• Journal of KIISE:Software and Applications
• /
• v.31 no.8
• /
• pp.1062-1072
• /
• 2004

Collaborative filtering systems based on ｛user-document｝ matrix are effective in recommending web documents to user. But they have a shortcoming of decreasing the accuracy of recommendations by the first rater problem and the sparsity. This paper proposes the automatic preference rating method that generates user profile to solve the shortcoming. The profile in this paper is content-based collaborative user profile. The content-based collaborative user profile is generated by combining a content-based user profile with a collaborative user profile by mutual information method. Collaborative user profile is based on ｛user-document｝ matrix in collaborative filtering system, thus, content-based user profile is generated by relevance feedback in content-based filtering systems. After normalizing combined content-based collaborative user profiles, it automatically rates user preference by reflecting normalized profile in ｛user-document｝matrix of collaborative filtering systems. We evaluated our method on a large database of user ratings for web document and it was certified that was more efficient than existent methods.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.4
• /
• pp.270-275
• /
• 2008

Under ubiquitous environment, recommendation system is using the collaborative filtering methods by quantifying context information, but insufficient context information can cause inaccurate recommendation result. In order to solve such problems, the researcher used context information and user's profile. But service history information in users' profiles can have the problems of being influenced by change of the user's taste or fashion as time passes by. In addition, context information and user's profile can't be properly inter-locked according to situation, which can cause inaccurate predictability. In this paper, in case a user's taste or fashion is changed as time passes by, the researcher didn't apply bundled-up value to the user's profile but applied different weight according to change of time. And the researcher could solve the problem that context information and a user's profile can't be properly inter-locked according to situation by applying different weight to the result gained by means of collaborative filtering and then by unifying it. In such ways, the researcher could improve predictability.

• Journal of Korea Multimedia Society
• /
• v.13 no.10
• /
• pp.1453-1462
• /
• 2010

In this paper, a new program recommendation system is proposed to recommend user preferred VOD program in IPTV environment. A proposed system is implemented with collaborative filtering method. For a user profile which describes user program preference, a program preference, sub-genre preference, and US(user similarity) weight of the user neighborhood is averaged and updated every week. In order to evaluate system performance, real 24-weeks cable TV watching data provided by Nilson Research Corp. are modified to fit for IPTV broadcasting environment and the simulation result shows quite comparative quality of recommendation. The experimental results optimum performance when user similarity based weighting, five person per group and five recommendation programs are used.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.1
• /
• pp.446-465
• /
• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• The KIPS Transactions:PartD
• /
• v.15D no.4
• /
• pp.463-474
• /
• 2008

The advancement of information technology and the popularization of Internet has explosively increased the amount of multimedia contents. Therefore, the requirement of multimedia recommendation to satisfy a user's needs increases fastly. Up to now, CF is used to recommend general items and multimedia contents. However, general CF doesn't reflect visual characteristics of image contents so that it can't be adaptable to image recommendation. Besides, it has limitations in new item recommendation, the sparsity problem, and dynamic change of user preference. In this paper, we present new image recommendation method FBCF (Feature Based Collaborative Filtering) to resolve such problems. FBCF builds new user profile by clustering visual features in terms of user preference, and reflects user's current preference to recommendation by using preference feedback. Experimental result using real mobile images demonstrate that FBCF outperforms conventional CF by 400% in terms of recommendation ratio.

• Journal of information and communication convergence engineering
• /
• v.22 no.1
• /
• pp.7-13
• /
• 2024

User preferences and ratings may be anticipated by recommendation systems, which are widely used in social networking, online shopping, healthcare, and even energy efficiency. Constructing trustworthy recommender systems for various applications, requires the analysis and mining of vast quantities of user data, including demographics. This study focuses on holding elections with vague voter and candidate preferences. Collaborative user ratings are used by filtering algorithms to provide suggestions. To avoid information overload, consumers are directed towards items that they are more likely to prefer based on the profile data used by recommender systems. Better interactions between governments, residents, and businesses may result from studies on recommender systems that facilitate the use of e-government services. To broaden people's access to the democratic process, the concept of "e-democracy" applies new media technologies. This study provides a framework for an electronic voting advisory system that uses machine learning.

• The Journal of the Korea Contents Association
• /
• v.13 no.11
• /
• pp.11-20
• /
• 2013

Recently, as SNS services have been increased, studies on recommendation schemes have been actively done. Recommendation scheme provides various favorable or needed services with users on real time. Group recommendation provides users with suitable groups based on their preference. In this paper, we propose a new group recommendation scheme considering user profiles and collaborative filtering in social networks. The proposed scheme can solve the problems of the static profile based group recommendation scheme because it collects the recent group activities and updates user profiles. It also recommends the more various groups by reflecting the similar tendencies of other users within a group through collaborative filtering. Our experimental results show that the proposed scheme recommends various groups that significantly considers the user's changing preferences compared to the existing scheme.

• Journal of KIISE:Computing Practices and Letters
• /
• v.7 no.6
• /
• pp.684-695
• /
• 2001

Recent recommender system uses a method of combining collaborative filtering system and content based filtering system in order to solve sparsity and first rater problem in collaborative filtering system. Collaborative filtering systems use a database about user preferences to predict additional topics. Content based filtering systems provide recommendations by matching user interests with topic attributes. In this paper, we describe a method for discovery of user preference through combining two techniques for recommendation that allows the application of machine learning algorithm. The proposed collaborative filtering method clusters user using genetic algorithm based on items categorized by Naive Bayes classifier and the content based filtering method builds user profile through extracting user interest using relevance feedback. We evaluate our method on a large database of user ratings for web document and it significantly outperforms previously proposed methods.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.47 no.4
• /
• pp.9-19
• /
• 2010

In this paper, a new program recommendation system is proposed to recommend user preferred VOD program in IPTV environment. A proposed system is implemented with hybrid filtering method that can cooperatively complements the shortcomings of the content-based filtering and collaborative filtering. For a user program preference, a single-scaled measure is designed so that the recommendation performance between content-based filtering and collaborative filtering is easily compared and reflected to final hybrid filtering procedure. In order to provide more accurate program recommendation, we use not only the user watching history, but also the user program preference and sub-genre program preference updated every week as a user preference profile. System performance is evaluated with modified IPTV data from real 24-weeks cable TV watching data provided by Nilson Research Corp. and it shows quite comparative quality of recommendation.

• The KIPS Transactions:PartD
• /
• v.9D no.6
• /
• pp.1009-1016
• /
• 2002

We design and implement a new case-based recommender system using implicit rating information for a digital content site. Our system consists of the User Profile Generation module, the Similarity Evaluation and Recommendation module, and the Personalized Mailing module. In the User Profile Generation Module, we define intra-attribute and inter-attribute weight deriver from own's past interests of a user stored in the access logs to extract individual preferences for a content. A new similarity function is presented in the Similarity Evaluation and Recommendation Module to estimate similarities between new items set and the user profile. The Personalized Mailing Module sends individual recommended mails that are transformed into platform-independent XML document format to users. To verify the efficiency of our system, we have performed experimental comparisons between the proposed model and the collaborative filtering technique by mean absolute error (MAE) and receiver operating characteristic (ROC) values. The results show that the proposed model is more efficient than the traditional collaborative filtering technique.