Heung-Nam Kim

Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation

Abstract We propose a collaborative filtering method to provide an enhanced recommendation quality derived from user-created tags. Collaborative tagging is employed as an approach in order to grasp and filter users’ preferences for items. In addition, we explore several advantages of collaborative tagging for data sparseness and a cold-start user. These applications are notable challenges in collaborative filtering. We present empirical experiments using a real dataset from del . icio . us . Experimental results show that the proposed algorithm offers significant advantages both in terms of improving the recommendation quality for sparse data and in dealing with cold-start users as compared to existing work.

CD-MAC: Cooperative Diversity MAC for Robust Communication in Wireless Ad Hoc Networks

This paper proposes a medium access control (MAC) algorithm, called Cooperative Diversity MAC (CD-MAC), which exploits the cooperative communication capability to improve robustness in wireless ad hoc networks. In CD-MAC, each terminal proactively selects a relay for cooperation and lets it transmit simultaneously when it is beneficial in mitigating interference from nearby terminals and thus improving the network performance. For practicability, CD-MAC is designed based on the widely adopted IEEE 802.11 MAC. System-level simulation study shows that CD-MAC significantly outperforms the original IEEE 802.11 MAC in terms of packet delivery ratio.

Collaborative error-reflected models for cold-start recommender systems

Collaborative Filtering (CF), one of the most successful technologies among recommender systems, is a system assisting users to easily find useful information. One notable challenge in practical CF is the cold start problem, which can be divided into cold start items and cold start users. Traditional CF systems are typically unable to make good quality recommendations in the situation where users and items have few opinions. To address these issues, in this paper, we propose a unique method of building models derived from explicit ratings and we apply the models to CF recommender systems. The proposed method first predicts actual ratings and subsequently identifies prediction errors for each user. From this error information, pre-computed models, collectively called the error-reflected model, are built. We then apply the models to new predictions. Experimental results show that our approach obtains significant improvement in dealing with cold start problems, compared to existing work.

Collaborative user modeling with user-generated tags for social recommender systems

With the popularity of social media services, the sheer amount of content is increasing exponentially on the Social Web that leads to attract considerable attention to recommender systems. Recommender systems provide users with recommendations of items suited to their needs. To provide proper recommendations to users, recommender systems require an accurate user model that can reflect a users characteristics, preferences and needs. In this study, by leveraging user-generated tags as preference indicators, we propose a new collaborative approach to user modeling that can be exploited to recommender systems. Our approach first discovers relevant and irrelevant topics for users, and then enriches an individual user model with collaboration from other similar users. In order to evaluate the performance of our model, we compare experimental results with a user model based on collaborative filtering approaches and a vector space model. The experimental results have shown the proposed model provides a better representation in user interests and achieves better recommendation results in terms of accuracy and ranking.

Dynamic voltage scaling on MPEG decoding

A number of research efforts have been devoted to reduce energy consumption of a processor without impacting the performance through the use of dynamic voltage scaling (DVS). The paper presents two DVS algorithms on MPEG decoding. One is DVS with delay and drop rate minimizing algorithm (DVS-DM) where voltage is determined based on previous workload only. Another algorithm scales the supply voltage according to the predicted MPEG decoding time and previous workload (DVS with predicted decoding time or DVS-PD). Simulation results show that DVS-PD improves energy efficiency as much as 56% compared to the conventional shutdown algorithm. We also found that the amount of energy saving with DVS-PD is not affected by the fluctuation of the movie stream, but is related to the error rate of the predictor, which implies that if decoding time is predicted more accurately, the DVS algorithm can be more efficient.

Collaborative user modeling for enhanced content filtering in recommender systems

Recommender systems, which have emerged in response to the problem of information overload, provide users with recommendations of content suited to their needs. To provide proper recommendations to users, personalized recommender systems require accurate user models of characteristics, preferences and needs. In this study, we propose a collaborative approach to user modeling for enhancing personalized recommendations to users. Our approach first discovers useful and meaningful user patterns, and then enriches the personal model with collaboration from other similar users. In order to evaluate the performance of our approach, we compare experimental results with those of a probabilistic learning model, a user model based on collaborative filtering approaches, and a vector space model. We present experimental results that show how our model performs better than existing alternatives.

Location-based service with context data for a restaurant recommendation

Utilizing Global Positioning System (GPS) technology, it is possible to find and recommend restaurants for users operating mobile devices. For recommending restaurants, Personal Digital Assistants or cellular phones only consider the location of restaurants. However, a users background and environment information is assumed to be directly related to recommendation quality. In this paper, therefore, a recommender system using context information and a decision tree model for efficient recommendation is presented. This system considers location context, personal context, environment context, and user preference. Restaurant lists are obtained from location context, personal context, and environment context using the decision tree model. In addition, a weight value is used for reflecting user preferences. Finally, the system recommends appropriate restaurants to the mobile user. For this experiment, performance was verified using measurements such as k-fold cross-validation and Mean Absolute Error. As a result, the proposed system obtained an improvement in recommendation performance.

Folksonomy link prediction based on a tripartite graph for tag recommendation

Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy development from a graph-based perspective. The proposed approach adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. We model a folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide tag recommendations for individual users. We evaluate our method on two real-world folksonomies collected from CiteULike and Last.fm. The experimental results demonstrate that the proposed method improves the recommendation performance and is effective for both active taggers and cold-start taggers compared to existing algorithms.

Leveraging personal photos to inferring friendships in social network services

Social network services have become widely used as an important tool to share rich information; in such networks, making new friends is the most basic functionality to enable users to take advantage of their social networks. In this paper we look into personal photos as an additional source for social network analysis and analyze the potential of people tags in the photos for friend recommendations. We also propose a new compact data structure, collectively called Face Co-Occurrence Networks (FCON), which stores crucial and quantitative information about peoples appearance in photos. We discover strong associative relationships among people and recommend reliable social friends by utilizing FCON. Experimental results demonstrate the effectiveness and efficiency of our method for recommending friends in social network services.

Folksonomy-based personalized search and ranking in social media services

In recent years, social Web users have been overwhelmed by the huge numbers of social media available. Consequentially, users have trouble finding social media suited to their needs. To help such users retrieve useful social media content, we propose a new model of tag-based personalized searches to enhance not only retrieval accuracy but also retrieval coverage. By leveraging social tagging as a preference indicator, we build two models: (i) a latent tag preference model that reflects how a certain user has assigned tags similar to a given tag and (ii) a latent tag annotation model that captures how users have tagged a certain tag to resources similar to a given resource. We then seamlessly map the tags onto items, depending on an individual users query, to find the most desirable content relevant to the users needs. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the art algorithms and show our methods feasibility for personalized searches in social media services.