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Copyright of Journal of Management & Systems is the property of Business Administration Association of R.O.C. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the expansion of academic information, scholars face a big challenge of efficiently selecting valid information in the era of big academic data. A venue recommendation system is one of the main ways to assist scholars in solving the information overload problem. This paper focuses on the issue of how to fit appropriate academic journals for manuscripts efficiently. It extracts diverse academic entities and edges from academic data for constructing academic heterogeneous information networks. This paper proposes a novel method for venue recommendation (SCVR) . Firstly, the topic information is extracted from the abstracts and topics by LDA and guides different types of nodes to map to the multi-topic feature space. Then, the meta-path contextual information is aggregated to the target node, forming a multi-topic node representation. Finally, the node representations from multiple mate-paths are combined into the final multi- topic node representations. SCVR learns the multi- topic node representations with paper content and network structure to venue recommendation.Experiments on two real academic datasets show that a heterogeneous information network recommendation incorporating article topics can effectively improve the performance of the venue recommendation. Compared with the current heterogeneous information network recommendation and traditional venue recommendation, the performance of SCVR has improved by an average of 2.7% and 19%, which indicates that SCVR has better performance in the area of venue recommendation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A heterogeneous information search recommendation algorithm based on multi feature fusion is proposed. The knowledge map technology is used to extract heterogeneous information features, select multi view machine model, learn and integrate local information of multi-feature heterogeneous information by using cooperative attention mechanism, get the importance vector of information through normalization of softmax function, and integrate all local information into the final node, obtain the score function. It uses the corresponding meta path interaction between users and commodities to obtain the global information recommendation optimization objective function of multiple label classification that is combined with the score function and the global information recommendation optimization objective function to obtain the final heterogeneous information search recommendation. The test results show that the algorithm can effectively recommend the information needed by users. The recommended complexity is low, and the normalized discount cumulative gain of searching for heterogeneous information is higher than 0.35, which has strong recommendation performance and can be applied to solve the problem of information overload. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, the recommendation system can handle the problem of information overload. Due to many problems in traditional recommendation systems, they can' t handle the discovery of hidden information. This paper proposed an adaptive graph convolution attention neural collaborative filtering recommendation model ( ANGCACF) . Firstly, graph convolution neural network obtained the user-item interaction figure and adaptively aggregated the user-item feature information. Secondly, the model added adaptive extended data to solve the data sparsity for the user-item feature information and used the attention mechanism to redistribute the weight to the user-item feature information and the adaptive extended data. Finally, it obtained the final recommendation result by using the algorithm framework of collaborative filtering based on matrix decomposition. Experiments on MovieLens-lM, MovieLens-lOOK and Amazon-book show that the algorithm is superior to the baseline method in five indexes : precision, recall, Mrr, hit and NDCG. [ABSTRACT FROM AUTHOR]

