Cheng Te Li

Team Formation for Generalized Tasks in Expertise Social Networks

Given an expertise social network and a task consisting of a set of required skills, the team formation problem aims at finding a team of experts who not only satisfy the requirements of the given task but also communicate to one another in an effective manner. To solve this problem, Lappas et al. has proposed the Enhance Steiner algorithm. In this work, we generalize this problem by associating each required skill with a specific number of experts. We propose three approaches to form an effective team for the generalized task. First, we extend the Enhanced-Steiner algorithm to a generalized version for generalized tasks. Second, we devise a density-based measure to improve the effectiveness of the team. Third, we present a novel grouping-based method that condenses the expertise information to a group graph according to required skills. This group graph not only drastically reduces the search space but also avoid redundant communication costs and irrelevant individuals when compiling team members. Experimental results on the DBLP dataset show the teams found by our methods performs well in both effectiveness and efficiency.

Exploiting large-scale check-in data to recommend time-sensitive routes

Location-based services allow users to perform geo-spatial check-in actions, which facilitates the mining of the moving activities of human beings. This paper proposes to recommend time-sensitive trip routes, consisting of a sequence of locations with associated time stamps, based on knowledge extracted from large-scale check-in data. Given a query location with the starting time, our goal is to recommend a time-sensitive route. We argue a good route should consider (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. By devising a statistical model, we integrate these four factors into a goodness function which aims to measure the quality of a route. Equipped with the goodness measure, we propose a greedy method to construct the time-sensitive route for the query. Experiments on Gowalla datasets demonstrate the effectiveness of our model on detecting real routes and cloze test of routes, comparing with other baseline methods. We also develop a system TripRouter as a real-time demo platform.

Emotion-based impressionism slideshow with automatic music accompaniment

In this paper, we propose the emotion-based Impressionism slideshow system with automatic music accompaniment. While conventional image slideshow systems accompany images with music manually, our proposed approach explores the affective content of painting to automatically recommend music based on emotions. This is achieved by association discovery between painting features and emotions, and between emotions and music features respectively. To generate more harmonic Impressionism presentation, a linear arrangement method is proposed based on modified traveling salesman algorithm. Moreover, some animation effects and synchronization issues for affective content of Impressionism fine arts are considered. Experimental result shows our emotion-based accompaniment brings better browsing experience of aesthetics.

Modeling and Visualizing Information Propagation in a Micro-blogging Platform

Micro-blogging is a type of social networking service that has become ubiquitous in Web 2.0 era. Micro-blogs allows bloggers to exchange information, discuss ideas, and share experiences with friends or even strangers with similar interests. In this paper, we try to identify ways to measure how information is propagated in micro-blogs. More specifically, we consider the following issues. (1) How to quantify a person¡¦s capability to disseminate ideas via a micro-blog. (2) How to measure the extent of propagation of a concept in a micro-blog. (3) How to demonstrate and visualize information propagation in a micro-blog. We propose methods to effectively measure each user¡¦s ability to disseminate information via micro-blogs. The design of the measure considers three factors: (a) the number of people influenced, (b) the speed of propagation, and (c) the geographic distance of the propagation. We also provide an online demonstration micro-blog system that allows the users to explore the information propagation. The system shows the propagation paths and social graphs, influence scores, timelines, and geographical information among people for the user-given terms.

On team formation with expertise query in collaborative social networks

Given a collaborative social network and a task consisting of a set of required skills, the team formation problem aims at finding a team of experts who not only satisfies the requirements of the given task but also is able to communicate with one another in an effective manner. This paper extends the original team formation problem to a generalized version, in which the number of experts selected for each required skill is also specified. The constructed teams need to contain adequate number of experts for each required skill. We develop two approaches to compose teams for the proposed generalized team formation tasks. First, we consider the specific number of experts to devise the generalized Enhanced-Steiner algorithm. Second, we present a grouping-based method condensing the expertise information to a compact representation, group graph, based on the required skills. Group graph can not only reduce the search space but also eliminate redundant communication cost and filter out irrelevant individuals when compiling team members. To further improve the effectiveness of the composed teams, we propose a density-based measure and embed it into the developed methods. Experimental results on the DBLP network show that the teams composed by the proposed methods have better performance in both effectiveness and efficiency.

