• Journal of the Korean Data and Information Science Society
• /
• v.17 no.3
• /
• pp.717-726
• /
• 2006

This study is to investigate the MAE of prediction value by collaborative filtering algorithm originated by GroupLens and improved algorithm. To decrease the MAE on the collaborative recommender system on user based, this research proposes the improved algorithm, which reduces the possibility of over estimation of active user's preference mean collaboratively using other user’s preference mean. The result shows the MAE of prediction by improved algorithm is better than original algorithm, so the active user's preference mean used in prediction formula is possibly over estimated.

• Proceedings of the Korea Society of Design Studies Conference
• /
• 2005.10a
• /
• pp.198-199
• /
• 2005

• Proceedings of the IEEK Conference
• /
• summer
• /
• pp.395-399
• /
• 2004

We present modality conversion as an effective means for QoS management. We show that modality conversion, in combination with content scaling, would give a wider range of adaptation to support QoS at media level. Here, we consider modality conversion with respect to resource constraint and human factor. To represent modality conversion as well as content scaling, we present the overlapped content value (OCV) model that relates the content value of different modalities with resources. The specification of user preference on modality conversion is divided into qualitative and quantitative levels. The user preference is then integrated into the OCV model so that modality conversion correctly reflects the user's wishes. For the conversion of multiple contents, an optimization problem is formulated and solved by dynamic programming. The experiments show that the proposed approach is efficient to be applied in practice.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.13 no.5
• /
• pp.526-529
• /
• 2003

Recommender systems or collaborative filtering are methods to identify potentially interesting or valuable items to a particular user Under the assumption that people with similar interest tend to like the similar types of items, these methods use a database on the preference of a set of users and predict the rating on the items that the user has not rated. Usually the preference of a particular user is liable to vary with time and this temporal variation may cause an inaccurate identification and prediction. In this paper we propose a method to adapt the temporal variation of the user preference in order to improve the predictive performance of a collaborative filtering algorithm. To be more specific, the correlation weight of the GroupLens system which is a general formulation of statistical collaborative filtering algorithm is modified to reflect only recent similarity between two user. The proposed method is evaluated for EachMovie dataset and shows much better prediction results compared with GrouPLens system.

• Journal of the Korean Society for information Management
• /
• v.39 no.3
• /
• pp.311-336
• /
• 2022

This study analyzed the contents of Goodreads review data, which is a social cataloging service with the participation of book users around the world, to identify the preference factors that affect book users' book recommendations in the library information service environment. To understand user preferences from a more detailed point of view, sub-datasets for each rating group, each book, and each user were constructed in the sample selection process. Stratified sampling was also performed based on the result of topic modeling of review text data to include various topics. As a result, a total of 90 preference factors belonging to 7 categories('Content', 'Character', 'Writing', 'Reading', 'Author', 'Story', 'Form') were identified. Also, the general preference factors revealed according to the ratings, as well as the patterns of preference factors revealed in books and users with clear likes and dislikes were identified. The results of this study are expected to contribute to more sophisticated recommendations in future recommendation systems by identifying specific aspects of user preference factors.

• 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.

• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.701-702
• /
• 2008

A large amount of efforts are required to search user's preferred contents for the program contents being provided by IPTV services. In this paper, using collaborative filtering, an automatic recommendation method of IPTV program contents is presented by reasoning similar group preferences on IPTV program contents which constitutes personalized IPTV environments. The proposed method models the user's preference of IPTV program contents with the program attributes such as content, genres, channels actor/actress, staffs and calculates it using the watching history of program contents in different genres and watching times. Also, the proposed method considers timely changing user's preference and the preference oon the content itself, which improves the traditional collaborative filtering methods that can not recommend the non-consumed items.

• Journal of Broadcast Engineering
• /
• v.8 no.4
• /
• pp.339-350
• /
• 2003

The Part 7 of MPEG-21, called Digital Item Adaptation, aims at an interoperable transparent access of multimedia contents in heterogeneous environments. This standard facilitates the development of Universal Multimedia Access (UMA) systems, which adapt the rich multimedia contents to provide user the best possible presentation under the constraints of various terminals and network connections. Content adaptation has two major aspects: one is modality conversion that converts content from one modality (e.g. video) to different modalities (e.g. image) the other is content scaling that changes the titrates (or qualities) of the contents without converting their modalities. At the output of adaptation process, the highly-subjective qualities of adapted contents nay vary widely with respect to point-of-views of different providers and different users. So, user should have some control on the adaptation process. In this paper, we describe two description tools of user characteristics, the presentation priority preference and the modality conversion preference, which allow user to have flexible choices on the qualities and modalities of output contents. We also present a systematic approach to integrate these user preferences into the adaptation process. These description tools are developed in the process of MPEG-21 standardization.

• Journal of Broadcast Engineering
• /
• v.24 no.3
• /
• pp.515-526
• /
• 2019

In the real world, the user's preference for a particular product is determined by many factors besides the quality of the product. The reflection of these external factors was very difficult because of various fundamental problems including lack of data. However, access to external factors has become easier as the infrastructure for public data is opened and the availability of evaluation platforms with diverse and vast amounts of data. In accordance with these changes, this paper proposes a recommendation system structure that can reflect the collectable factors that affect user's preference, and we try to observe the influence of actual influencing factors on preference by applying case. The structure of the proposed system can be divided into a process of selecting and extracting influencing factors, a process of supplementing insufficient data using sentence analysis, and finally a process of combining and merging user's evaluation data and influencing factors. We also propose a validation process that can determine the appropriateness of the setting of the structural variables such as the selection of the influence factors through comparison between the result group of the proposed system and the actual user preference group.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2006.06a
• /
• pp.344-353
• /
• 2006

Nowadays many people participate in online communities for information sharing. But most recommender systems are designed for personalization of individual user, so it is necessary to develop a recommendation procedure for group users, such as participants in online communities. This paper proposes a group recommender system to recommend books for group users in online communities. For such a purpose, we suggest a group recommendation procedure consisting of two phases. The first phase is to generate recommendation list for 'big user' using collaborative filtering, and the second phase is to remove irrelevant books among previous list reflecting the preference of each individual user. The procedure is explained step by step with an illustrative example. And this procedure can potentially be applied to other domains, such as music, movies and etc.