistical properties, we prove that PBA provides unbiased estimates of the true aggregates. We analyze the computational complexity of PBA and its variants and provide a detailed evaluation of its accuracy on synthetic and trace data. In addition to sampling, this study also considers placing classification rules into switches from various network functions. While much work has focused on compressing the rules, most of this work proposes solutions operating in the memory of a single switch. Instead, this study proposed a collaborative approach encompassing switches and network functions. This architecture enables trade-off between usage of (expensive) switch memory and (cheaper) downstream network bandwidth and network function resources. Our system can reduce memory usage significantly compared to strawman approaches as demonstrated with extensive simulations and prototype evaluation with real traffic traces and rules

UNDERSTANDING USERS’ SATISFACTION WITH SOCIAL LEARNING NETWORK

The social learning network (SLN) constructed based on web 2.0 techniques demonstrates some unique characteristics comparing to traditional e-learning context based on 1.0 technique. In response to the new characteristics in SLN, we advance the theoretical understanding of user satisfaction by reconceptualising e-learning as a relational process among students. Based on that, we draw on network externalities and social capital theory to examine users’ satisfaction with social learning network. Considering that network externalities are involved in the process, we propose that two types of network externalities: direct network externality and indirect network externality moderate the relationship between social capital and user satisfaction. Theoretical, practical implications and future research are also discussed

Robust utility maximization with intractable claims

We study a continuous-time expected utility maximization problem in which the investor at maturity receives the value of a contingent claim in addition to the investment payoff from the financial market. The investor knows nothing about the claim other than its probability distribution, hence an ``intractable claim''. In view of the lack of necessary information about the claim, we consider a robust formulation to maximize her utility in the worst scenario. We apply the quantile formulation to solve the problem, expressing the quantile function of the optimal terminal investment income as the solution of certain variational inequalities of ordinary differential equations. In the case of an exponential utility, the problem reduces to a (non-robust) rank--dependent utility maximization with probability distortion whose solution is available in the literature