Labeled Influence Maximization in Social Networks for Target Marketing

The influence maximization problem is to find a set of seed nodes which maximize the spread of influence in a social network. The seed nodes are used for the viral marketing to gain the maximum profits through the effective word-of-mouth. However, in more real-world cases, marketers usually target certain products at particular groups of customers. While original influence maximization problem considers no product information and target customers, in this paper, we focus on the target marketing. We propose the labeled influence maximization problem, which aims to find a set of seed nodes which can trigger the maximum spread of influence on the target customers in a labeled social network. We propose three algorithms to solve such labeled influence maximization problem. We first develop the algorithms based on the greedy methods of original influence maximization by considering the target customers. Moreover, we develop a novel algorithm, Maximum Coverage, whose central idea is to offline compute the pair wise proximities of nodes in the labeled social network and online find the set of seed nodes. This allows the marketers to plan and evaluate strategies online for advertised products. The experimental results on IMDb labeled social network show our methods can achieve promising performances on both effectiveness and efficiency.

Measuring and Recommending Time-Sensitive Routes from Location-Based Data

Location-based services allow users to perform geospatial recording actions, which facilitates the mining of the moving activities of human beings. This article proposes to recommend time-sensitive trip routes consisting of a sequence of locations with associated timestamps based on knowledge extracted from large-scale timestamped location sequence data (e.g., check-ins and GPS traces). We argue that a good route should consider (a) the popularity of places, (b) the visiting order of places, (c) the proper visiting time of each place, and (d) the proper transit time from one place to another. By devising a statistical model, we integrate these four factors into a route goodness function that aims to measure the quality of a route. Equipped with the route goodness, we recommend time-sensitive routes for two scenarios. The first is about constructing the route based on the user-specified source location with the starting time. The second is about composing the route between the specified source location and the destination location given a starting time. To handle these queries, we propose a search method, Guidance Search, which consists of a novel heuristic satisfaction function that guides the search toward the destination location and a backward checking mechanism to boost the effectiveness of the constructed route. Experiments on the Gowalla check-in datasets demonstrate the effectiveness of our model on detecting real routes and performing cloze test of routes, comparing with other baseline methods. We also develop a system TripRouter as a real-time demo platform.

Intelligent menu planning: recommending set of recipes by ingredients

With the growth of recipe sharing services, online cooking recipes associated with ingredients and cooking procedures are available. Many recipe sharing sites have devoted to the development of recipe recommendation mechanism. However, there is a need for users to plan menu of meals by ingredients. While most research on food related research has been on recipe recommendation and retrieval, little research has been done on menu planning. In this paper, we investigate an intelligent menu planning mechanism which recommending sets of recipes by user-specified ingredients. Those recipes which are well-accompanied and contain the query ingredients are returned. We propose a graph-based algorithm for menu planning. The proposed approach constructs a recipe graph to capture the co-occurrence relationships between recipes from collection of menus. A menu is generated by approximate Steiner Tree Algorithm on the constructed recipe graph. Evaluation of menu collections from Food.com shows that the proposed approach achieves encouraging results.

Egocentric Information Abstraction for Heterogeneous Social Networks

Social network is a powerful data structure that allows the depiction of relationship information between entities. However, real-world social networks are sometimes too complex for human to pursue further analysis. In this work, an unsupervised mechanism is proposed for egocentric information abstraction in heterogeneous social networks. To achieve this goal, we propose a vector space representation for heterogeneous social networks to identify linear combination of relations as features and compute statistical dependencies as feature values. Then we design several abstraction criteria to distill representative and important information to construct the abstracted graphs for visualization. The evaluations conducted on a real world movie dataset and an artificial crime dataset demonstrate that the abstractions can indeed retain important information and facilitate more accurate and efficient human analysis.

Mining and Planning Time-aware Routes from Check-in Data

Location-based services allow users to perform check-in actions, which not only record their geo-spatial activities, but also provide a plentiful source for data scientists to analyze and plan more accurate and useful geographical recommender system. In this paper, we present a novel Time-aware Route Planning (TRP) problem using location check-in data. The central idea is that the pleasure of staying at the locations along a route is significantly affected by their visiting time. Each location has its own proper visiting time due to the category, objective, and population. To consider the visiting time of locations into route planning, we develop a three-stage time-aware route planning framework. First, since there is usually either noise time on existing locations or no visiting information on new locations constructed, we devise an inference method, LocTimeInf, to predict and recover the location visiting time on routes. Second, we aim to find the representative and popular time-aware location-transition behaviors from user check-in data, and a Time-aware Transit Pattern Mining (TTPM) algorithm is proposed correspondingly. Third, based on the mined time-aware transit patterns, we develop a Proper Route Search (PR-Search) algorithm to construct the final time-aware routes for recommendation. Experiments on Gowalla check-in data exhibit the promising effectiveness and efficiency of the proposed methods, comparing to a series of competitors.