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A social recommendation method based on the integration of social relationship and product popularity
(2019-01-01)
Lai, Chin-Hui
;
Lee, Shin-Jye
;
Huang, Hung-Ling
;
科技管理研究所
;
Institute of Management of Technology
Web 2.0 technology fosters the flourishing growth and development of social networks. More and more people are participating in the activities on social networks to interact and share information with each other. Thus, consumers are often making their purchasing decisions based on information from the Internet such as reviews, ratings, and comments on products, especially from their trusted friends. However, a great amount of available information may cause the problem of information overload for consumers. In seeking to attain a good recommendation performance by taking the high-potential factors into account as far as possible, this paper proposes a novel social recommendation method on the basis of the integration of interactions, trust relationships and product popularity to predict user preferences, and recommend relevant products in social networks. In addition, the proposed method mainly focuses on analyzing user interactions to infer their latent interactions in accordance with the user ratings and corresponding reviews. Additionally, users may be affected by the popularity of products, so this factor has also been taken into consideration in this work. The experimental results show that the proposed recommendation method has a better recommendation performance in comparisons to other methods because the proposed method can accurately analyze user preferences and further recommend high-potential products to target users in social networks to support their purchase decision making. Furthermore, the proposed method can not only reduce the time and effort users spend on querying information, but also positively relieve the problem of information overload.
Group Recommendation Based on the Analysis of Group Influence and Review Content
(2017-01-01)
Lai, Chin-Hui
;
Hong, Pei-Ru
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
With the development of internet, users not only receive information passively but also share their own opinions on the social networking websites. Accordingly, users' preferences for items may be affected by others through opinion sharing and social interactions. Moreover, users with similar preferences usually form a group to share related information with others. Users' preferences may be affected by group members. Existing researches often focus on analyzing personal preferences and group recommendation approaches without user influence. In this work, we propose a novel group recommendation approach which combines the group influence, rating-based score and profile similarity to predict group preference. The group influence is composed of group member influences, review influence and recommendation influence. The profile similarity is derived from the analysis of item descriptions and review content. The experimental results show that considering the group influence and content information in group recommendation approach can effectively improve the recommendation performance.
Document Recommendation with Implicit Feedback Based on Matrix Factorization and Topic Model
(2018-01-01)
Lai, Chin-Hui
;
Liu, Duen-Ren
;
Lin, Siao-Rong
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Recommender systems have been applied in many domains to solve the information-overload problem, and most of them make recommendations based on explicit data which expressed ratings in different scores. However, there are a lot of implicit data in the real world, such as users' purchase history, click history, browsing activity and so on, and it is difficult to find users' preferences based on this kind of data. In this work, we proposed a novel recommendation method, which incorporates topic model and matrix factorization. The content of documents and similar users' preferences are used to predict the negative and positive examples. The proposed approach achieves better performance than other recommender systems with implicit feedback.
Image Classification Based on the Boost Convolutional Neural Network
(2018-01-01)
Lee, Shin-Jye
;
Chen, Tonglin
;
Yu, Lun
;
Lai, Chin-Hui
;
科技管理研究所
Convolutional neural networks (CNNs), which are composed of multiple processing layers to learn the representations of data with multiple abstract levels, are the most successful machine learning models in recent years. However, these models can have millions of parameters and many layers, which are difficult to train, and sometimes several days or weeks are required to tune the parameters. Within this paper, we present the usage of a trained deep convolutional neural network model to extract the features of the images, and then, used the AdaBoost algorithm to assemble the Softmax classifiers into recognizable images. This method resulted in a 3% increase of accuracy of the trained CNN models, and dramatically reduced the retraining time cost, and thus, it has good application prospects.
A Trustworthy QoS-based Mechanism for Web Service Discovery Based on Collaborative Filtering
(2013)
Lin, Szu-Yin
;
Lai, Chin-Hui
;
Wu, Chih-Heng
;
Lo, Chi-Chun
;
資訊管理與財務金融系註：原資管所+財金所
Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. U sing the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services\' Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.
