Sang-Ki Ko

A movie recommendation algorithm based on genre correlations

Since the late 20th century, the number of Internet users has increased dramatically, as has the number of Web searches performed on a daily basis and the amount of information available. A huge amount of new information is transferred to the Web on a daily basis. However, not all data are reliable and valuable, which implies that it may become more and more difficult to obtain satisfactory results from Web searches. We often iterate searches several times to find what we are looking for. To solve this problem, researchers have suggested the use of recommendation systems. Instead of searching for the same information several times, a recommendation system proposes relevant information. In the Web 2.0 era, recommendation systems often rely on collaborative filtering by users. In general, a collaborative filtering approach based on user information such as gender, location, or preference is effective. However, the traditional approach can fail due to the cold-start problem or the sparsity problem, because initial user information is required for this approach to be effective. Recently, several attempts have been made to tackle these collaborative filtering problems. One such attempt used category correlations of contents. For instance, a movie has genre information provided by movie experts and directors. This category information is more reliable than user ratings. Moreover, newly created content always has category information, allowing avoidance of the cold-start problem. In this study, we consider a movie recommendation system and improve the previous algorithms based on genre correlations to correct its shortcomings. We also test the modified algorithm and analyze the results with respect to two characteristics of genre correlations.

THE EDIT-DISTANCE BETWEEN A REGULAR LANGUAGE AND A CONTEXT-FREE LANGUAGE

The edit-distance between two strings is the smallest number of operations required to transform one string into the other. The distance between languages L1 and L2 is the smallest edit-distance be...

Analysis of a cellular automaton model for car traffic with a junction

We propose a cellular automaton model that simulates traffic flow with a junction. We consider the form-one-lane rule and the merge-lane rule that decide which car moves ahead when two cars in two different lanes are in front of a junction. We simulate the proposed cellular automaton model for both rules, and generate fundamental diagrams and car distribution examples. Then, we analyze experimental results and demonstrate that the proposed model reflects the real world traffic flow with a junction according to the considered rules.

Pseudo-inversion on Formal Languages

We consider the pseudo-inversion operation inspired by a biological event as a result of the partial inversion. We define the pseudo-inversion of a string w = uxv to consist of all strings v R xu R , where uv ≠ λ and consider the operation from a formal language theoretic viewpoint. We show that regular languages are closed under the pseudo-inversion operation whereas context-free languages are not. Furthermore, we consider the iterated pseudo-inversion operation and establish the basic properties. Finally, we introduce the pseudo-inversion-freeness and examine closure properties and decidability problems for regular and context-free languages. We establish that pseudo-inversion-freeness is decidable in polynomial time for regular languages and undecidable for context-free languages.

A smart movie recommendation system

We propose a movie recommendation system based on genre correlations. We modify the previous algorithm; we use a list of movies as input instead of genre combinations. We implement a new recommendation algorithm as Android application with additional functions. By combining with existing web services such as Google Movie Showtimes and Open APIs, our system can recommend movies playing in cinemas currently and show the detailed information of movies. Location-based function is also implemented. We utilize GPS information of mobile device and web service provided by Google Maps for recommending suitable cinemas for users with mobile devices.

Approximate matching between a context-free grammar and a finite-state automaton

For a given context-free grammar (CFG) and a finite-state automaton (FA), we tackle the edit-distance problem-the problem of computing the most similar pair of strings in the two respective languages. In particular, we consider three different gap cost models for the edit-distance that are crucial for finding a proper alignment between two bio sequences: the linear, affine and concave models. We design efficient algorithms for the edit-distance between a CFG and an FA under these gap cost models. The time complexity of our algorithm for computing the linear or affine gap distance is polynomial and the time complexity for the concave gap distance is exponential.

State complexity of deletion and bipolar deletion

It is well known that the language obtained by deleting an arbitrary language from a regular language is regular. We give an upper bound for the state complexity of deleting an arbitrary language from a regular language and a matching lower bound. We show that the state complexity of deletion is

State Complexity of Insertion

n \cdot 2^{n-1}