The mechanism of the online user preference evolution is of great significance for understanding the online user behaviors and improving the quality of online services. Since users are allowed to rate on objects in many online systems, ratings can well reflect the users' preference. With two benchmark datasets from online systems, we uncover the memory effect in users' selecting behavior which ...

Numerous studies on blended learning suggested that students embraced this learning approach and yet there are not much comparative studies on the merits of online versus face-to-face activities. Online discussion, online debate and face-to-face debates between two classes of undergraduate student teachers were conducted. The author was interested to examine if the blended approach with another...

Recommender systems in online marketing websites like Amazon.com and CDNow.com suggest relevant services and favorite products to customers. In this paper, we proposed a novel hypergraph-based kernel computation combined with k nearest neighbor (kNN) to predict ratings of users. In this method, we change regular definition style of hypergraph diffusion kernel. Our comparative studies show that ...

Our research is within the subfield of modeling trust and reputation in multi-agent systems for online communities. Specifically, in an online community involving users and entities, users provide opinions (ratings) to entities. For each user, we are interested in addressing two problems: • how to accurately model the reputation of entities by aggregating opinions from all the users (advisors);...

In this paper, we study whether TaskRabbit, a prominent online freelance marketplace, is impacted by racial and gender bias. We collect all worker profiles from TaskRabbit to gather information about workers’ gender, race, customer reviews, ratings, and positions in search rankings. We find that gender and race are significantly correlated with worker evaluations, as well as the workers’ rank i...

The large majority of reputation systems use features such as star ratings and reviews to give users a reputation in online peer-to-peer markets. Both have been shown to be effective for signaling trustworthiness. However, the exact extent to which these features can change perceptions of users’ trustworthiness remains an open question. Using data from an online experiment conducted on Airbnb u...

Customer ratings are valuable sources to understand their satisfaction and are critical for designing better customer experiences and recommendations. The majority of customers, however, do not respond to rating surveys, which makes the result less representative. To understand overall satisfaction, this paper aims to investigate how likely customers without responses had satisfactory experienc...

The personal description of a company associated with job satisfaction, culture, and opinions senior leadership is available on workplace community websites. However, it almost impossible to read all the different possibly even contradictory reviews make an accurate overall rating. Therefore, extracting aspects or sentiments from online corresponding ratings important challenge. We collect anon...

Several attempts have been made to analyze information diffusion on online knowledge sharing sites. In this poster, we report a preliminary study analyzing @cosme, a viral marketing site, which is among the largest community sites in Japan. The notable characteristics of @cosme are that users can bookmark their favorite reviewers; moreover, they can post their own reviews of products. In other ...