Yuqing Mao

Online silk road: nurturing social search through knowledge bartering

Social search empowers seekers to help each other find the information they need by sharing their domain knowledge and search efforts. Current social search activities are primarily voluntary, acted on the goodwill to help others or the purpose for self-promotion and the contributed content is mostly retrievable free of charge to the public. However, the voluntary nature of social search compromises its long-term sustainability as participants are not offered intrinsic incentives to contribute and share information, and free information presents intricate ramifications on the its quality. In this paper, we present the idea of knowledge bartering, where one can barter a knowledge item they have for another item they wish to have. To make the idea viable, we propose the online silk road solution to automate a knowledge bartering process that can maximise the social welfare within a community.

From credit and risk to trust: towards a credit flow based trust model for social networks

Trust management is a paramount issue in social networks. Existing models based on global reputation are simplistic as they do not support personalised measures for individual users. Models based on local trust propagation tend to be too subjective to be reliable as they do not consider a social network in its entirety. More importantly, neither model has taken the risk factor into the consideration of trust management. In this paper, we contribute a novel trust model that allows personalised measures to be naturally established on objective grounds through tracing credit flows within a social network, where the trust between a pair of users can be derived from the credit flowing from one into the other and the relative risk disparity between them. This model uses power flows in an electrical grid as a metaphor for the credit flows in a social network and is based on the hypothesis that the credit flows in a social network are similar in nature to the power flows in an electrical grid. Experiments with a real-world dataset have proved the hypothesis and the results have shown that the credit flow based trust model can derive not only personalised but also more accurate trust measures than existing models do.

Web of Credit: Adaptive Personalized Trust Network Inference From Online Rating Data

Trust is a pivotal element of any information system that allows users to share, communicate, interact, or collaborate with one another. Trust inference is particularly crucial for online social networks where interaction with acquaintances or even anonymous strangers is widely a norm. In the past decade, a number of trust inference algorithms have been proposed to address this issue, which are primarily based either on the “reputation” or the “Web of trust (WoT)” model. The reputation-based model supports objective inference of a universal reputation for each user by analyzing the interaction histories among the users; however, it does not allow individual users to specify personalized trust measures for the same other users. In contrast, the WoT-based model allows each individual user to specify a trust value for their direct neighbors within a trust network. However, the accuracy of such a subjective trust value is questionable and further subject to loss in the course of propagating trust measures to nonneighboring users in the network. In this paper, we propose a new trust model referred to as “Web of credit (WoC),” where one gives credit to those others one has interacted with based on the quality of the information one’s peers have provided. Credit flows from one user to another within a trust network, forming trust relationships. This new model combines the objectivism from the reputation-based model for credit assignment by exploiting the actual interaction histories among users in the form of online rating data and the individualism from the WoT-based model for personalized trust measures. We further contribute a WoC-based trust inference algorithm that is adaptive to the change of user profiles by automatically redistributing credit and reinferring trust measures within the network. Experiments with two real-world data sets have shown that the WoC-based trust inference algorithm is not only able to infer more accurate trust measures than both reputation-based and WoT-based algorithms do but also fast enough to be a viable solution for real-time trust inference in large-scale trust networks.

Knowledge Barter-Auctioning: An Incentive for Quality of User-Generated Content in Online Communities

Incentives play a pivotal role in stimulating user-generated content (UGC). Non-financial social incentives are generally effective in boosting the quantity, but have limited effect on the quality. Conversely, financial incentives generally motivate better quality, but often complicate the efforts to attract quantity. We propose knowledge barter-auctioning, a non-financial remunerative mechanism that is particularly effective in stimulating the quality of UGC yet without detriment to its quantity by allowing a vendor to choose the best barter partner in order to maximise their expected revenue.

Stimulating High Quality Social Media through Knowledge Barter-Auctioning

Incentives play a pivotal role in stimulating user-generated content (UGC), which is critical to the viability and success of todays social computing services. Non-financial social incentives are generally effective in boosting the quantity, but have limited effect on the quality. Conversely, financial incentives generally motivate better quality, but often complicate the efforts to attract quantity. In this paper, we propose knowledge barter-auctioning, a non-financial remunerative mechanism that is particularly effective in stimulating the quality of UGC yet without detriment to its quantity. This mechanism provides an optimal way for the knowledge vendor to choose the best barter partner in order to maximise their expected revenue, which is an extrinsic motivation for the triumph of quality as UGC of higher quality will enable the vendor to attract more bidders and consequently make a higher revenue through the barter auction. We have conducted a series of experiments using a real-world dataset to analyse the ramifications of UGC quality in knowledge bartering processes.

A Social-Knowledge-Directed Query Suggestion Approach for Exploratory Search

Existing query suggestion techniques mainly revolve around mining existing queries that are most similar to a given query. If the query fails to precisely capture a users real intent, for example, in most exploratory search tasks, suggested queries are likely to fail as well. If suggested queries are not only relevant to the query but also diverse in nature, it is likely that some of them are close to the users real intent. In this paper, we propose a novel social-knowledge-directed query suggestion approach for exploratory search, which integrates the social knowledge into the probabilistic model based on query-URL bipartite graphs. Social knowledge is discovered by conducting kernel principle component analysis on the related queries, and incorporating the social knowledge with random walk on the bipartite graph can obtain diverse queries that are relevant to a given one. We have conducted a set of experiments to validate this approach and the results show that this approach outperforms other query suggestion methods in terms of supporting exploratory search.

A probabilistic topic model with social tags for query reformulation in informational search

It is non-trivial to formulate a query that can precisely describe the goal of an informational search task. Query reformulation based on the query clustering approach addresses this issue by expanding a new query with related existing queries that were generated by other users. However, the query clustering approach is unable to cluster queries that are intrinsically related but neither contain common terms nor return common clicked Web page URLs. More importantly, it does not address the issue of ranking retrieved results according to their relevance to the search goal. In this paper, we present new query reformulation approach based on a novel probabilistic topic model to discovering the latent semantic relationships between the queries and the URLs. It can not only discover related queries that cannot be clustered by existing query clustering approaches but also rank retrieved results according to the similarities of probability distributions over the latent topics among the queries and the URLs. The results of our experiments have shown that this approach can significantly improve the performance of an informational search task in terms of search accuracy and search efficiency.

EPISOSE: An Epistemology-Based Social Search Framework for Exploratory Information Seeking

Search engines are indispensable for locating information in WWW, but encounter great difficulties in handling exploratory information seeking, where precise keywords are hard to be formulated. A viable solution is to improve efficiency and quality of exploratory search by utilizing the wisdom of crowds (i.e., taking advantage of collective knowledge and efforts from a mass of searchers who share common or relevant search interests/goals). In this paper, we present an epistemology-based social search framework for supporting exploratory information seeking, which makes the best of both search engines’ immense power of information collection and pre-processing and human users’ knowledge of information filtering and post-processing. To validate the feasibility and effectiveness of the framework, we have designed and implemented a prototype system with the guidance of the framework. Our experimental results show that an epistemology-based social search system outperforms a conventional search engine for most exploratory information seeking tasks.