Shou-De Lin

Designing the market game for a trading agent competition

The authors discuss the design and operation of a trading agent competition, focusing on the game structure and some of the key technical issues in running and playing the game. They also describe the competitions genesis, its technical infrastructure, and its organization. The article by A. Greenwald and P. Stone (2001), describes the competition from a participants perspective and describes the strategies of some of the top-placing agents. A visualization of the competition and a description of the preliminary and final rounds of the TAC are available in IC Online (http://computer.org/internet/tac.htm).

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.

Cost-Sensitive Multi-Label Learning for Audio Tag Annotation and Retrieval

Audio tags correspond to keywords that people use to describe different aspects of a music clip. With the explosive growth of digital music available on the Web, automatic audio tagging, which can be used to annotate unknown music or retrieve desirable music, is becoming increasingly important. This can be achieved by training a binary classifier for each tag based on the labeled music data. Our method that won the MIREX 2009 audio tagging competition is one of this kind of methods. However, since social tags are usually assigned by people with different levels of musical knowledge, they inevitably contain noisy information. By treating the tag counts as costs, we can model the audio tagging problem as a cost-sensitive classification problem. In addition, tag correlation information is useful for automatic audio tagging since some tags often co-occur. By considering the co-occurrences of tags, we can model the audio tagging problem as a multi-label classification problem. To exploit the tag count and correlation information jointly, we formulate the audio tagging task as a novel cost-sensitive multi-label (CSML) learning problem and propose two solutions to solve it. The experimental results demonstrate that the new approach outperforms our MIREX 2009 winning method.

Inferring Air Quality for Station Location Recommendation Based on Urban Big Data

This paper tries to answer two questions. First, how to infer real-time air quality of any arbitrary location given environmental data and historical air quality data from very sparse monitoring locations. Second, if one needs to establish few new monitoring stations to improve the inference quality, how to determine the best locations for such purpose? The problems are challenging since for most of the locations (>99%) in a city we do not have any air quality data to train a model from. We design a semi-supervised inference model utilizing existing monitoring data together with heterogeneous city dynamics, including meteorology, human mobility, structure of road networks, and point of interests (POIs). We also propose an entropy-minimization model to suggest the best locations to establish new monitoring stations. We evaluate the proposed approach using Beijing air quality data, resulting in clear advantages over a series of state-of-the-art and commonly used methods.

Unsupervised link prediction using aggregative statistics on heterogeneous social networks

The concern of privacy has become an important issue for online social networks. In services such as Foursquare.com, whether a person likes an article is considered private and therefore not disclosed; only the aggregative statistics of articles (i.e., how many people like this article) is revealed. This paper tries to answer a question: can we predict the opinion holder in a heterogeneous social network without any labeled data? This question can be generalized to a link prediction with aggregative statistics problem. This paper devises a novel unsupervised framework to solve this problem, including two main components: (1) a three-layer factor graph model and three types of potential functions; (2) a ranked-margin learning and inference algorithm. Finally, we evaluate our method on four diverse prediction scenarios using four datasets: preference (Foursquare), repost (Twitter), response (Plurk), and citation (DBLP). We further exploit nine unsupervised models to solve this problem as baselines. Our approach not only wins out in all scenarios, but on the average achieves 9.90% AUC and 12.59% NDCG improvement over the best competitors. The resources are available at http://www.csie.ntu.edu.tw/~d97944007/aggregative/

Matching users and items across domains to improve the recommendation quality

Given two homogeneous rating matrices with some overlapped users/items whose mappings are unknown, this paper aims at answering two questions. First, can we identify the unknown mapping between the users and/or items? Second, can we further utilize the identified mappings to improve the quality of recommendation in either domain? Our solution integrates a latent space matching procedure and a refining process based on the optimization of prediction to identify the matching. Then, we further design a transfer-based method to improve the recommendation performance. Using both synthetic and real data, we have done extensive experiments given different real life scenarios to verify the effectiveness of our models. The code and other materials are available at http://www.csie.ntu.edu.tw/~r00922051/matching/

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.

Generalized k -Labelsets Ensemble for Multi-Label and Cost-Sensitive Classification

Label powerset (LP) method is one category of multi-label learning algorithm. This paper presents a basis expansions model for multi-label classification, where a basis function is an LP classifier trained on a random k-labelset. The expansion coefficients are learned to minimize the global error between the prediction and the ground truth. We derive an analytic solution to learn the coefficients efficiently. We further extend this model to handle the cost-sensitive multi-label classification problem, and apply it in social tagging to handle the issue of the noisy training set by treating the tag counts as the misclassification costs. We have conducted experiments on several benchmark datasets and compared our method with other state-of-the-art multi-label learning methods. Experimental results on both multi-label classification and cost-sensitive social tagging demonstrate that our method has better performance than other methods.

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.