Juntae Kim

A recommendation algorithm using multi-level association rules

Recommendation systems predict users preference to suggest items. Collaborative filtering is the most popular method in implementing a recommendation system. The collaborative filtering method computes similarities between users based on each users known preference, and recommends the items preferred by similar users. Although the collaborative filtering method generally shows good performance, it suffers from two major problems - data sparseness and scalability. We present a model-based recommendation algorithm that uses multilevel association rules to alleviate those problems. In this algorithm, we build a model for preference prediction by using association rule mining. Multilevel association rules are used to compute preferences for items. The experimental results show that applying multilevel association rules is effective, and performance of the algorithm is improved compared with the collaborative filtering method in terms of the recall and the computation time.

Detection of auto programs for MMORPGs

Auto-playing programs are often used on behalf of human players in a MMORPG(Massively Multi-player Online Role Playing Game). By playing automatically and continuously, it helps to speed up the game character’s level-up process. However, the auto-playing programs, either software or hardware, do harm to games servers in various ways including abuse of resources. In this paper, we propose a way of detecting the auto programs by analyzing the window event sequences produced by the game players. In our proposed method, the event sequences are transformed into a set of attributes, and various learning algorithms are applied to classify the data represented by the set of attribute values into human or auto player. The results from experiments with several MMORPGs show that the Decision Tree learning with proposed method can identify the auto-playing programs with high accuracy.

An Adaptive Recommendation System with a Coordinator Agent

This paper presents a recommendation system with a coordinator agent that is adaptive to its environment. Recommendation systems that suggest items to users are gaining popularity in the field of electronic commerce. Various methods such as collaborative, content-based, and demographic recommendation have been used to analyze and predict the preference of users. According to the characteristic of the application domain, the performance of each method varies. In the proposed system, we introduce a coordinator agent that adaptively changes the weights of each recommendation method to provide combined recommendation appropriate for the given environment.

Reducing the Search Space for Pathfinding in Navigation Meshes by Using Visibility Tests

A navigation mesh (NavMesh) is a suitable tool for the representation of a three-dimensional game world. A NavMesh consists of convex polygons covering free space, so the path can be found reliably without detecting collision with obstacles. The main disadvantage of a NavMesh is the huge state space. When the A * algorithm is applied to polygonal meshes for detailed terrain representation, the pathfinding can be inefficient due to the many states to be searched. In this paper, we propose a method to reduce the number of states searched by using visibility tests to achieve fast searching even on a detailed terrain with a large number of polygons. Our algorithm finds the visible vertices of the obstacles from the critical states and uses the heuristic function of A * , defined as the distance to the goal through such visible vertices. The results show that the number of searched states can be substantially reduced compared to the A * search with a straight-line distance heuristic.

Improving Performance of Search Engine By Using WordNet-based Collaborative Evaluation and Hyperlink

In this paper, we propose a web page weighting scheme based on WordNet-based collaborative evaluation and hyperlink to improve the precision of web search engine. Generally search engines use keyword matching to decide web page ranking. In the information retrieval from huge data such as the Web, simple word comparison cannot distinguish important documents because there exist too many documents with similar relevancy. In this paper, we implement a WordNet-based user interface that helps to distinguish different senses of query word, and constructed a search engine in which the implicit evaluations by multiple users are reflected in ranking by accumulating the number of clicks. In accumulating click counts, they are stored separately according to lenses, so that more accurate search is possible. Weighting of each web page by using collaborative evaluation and hyperlink is reflected in ranking. The experimental results with several keywords show that the precision of proposed system is improved compared to conventional search engines.