RECOMMENDING DOCUMENTS VIA KNOWLEDGE FLOW-BASED GROUP RECOMMENDATION
(2011)
Lai, Chin-Hui
;
Liu, Duen-Ren
;
Chen, Ya-Ting
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Recommender systems can mitigate the information overload problem and help workers retrieve knowledge based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task related codified knowledge (documents) to perform tasks. A worker\'s document referencing behaviour can be modelled as a knowledge flow (KF) to represent the evolution of his/her information needs over time. Document recommendation methods can proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. However, most traditional recommendation methods do not consider workers\' knowledge flows and the information needs of the majority of a group of workers with similar knowledge flows. A group\'s needs may partially reflect the needs of an individual worker that cannot be inferred from his/her past referencing behaviour. Thus, we leverage the group perspective to complement the personal perspective by using a hybrid approach, which combines the KF-based group recommendation method (KFGR) with the user-based collaborative filtering method (UCF). The proposed hybrid method achieves a trade-off between the group-based and the personalized method by integrating the merits of both methods. Our experiment results show that the proposed method can enhance the quality of recommendations made by traditional methods.
Recommendations based on personalized tendency for different aspects of influences in social media
(2015-12-01)
Lai, Chin-Hui
;
Liu, Duen-Ren
;
Liu, Mei-Lan
;
交大名義發表
;
National Chiao Tung University
Among the applications of Web 2.0, social networking sites continue to proliferate and the volume of content keeps growing; as a result, information overload causes difficulty for users attempting to choose useful and relevant information. To resolve this problem, most researches only utilize users\' preferences, the content of items or social influence to make recommendations. However, people\'s preferences for items may be affected by social friends, personal interest and item popularity. Moreover, each factor has a different impact on each user. In this work, we propose a novel recommendation method based on different types of influences: social, interest and popularity, using personal tendencies in regard to these factors to recommend photos in a photo-sharing website, Flickr. The personal tendencies related to these three influences are regarded as personalized weights to combine influence scores for predicting the scores of items. The experimental results show that our proposed methods can improve the quality of recommendations.
Mining group-based knowledge flows for sharing task knowledge
(2011-01-01)
Liu, Duen-Ren
;
Lai, Chin-Hui
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
In an organization, knowledge is the most important resource in the creation of core competitive advantages. It is circulated and accumulated by knowledge flows (KFs) in the organization to support workers' task needs. Because workers accumulate knowledge of different domains, they may cooperate and participate in several task-based groups to satisfy their needs. In this paper, we propose algorithms that integrate information retrieval and data mining techniques to mine and construct group-based KFs (GKFs) for task-based groups. A GKF is expressed as a directed knowledge graph which represents the knowledge referencing behavior, or knowledge flow, of a group of workers with similar task needs. Task-related knowledge topics and their relationships (flows) can be identified from the knowledge graph so as to fulfill workers' task needs and promote knowledge sharing for collaboration of group members. Moreover, the frequent knowledge referencing path can be identified from the knowledge graph to indicate the frequent knowledge flow of the workers. To demonstrate the efficacy of the proposed methods, we implement a prototype of the GKF mining system. Our GKF mining methods can enhance organizational learning and facilitate knowledge management, sharing, and reuse in an environment where collaboration and teamwork are essential. (C) 2010 Elsevier B.V. All rights reserved.
A trustworthy QoS-based collaborative filtering approach for web service discovery
(2014-07-01)
Lin, Szu-Yin
;
Lai, Chin-Hui
;
Wu, Chih-Heng
;
Lo, Chi-Chun
;
資訊管理與財務金融系註：原資管所+財金所
Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services\' Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality. (C) 2014 Elsevier Inc. All rights reserved.
Novel personal and group-based trust models in collaborative filtering for document recommendation
(2013-08-01)
Lai, Chin-Hui
;
Liu, Duen-Ren
;
Lin, Cai-Sin
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Collaborative filtering (CF) recommender systems have been used in various application domains to solve the information-overload problem. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques in order to improve recommendation quality. Some researchers have proposed rating-based trust models to derive trust values based on users' past ratings of items, or based on explicitly specified relations (e.g. friends) or trust relationships; however, the rating-based trust model may not be effective in CF recommendations due to unreliable trust values derived from very few past rating records. In this work, we propose a hybrid personal trust model which adaptively combines the rating-based trust model and explicit trust metric to resolve the drawback caused by insufficient past rating records. Moreover, users with similar preferences usually form a group to share items (knowledge) with each other; thus, users' preferences may be affected by group members. Accordingly, group trust can enhance personal trust to support recommendations from the group perspective. We then propose a recommendation method based on a hybrid model of personal and group trust to improve recommendation performance. The experimental results show that the proposed models can improve the prediction accuracy of other trust-based recommender systems. (C) 2013 Elsevier Inc. All rights reserved.

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