Al Mamunur Rashid

Using Social Psychology to Motivate Contributions to Online Communities

Under-contribution is a problem for many online communities. Social psychology theories of social loafing and goal-setting can lead to mid-level design goals to address this problem. We tested design principles derived from these theories in four field experiments involving members of an online movie recommender community. In each of the experiments participated were given different explanations for the value of their contributions. As predicted by theory, individuals contributed when they were reminded of their uniqueness and when they were given specific and challenging goals. However, other predictions were disconfirmed. For example, in one experiment, participants given group goals contributed more than those given individual goals. The article ends with suggestions and challenges for mining design implications from social science theories.

Getting to know you: learning new user preferences in recommender systems

Recommender systems have become valuable resources for users seeking intelligent ways to search through the enormous volume of information available to them. One crucial unsolved problem for recommender systems is how best to learn about a new user. In this paper we study six techniques that collaborative filtering recommender systems can use to learn about new users. These techniques select a sequence of items for the collaborative filtering system to present to each new user for rating. The techniques include the use of information theory to select the items that will give the most value to the recommender system, aggregate statistics to select the items the user is most likely to have an opinion about, balanced techniques that seek to maximize the expected number of bits learned per presented item, and personalized techniques that predict which items a user will have an opinion about. We study the techniques thru offline experiments with a large pre-existing user data set, and thru a live experiment with over 300 users. We show that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.

On the recommending of citations for research papers

Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain.

Motivating participation by displaying the value of contribution

One of the important challenges faced by designers of online communities is eliciting sufficent contributions from community members. Users in online communities may have difficulty either in finding opportunities to add value, or in understanding the value of their contributions to the community. Various social science theories suggest that showing users different perspectives on the value they add to the community will lead to differing amounts of contribution. The present study investigates a design augmentation for an existing community Web site that could benefit from additional contribution. The augmented interface includes individualized opportunities for contribution and an estimate of the value of each contribution to the community. The value is computed in one of four different ways: (1) value to self; (2) value to a small group the user has affinity with; (3) value to a small group the user does not have affinity with; and (4) value to the entire user community. The study compares the effectiveness of the different notions of value to 160 community members.

Learning preferences of new users in recommender systems: an information theoretic approach

Recommender systems are an effective tool to help find items of interest from an overwhelming number of available items. Collaborative Filtering (CF), the best known technology for recommender systems, is based on the idea that a set of like-minded users can help each other find useful information. A new user poses a challenge to CF recommenders, since the system has no knowledge about the preferences of the new user, and therefore cannot provide personalized recommendations. A new user preference elicitation strategy needs to ensure that the user does not a) abandon a lengthy signup process, and b) lose interest in returning to the site due to the low quality of initial recommendations. We extend the work of [23] in this paper by incrementally developing a set of information theoretic strategies for the new user problem. We propose an offline simulation framework, and evaluate the strategies through extensive offline simulations and an online experiment with real users of a live recommender system.

Proactive displays: Supporting awareness in fluid social environments

Academic conferences provide a social space for people to present their work and interact with one another. However, opportunities for interaction are unevenly distributed among the attendees. We seek to extend the opportunities for interaction among attendees by using technology to enable them to reveal information about their background and interests in different settings. We evaluate a suite of applications that augment three physical social spaces at an academic conference. The applications were designed to augment formal conference paper sessions and informal breaks. A mixture of qualitative observation and survey response data are used to frame the impacts from both individual and group perspectives. Respondents reported on their interactions and serendipitous findings of shared interests with other attendees. However, some respondents also identify distracting aspects of the augmentation. Our discussion relates these results to existing theory of group behavior in public places and how these social space augmentations relate to awareness as well as the problem of shared interaction models.

User Attitudes Towards Browsing Data Collection

Web browsing data is the foundation of online advertising and can also be used to understand behavioral patterns of web users. This data, however, has also been the source of pervasive privacy concerns. In this paper we probed the sources of the underlying privacy concerns. Through user studies, we investigated the level of concern with URL collection compared to higher-level profiling into interest or preference categories. Consistent with intuition and industry practice, our results indicate that most users do indeed find URLs much more sensitive than the profiles built from them. Interestingly, however, we found that a great challenge in this area is comprehension of what categorization actually means, which is essential to properly make privacy decisions when services collect categories rather than URLs. This paper also investigated whether certain categories may also be a source of privacy concern. We tested sensitivity of a range of categories. Most categories were considered sensitive by relatively few users, and the categories deemed sensitive followed a power-law distribution.