Signed PageRank on Online Rating Systems

The ratings in many user-object online rating systems can reflect whether users like or dislike the objects,and in some online rating systems,users can directly choose whether to like an object.So these systems can be represented by signed bipartite networks,but the original unsigned node evaluation algorithm cannot be directly used on the signed networks.This paper proposes the Signed Page Rank algorithm for signed bipartite networks to evaluate the object and user nodes at the same time.Based on the global information,the nodes can be sorted by the Signed Page Rank values in descending order,and the result is SR Ranking.The authors analyze the characteristics of top and bottom nodes of the real networks and find out that for objects,the SR Ranking can provide a more reasonable ranking which combines the degree and rating of node,and the algorithm also can help us to identify users with specific rating patterns.By discussing the location of negative edges and the sensitivity of object SR Ranking to negative edges,the authors also explore that the negative edges play an important role in the algorithm and explain that why the bad reviews are more important in real networks.

Dishonest Behaviors in Online Rating Systems:Cyber Competition,Attack Models,and Attack Generator

Recently, online rating systems are gaining popularity. Dealing with unfair ratings in such systems has been recognized as an important but challenging problem. Many unfair rating detection approaches have been developed and evaluated against simple attack models. However, the lack of unfair rating data from real human users and realistic attack behavior models has become an obstacle toward developing reliable rating systems. To solve this problem, we design and launch a rating challenge to collect unfair rating data from real human users. In order to broaden the scope of the data collection, we also develop a comprehensive signai-based unfair rating detection system. Based on the analysis of real attack data, we discover important features in unfair ratings, build models and generator developed in this paper can be directly attack models, and develop an unfair rating generator. The used to test current rating aggregation systems, as well as to assist the design of future rating systems.

ITMA ASIA + CITME 2016 online visitor registration opens with early bird rates

Comprehensive visitor promotion campaign gains momentumITMA ASIA+CITME 2016,the fifth edition of the combined textile machinery show,is expected to attract a trade visitorship of around 100,000 from around the world.Visitors can now purchase badges online at www.itmaasia.com and www.citme.com.cn until 1 October 2016 and enjoy a discount of 40 percent.The early bird rates are RMB 30 for

Assessment of laryngologists'ratings on physician review websites

Objective:To assess and characterize online ratings and comments on laryngologists and determine factors that correlate with higher ratings.Methods:All the American Laryngological Association(ALA)members were queried across several online platforms.Ratings were normalized for comparison on a five-point Likert scale.Ratings were categorized based on context and for positive/negative aspects.Results:Of the 331 ALA members,256(77%)were rated on at least one online platform.Across all platforms,the average overall rating was 4.39±0.61(range:1.00-5.00).Specific positive ratings including“bedside manners,”“diagnostic accuracy,”“adequate time spent with patient,”“appropriate follow-up,”and“physician timeliness”had significant positive correlations to overall ratings,by Pearson's correlation(P<0.001).Long wait times had significant negative correlations to overall ratings(P<0.001).Conclusion:Online ratings and comments for laryngologists are significantly influenced by patient perceptions of bedside manner,physician competence,and time spent with the patient.