We are developing an Internet platform called MediaTeam that provides a marketplace connecting media content consumers to communities of media content creators. The platform is enabled by our method for automated assembly of virtual project teams. Media creators use the automated team assembler to quickly identify and team with collaborators. The team assembly platform factors in how the skills, work, and communication styles of team members complement each other into its team recommendation process. We are now testing the teaming and collaboration platforms with video creators and seek to launch by the summer.

Geotagged photos of users on social media sites provide abundant location-based data, which can be exploited for various location-based services, such as travel recommendation. In this paper, we propose a novel approach to a new application, i.e., personalized landmark recommendation based on users’ geotagged photos. We formulate the landmark recommendation task as a collaborative filtering problem, for which we propose a category-regularized matrix factorization approach that integrates both user-landmark preference and category-based landmark similarity. We collected geotagged photos from Flickr and landmark categories from Wikipedia for our experiments. Our experimental results demonstrate that the proposed approach outperforms popularity-based landmark recommendation and a basic matrix factorization approach in recommending personalized landmarks that are less visited by the population as a whole.

The blogosphere shows the characteristics of a power law distribution where a small set of the bloggers (influentials) get the majority of readership and the vast majority receives little traffic. Blogger recommendation algorithms aim at finding influentials for recommendation, putting bloggers with limited readership at further disadvantage. These bloggers could benefit from mutual endorsement of each other with the eventual goal of forming strong local communities with broader readership. In this paper, we propose a recommendation algorithm to connect blogger pairs with the intent that once connected the bloggers would share a mutually influencing relationship between them. In particular, we compute bloggers' influence profile based on how much she influences her blog friends and recommend bloggers with similar influence profiles. We characterize bloggers into four different groups: global leaders, connectors, local leaders, isolates. Our result shows marginal benefit for isolates and significant benefit for local leaders. Our approach can be instructive in building intelligent recommendation engine for bloggers with limited readership to build strong local communities.

Recommender systems are increasingly used for personalised navigation through large amounts of information, especially in the e-commerce domain for product purchase advice. Whilst much research effort is spent on developing recommenders further, there is little to no effort spent on analysing the impact of them - neither on the supply (company) nor demand (consumer) side. In this paper, we investigate the diversity impact of a movie recommender. We define diversity for different parts of the domain and measure it in different ways. The novelty of our work is the usage of real rating data (from Netflix) and a recommender system for investigating the (hypothetical) effects of various configurations of the system and users' choice models.We consider a number of different scenarios (which differ in the agent's choice model), run very extensive simulations, analyse various measurements regarding experimental validation and diversity, and report on selected findings. The choice models are an essential part of our work, since these can be influenced by the owner of the recommender once deployed.

In online discussion forums, users are more motivated to take part in discussions when observing other users’ participation—the effect of social influence among forum users. In this paper, we study how to utilize social influence for increasing the overall forum participation. To this end, we propose a mechanism to maximize user influence and boost participation by displaying forum threads to users. We formally define the participation maximization problem, and show that it is a special instance of the social welfare maximization problem with submodular utility functions and it is NP-hard. However, generic approximation algorithms is impracticable for real-world forums due to time complexity. Thus we design a heuristic algorithm, named Thread Allocation Based on Influence (TABI), to tackle the problem. Through extensive experiments using a dataset from a real-world online forum, we demonstrate that TABI consistently outperforms all other algorithms in maximizing participation. The results of this work demonstrates that current recommender systems can be made more effective by considering future influence propagations. The problem of participation maximization based on influence also opens a new direction in the study of social influence.

Social media has led to a data explosion and has begun to play an ever increasing role as a valuable source of information and a mechanism for information discovery. The wealth of data highlights the need for methods to filter and sort information in order to allow users to discover useful information. Most traditional solutions focus on the user, either the user's social network, or a form of personalization based on collaborative filtering or predictive user modeling. This paper presents a novel algorithm to view information through a lens based on a user defined collection while excluding the attributes of the user from the analysis. As a result, the lens is transparent, tunable and sharable amongst users and, additionally allows both a reduction in information overload while discovering new related content.

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.

We address the problem of minimizing a convex function over the space of large matrices with low rank. While this optimization problem is hard in general, we propose an efficient greedy algorithm and derive its formal approximation guarantees. Each iteration of the algorithm involves (approximately) finding the left and right singular vectors corresponding to the largest singular value of a certain matrix, which can be calculated in linear time. This leads to an algorithm which can scale to large matrices arising in several applications such as matrix completion for collaborative filtering and robust low rank matrix approximation.

In this paper we introduce our recommender Happy Movie, a Facebook application for movie recommendation to groups. This system exploits information about the social relationships and behaviour of the users to provide better recommendations. Our previous works have shown that social factors improve the recommendation results. However it required many questionnaires to be filled for obtaining the social information, so we have moved to a social network environment where this information is easily available.

With an explosion in the proliferation of user-generated content in communities, information overload is increasing and quality of readily available online content is deteriorating. There is an increasing need for intelligent systems that make use of implicit user generated knowledge in communities for community engagement. We describe our approach based on modeling user utterances in communities to proactively target the community for exchange of questions and answers. We envision a system that automatically encourages user engagement and participation by routing relevant conversations to users based on individual and community activity levels. In this paper, we analyze health forum conversations from WebMD, a popular health portal consumer site, and classify them in different acts of speech using Verbal Response Modes (VRM) theory. We describe our approach for modeling an intelligent community recommender to engage participants based on observations from our analysis.