In the era of big data, logistic planning can be made data-driven to take advantage of accumulated knowledge in the past. While in the movie industry, movie planning can also exploit the existing online movie knowledge library to achieve better results. However, it is ineffective to solely rely on conventional heuristics for movie planning, due to a large number of existing movies and various real-world factors that contribute to the success of each movie, such as the movie genre, available budget, production team (involving actor, actress, director, and writer), etc. In this paper, we study a "Blockbuster Planning" (BP) problem to learn from previous movies and plan for low budget yet high return new movies in a totally data-driven fashion. After a thorough investigation of an online movie knowledge library, a novel movie planning framework "Blockbuster Planning with Maximized Movie Configuration Acquaintance" (BigMovie) is introduced in this paper. From the investment perspective, BigMovie maximizes the estimated gross of the planned movies with a given budget. It is able to accurately estimate the movie gross with a 0.26 mean absolute percentage error (and 0.16 for budget). Meanwhile, from the production team's perspective, BigMovie is able to formulate an optimized team with people movie genres that team members are acquainted with. Historical collaboration records are utilized to estimate acquaintance scores of movie configuration factors via an acquaintance tensor. We formulate the BP problem as a non-linear binary programming problem and prove its NP-hardness. To solve it in polynomial time, BigMovie relaxes the hard binary constraints and addresses the BP problem as a cubic programming problem. Extensive experiments conducted on IMDB movie database demonstrate the capability of BigMovie for an effective data-driven blockbuster planning.
Whether teaching in a classroom or a Massive Online Open Course it is crucial to present the material in a way that benefits the audience as a whole. We identify two important tasks to solve towards this objective, 1 group students so that they can maximally benefit from peer interaction and 2 find an optimal schedule of the educational material for each group. Thus, in this paper, we solve the problem of team formation and content scheduling for education. Given a time frame d, a set of students S with their required need to learn different activities T and given k as the number of desired groups, we study the problem of finding k group of students. The goal is to teach students within time frame d such that their potential for learning is maximized and find the best schedule for each group. We show this problem to be NP-hard and develop a polynomial algorithm for it. We show our algorithm to be effective both on synthetic as well as a real data set. For our experiments, we use real data on students' grades in a Computer Science department. As part of our contribution, we release a semi-synthetic dataset that mimics the properties of the real data. The challenges of assessing the "health" of online social media platforms and strategically growing them are recognized by many practitioners and researchers. For those platforms that primarily rely on user-generated content, the reach -- the degree of participation referring to the percentage and involvement of users -- is a key indicator of success. This paper lays a theoretical foundation for measuring engagement as a driver of reach that achieves growth via positive externality effects. The paper takes a game theoretic approach to quantifying engagement, viewing a platform's social capital as a cooperatively created value and finding a fair distribution of this value among the contributors. It introduces engagement capacity, a measure of the ability of users and user groups to engage peers, and formulates the Engaging Team Formation Problem (EngTFP) to identify the sets of users that "make a platform go". We show how engagement capacity can be useful in characterizing forum user behavior and in the reach maximization efforts. We also stress how engagement analysis differs from influence measurement. Computational investigations with Twitter and Health Forum data reveal the properties of engagement capacity and the utility of EngTFP. Professional career training for novice employees at elementary levels to help them master necessary working skills is critical for both achieving employees' professional success and enhancing the enterprise growth. Besides adopting professional services from external career training agencies, companies can actually train the employees more effectively by involving them in various internal projects carried out in the companies. In this paper, we will study the "Employee Training" (ET) problem by assigning the employees to various concrete company internal projects. From the company perspective, besides training the employees, another important objective of carrying out these projects is to finish them successfully. The successful accomplishment of projects depends on various issues, like the skill qualification of the built teams and the effective collaboration among the team members. To achieve these two objectives simultaneously, a novel framework named "Team foRmAtion based employee traINing" (TRAIN) is proposed in this paper. TRAIN formulates the ET problem as a joint optimization problem, where the objective function considers the employees' overall skill gain and the team internal communication costs at the same time. To ensure the success of the projects, a new team skill qualification constraint is proposed and added to the optimization problem. Extensive experiments conducted on the real-world enterprise employee project team dataset demonstrate the effectiveness of TRAIN in addressing the problem. Given a task T, a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X, a subset of X, to perform the task. We call this the T EAM F ORMATION problem. We require that members of X' not only meet the skill requirements of the task, but can also work effectively together as a team. We measure effectiveness using the communication cost incurred by the subgraph in G that only involves X'. We study two variants of the problem for two different communication-cost functions, and show that both variants are NP-hard. We explore their connections with existing combinatorial problems and give novel algorithms for their solution. To the best of our knowledge, this is the first work to consider the T EAM F ORMATION problem in the presence of a social network of individuals. Experiments on the DBLP dataset show that our framework works well in practice and gives useful and intuitive results.