• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.82-88
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• 2017

Recently recommendation methods need personalization and diversity as well as accuracy whereas the traditional researches have been mainly focused on the accuracy of recommendation in terms of quality. The diversity of recommendation is also important to people in terms of quantity in addition to quality since people's desire for content consumption have been stronger rapidly than past. In this paper, we pay attention to similarity of data gathered simultaneously among different types of contents. With this motivation, we propose an enhanced recommendation method using correlation analysis with considering data similarity between two types of contents which are movie and music. Specifically, we regard folksonomy tags for music as correlated data of genres for movie even though they are different attributes depend on their contents. That is, we make result of new recommendation movie items through mapping music folksonomy tags to movie genres in addition to the recommendation items from the typical collaborative filtering. We evaluate effectiveness of our method by experiments with real data set. As the result of experimentation, we found that the diversity of recommendation could be extended by considering data similarity between music contents and movie contents.

• The KIPS Transactions:PartB
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• v.18B no.5
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• pp.279-290
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• 2011

As the volume of internet and web contents have shown an explosive growth in recent years, lately contents recommendation system (CRS) has emerged as an important issue. Consequently, researches on contents recommendation method (CRM) for CRS have been conducted consistently. However, traditional CRMs have the limitations in that they are incapable of utilizing in web 2.0 environments where positions of content creators are important. In this paper, we suggest a novel way to recommend web contents of high quality using both degree of centrality and TF-IDF. For this purpose, we analyze TF-IDF and degree of centrality after collecting RSS and FOAF. Then we recommend contents using these two analyzed values. For the verification of the suggested method, we have developed the CRS and showed the results of contents recommendation. With the suggested idea we can analyze relations between users and contents on the entered query, and can consequently provide the appropriate contents to the user. Moreover, the implemented system we suggested in this paper can provide more reliable contents than traditional CRS because the importance of the role of content creators is reflected in the new system.

• The Journal of the Korea Contents Association
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• v.6 no.5
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• pp.1-8
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• 2006

In ubiquitous environments, recommendation service based on multiple contexts is required. The total amount of information is larger due to the greater number of contexts in multiple context environments. This paper proposes a new effective recommendation method adaptive to multiple contexts in ubiquitous environments. A new method of recommendations in multiple context environments is suggested that uses user's preferences and behavior as a weighting factor. This paper describes the recommendation method, scenario and the experimental results. The results verify that the proposed method's recommendation performance is better than other existing method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.1-9
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• 2010

The increasing popularity of mobile devices, such as cellular phones, smart phones, and PDAs, has fostered the need to recommend more effective information in ubiquitous environments. We propose the recommendation method for mobile contents service using contexts and prefetching in ubiquitous environment. The proposed method enables to find some relevant information to specific user's contexts and computing system contexts. The prefetching has been applied to recommend to user more effectively. Our proposed method makes more effective information recommendation. The proposed method is conceptually comprised of three main tasks. The first task is to build a prefetching zone based on user's current contexts. The second task is to extract candidate information for each user's contexts. The final task is prefetch the information considering mobile device's resource. We describe a new recommendation.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.35-48
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• 2017

There are many and various devices like sensors, displays, smart phone, etc. in smart environment. And contents can be provided by using these devices. Vast amounts of contents are provided to users, but in most environments, there are no regard for user or some simple elements like location and time are regarded. So there's a limit to provide meaningful contents to users. In this paper, I suggest the contents recommendation system that can recommend contents to users by reasoning context of users, devices and contents. The contents recommendation system suggested in this paper recommend the contents by calculating the user preferences using the situation reasoned with the contextual data acquired from various devices and the user profile received from the user directly. To organize this process, the method on how to model ontology with domain knowledge and how to design and develop the contents recommendation system are discussed in this paper. And an application of the contents recommendation system in Centum City, Busan is introduced. Then, the evaluation methods how the contents recommendation system is evaluated are explained. The evaluation result shows that the mean absolute error is 0.8730, which shows the excellent performance of the proposed contents recommendation system.

• Journal of the Korean Society for information Management
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• v.26 no.1
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• pp.81-105
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• 2009

In this paper, we propose user contents using behavior and location information on contents on various channels, such as web, IPTV, for contents distribution. With methods to build user and contents profiles, contents using behavior as an implicit user feedback was applied into machine learning procedure for updating user profiles and contents preference. In machine learning procedure, contents-based and collaborative filtering methods were used to analyze user's contents preference. This study proposes contents location information on web sites for final recommendation contents as well. Finally, we refer to a generalized recommender system for personalization. With those methods, more effective and accurate recommendation service can be possible.

• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1242-1250
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• 2022

When recommending cold items that do not have user-item interactions to users, even we adopt state-of-the-arts algorithms, the predicted information of cold items tends to have lower accuracy compared to warm items which have enough user-item interactions. The lack of information makes for recommender systems to recommend monotonic items which have a few top popular contents matched to user preferences. As a result, under-diversified items have a negative impact on not only recommendation diversity but also on recommendation accuracy when recommending cold items. To address the problem, we adopt a diversification algorithm which tries to make distributions of accumulated contents embedding of the two items groups, recommended items and the items in the target user's already interacted items, similar. Evaluation on a real world data set CiteULike shows that the proposed method improves not only the diversity but also the accuracy of cold item recommendation.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.6
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• pp.11-24
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• 2019

The tendency of buyers of web information is changing from the cost-effectiveness to the cost-satisfaction. There is such tendency in the recommendation of multimedia contents, some of which are folksonomy-based recommendation services using mood. However, there is a problem that they does not consider synonyms. In order to solve this problem, some studies have solved the problem by defining 12 moods of Thayer model as AV values (Arousal and Valence), but the recommendation performance is lower than that of a keyword-based method at the recall level 0.1. In this paper, we propose a method based on using mood vector of multimedia contents. The method can solve the synonym problem while maintaining the same performance as the keyword-based method even at the recall level 0.1. Also, for performance analysis, we compare the proposed method with an existing method based on AV value and a keyword-based method. The result shows that the proposed method outperform the existing methods.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1013-1023
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• 2019

In content consumption environment with various live TV channels, VoD contents and web contents, recommendation service is now a necessity, not an option. Currently, various kinds of recommendation services are provided in the OTT service or the IPTV service, such as recommending popular contents or recommending related contents which similar to the content watched by the user. However, in the case of a content viewing environment through TV or IPTV which shares one TV and a TV set-top box, it is difficult to recommend proper content to a specific user because one or more usage histories are accumulated in one subscription information. To solve this problem, this paper interprets the concept of family as {user, time}, extends the existing recommendation relationship defined as {user, content} to {user, time, content} and proposes a method based on deep learning algorithm. Through the proposed method, we evaluate the recommendation performance qualitatively and quantitatively, and verify that our proposed model is improved in recommendation accuracy compared with the conventional method.

• The Journal of the Korea Contents Association
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• v.5 no.4
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• pp.36-44
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• 2005

Recommender services are being used by an ever-increasing number of mobile commerce applications to help consumers find items to purchase with the use of the situated contexts. In this paper, we propose a new leveled recommendation for context-aware mobile commerce. This enables a consumer to obtain relevant information efficiently by using leveled recommendation, patterns and prefetching. This paper describes the method and application scenarios. Several experiments are performed and the results verify that the proposed method's recommendation performance is better than other existing methods.