Byeong Man Kim

A new approach for combining content-based and collaborative filters

With the development of e-commerce and the proliferation of easily accessible information, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been proposed for performing recommendations, including content-based and collaborative techniques. Content-based filtering selects information based on semantic content, whereas collaborative filtering combines the opinions of other users to make a prediction for a target user. In this paper, we describe a new filtering approach that combines the content-based filter and collaborative filter to capitalize on their respective strengths, and thereby achieves a good performance. We present a series of recommendations on the selection of the appropriate factors and also look into different techniques for calculating user-user similarities based on the integrated information extracted from user profiles and user ratings. Finally, we experimentally evaluate our approach and compare it with classic filters, the result of which demonstrate the effectiveness of our approach.

Clustering approach for hybrid recommender system

Recommender system is a kind of Web intelligence techniques to make a daily information filtering for people. Clustering techniques have been applied to the item-based collaborative filtering framework to solve the cold start problem. It also suggests a way to integrate the content information into the collaborative filtering. Extensive experiments have been conducted on MovieLens data to analyze the characteristics of our technique. The results show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem.

A probabilistic music recommender considering user opinions and audio features

A recommender system has an obvious appeal in an environment where the amount of on-line information vastly outstrips any individuals capability to survey. Music recommendation is considered a popular application area. In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focus on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to utilize information extracted directly from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. In addition, this model has been extended for improved recommendation performance by utilizing audio features that help alleviate three well-known problems associated with data sparseness in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experimental results based on two real-world data sets lead us to believe that content information is crucial in achieving better personalized recommendation beyond user ratings. We further show how primitive audio features can be combined into aggregate features for the proposed CRMS and analyze their influences on recommendation performance. Although this model was developed originally for music collaborative recommendation based on audio features, our experiment with the movie data set demonstrates that it can be applied to other domains.

An Approach for Combining Content-based and Collaborative Filters

In this work, we apply a clustering technique to integrate the contents of items into the item-based collaborative filtering framework. The group rating information that is obtained from the clustering result provides a way to introduce content information into collaborative recommendation and solves the cold start problem. Extensive experiments have been conducted on MovieLens data to analyze the characteristics of our technique. The results show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem.

A music recommender based on audio features

Many collaborative music recommender systems (CMRS) have succeeded in capturing the similarity among users or items based on ratings, however they have rarely considered about the available information from the multimedia such as genres, let alone audio features from the media stream. Such information is valuable and can be used to solve several problems in RS. In this paper, we design a CMRS based on audio features of the multimedia stream. In the CMRS, we provide recommendation service by our proposed method where a clustering technique is used to integrate the audio features of music into the collaborative filtering (CF) framework in hopes of achieving better performance. Experiments are carried out to demonstrate that our approach is feasible.

Constructing user profiles for collaborative recommender system

With the development of e-commerce and information access, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. In this paper, clustering technique is applied in the collaborative recommender framework to consider semantic contents available from the user profiles. We also suggest methods to construct user profiles from rating information and attributes of items to accommodate user preferences. Further, we show that the correct application of the semantic content information obtained from user profiles does enhance the effectiveness of collaborative recommendation.

Probabilistic Model Estimation for Collaborative Filtering Based on Items Attributes

With the development of e-commerce and the proliferation of easily accessible information, recommender systems have become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. While many collaborative recommender systems (CRS) have succeeded in capturing the similarity among users or items based on ratings to provide good recommendation, there are still some challenges for them to be a more efficient RS. In this paper, we address three problems in CRS (user bias, non-transitive association, and new item problem) and provide a new item-based probabilistic model approach in order to solve the addressed problems in hopes of achieving better performance. In this probabilistic model, items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. Experiments on a real-word data set illustrate that our proposed approach is comparable with others.

A probabilistic model for music recommendation considering audio features

In order to make personalized recommendations, many collaborative music recommender systems (CMRS) focused on capturing precise similarities among users or items based on user historical ratings. Despite the valuable information from audio features of music itself, however, few studies have investigated how to directly extract and utilize information from music for personalized recommendation in CMRS. In this paper, we describe a CMRS based on our proposed item-based probabilistic model, where items are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. By utilizing audio features, this model provides a way to alleviate three well-known challenges in collaborative recommender systems: user bias, non-association, and cold start problems in capturing accurate similarities among items. Experiments on a real-world data set illustrate that the audio information of music is quite useful and our system is feasible to integrate it for better personalized recommendation.

High capacity reversible watermarking using differential histogram shifting and predicted error compensation

Reversible watermarking inserts watermarks into digital media in such a way that visual transparency is preserved, which enables the restoration of the original media from the watermarked one without any loss of media quality. It has various applications where high capacity and high visual quality are major requirements for reversible watermarking. This work presents a new reversible watermarking algorithm that embeds message bits by modifying the differential histogram of adjacent pixels. To satisfy both high embedding capacity and visual quality, the proposed technique exploits the fact that the adjacent pixels are highly correlated. Also, we prevent overflow and underflow problems by designing a predicted error compensation scheme. Through experiments using multiple kinds of test images, we prove that the presented algorithm provides 100% reversibility, higher capacity, and higher visual quality than any previous method, while maintaining low induced distortion.

A new collaborative recommender system addressing three problems

With the development of e-commerce and information access, a large amount of information can be found online, which makes a good recommendation service to be urgently necessary. While many collaborative recommender systems (CRS) have succeeded in capturing the similarity among users or items based on ratings, there are still some challenges for them to be a more efficient RS. In this paper, we address three problems in CRS, that is user bias, nontransitive association, and new item problem, and show that the ICHM suggested in our previous work is able to solve the addressed problems. A series of experiments are carried out to show that our approach is feasible.