Recommender systems can effectively alleviate the problem of information overload caused by the rapid development of the Internet. However, the occurrence of shilling attacks restricts the healthy development of recommender systems. Therefore, how to detect shilling attacks accurately and efficiently is an important problem in the field of recommender systems security. The existing detection methods usually design hand-crafted detection features based on expert knowledge or automatically learn features from a single perspective using deep learning, then the attack users are identified according to the extracted features by hard classification, resulting in the poor detection performance. By automatically learning features from multiple perspectives and introducing a hesitant fuzzy decision, a novel detection method based on CNN and hesitant fuzzy set was proposed and named CNN-HFS. Firstly, for each user, three behavior matrices were extracted from the perspectives of rating, preference and rating time, respectively. To reduce the influence of data sparse, these matrices were scaled by bicubic interpolation to correspondingly obtain a dense rating matrix, a dense preference matrix and a dense time matrix. Next, each scaling matrix of users was regarded as an image, and three different CNN classifiers were trained based on these scaling matrices in three different views respectively. For each user, three membership degrees to the classifier of attack users were calculated. Finally, a fuzzy hesitant set was introduced to make a comprehensive decision, and the attack users were identified according to the decision results. To validate the effectiveness of the proposed CNN-HFS, the extensive experiments were conducted on the MovieLens 1M and Amazon datasets. The evaluation metrics of precision, recall and F1-measure were used to compare the proposed method with SVM-TIA, CoDetector, CNN-SAD, SDAEs-PCA, CNN-R, CNN-P and CNN-T. The experimental results showed that the proposed method is superior to seven baseline methods in terms of three detection metrics and achieves an excellent detection performance under various attacks. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of the internet, how to mine and analyze massive network information has become a recognized hot and difficult problem. Among them, the recommendation system can provide users with accurate and fast business (commodities, projects, services, etc.) information, which is the common interest and research hotspot of industry and academia in recent years. A recommendation system can help users to solve the problem of information overload when there is no clear demand or a large amount of information. However, at present, the types of data are diverse and the application scenarios are extensive. When faced with this situation, the recommendation system also encounters challenges such as cold start and sparse matrix. Deep learning is an important research field and the most important branch of machine learning. In recent years, deep learning has developed rapidly. Researchers have made great breakthroughs and achievements in speech recognition, image processing, natural language processing and other fields by using deep learning. At present, deep learning has also been favored by a large number of researchers in the field of recommendation and has become a new direction. Incorporating deep learning technology into the recommendation method can effectively solve the problems of cold start and sparse matrix in traditional recommendation systems, and improve the performance and recommendation accuracy of the recommendation system. 河北科技大学学报 2020年 第1期 周万珍，等:推荐系统研究综述 This paper mainly summarizes the application of traditional recommendation methods and the application of neural network in current deep learning technology in recommendation methods, among which the traditional recommendation methods can be divided into the following three categories: 1) Content-based recommendation methods is mainly based on the feature information between the user and the project. The connection between users will not affect the recommendation result, so there is no problem of cold start and sparse matrix, but the content-based recommendation results are low in novelty and face the problem of feature extraction. 2) The collaborative filtering recommendation method is the most widely used method that does not require information about users or items, but only makes accurate recommendations based on the user's interactions with items such as clicks, views, and ratings. Although this method is simple and effective, sparse matrix and cold start problems will occur. 3) The hybrid recommendation method combines the characteristics of the first two traditional recommendation methods and can achieve good recommendation effect. However, this method still faces some challenges and difficulties in processing multi-source heterogeneous auxiliary information such as text and images.Recommendation methods based on deep learning are mainly classified according to neural network categories, which are divided into the following four categories: Recommendation methods based on deep neural network (DNN); recommendation methods based on convolutional neural network (CNN); recommended methods based on cyclic neural network (RNN) and long and short term memory neural network (LSTM); and recommended methods based on graph neural network (GNN). Incorporating deep learning technology into the recommendation field, the constructed model has the following five advantages: it has strong representation ability, and can directly extract the characteristics of users and items from the content; with strong anti-noise ability, it can easily process data with noise; in deep learning, cyclic neural network can model dynamic or sequential data; it can learn user or project characteristics more accurately; and deep learning facilitates the unified processing of data and can process large-scale data. Applying deep learning technology to the recommendation field can effectively overcome the challenges faced by traditional recommendation methods and improve the recommendation effect.

In order to improve the accuracy and quality of the personalized recommendation system, this paper constructed a collaborative filtering recommendation algorithm based on SVD and intuitionistic fuzzy clustering ( SVDIFC-CF) to address the information overload and data sparsity problems in traditional recommendation algorithms. The algorithm firstly introduced SVD to fill in the original matrix after dimensionality reduction. Then it used the user-item preference matrix to perform intuitionistic fuzzy clustering of users. Finally it calculated the top N users with the highest similarity to the target user, and found the items that users were most interested in as recommendation results. This paper used the Movie Lens and Jester datasets to verify the effectiveness of the algorithm. The experimental results show that compared with the traditional recommendation algorithm, this algorithm can effectively solve the problem of data sparseness and cold start, and improve the accuracy and quality of recommendation. [ABSTRACT FROM AUTHOR]

Recommender systems can effectively reduce the information overload by recommending items that users may be interested in. Euclidean-embedding-based collaborative filtering methods map users and items to a unified latent space, which is one of the most important methods to build a recommender system. However, traditional Euclidean-embedding-based collaborative filtering methods only consider the low-order interaction between user latent feature vectors and item latent feature vectors, and cannot efficiently model the complex interaction behavior between users and items in the real world. In this paper, we propose a deep-Euclidean-embedding-based collaborative filtering algorithm, which utilizes deep learning technology to learn the high-order and nonlinear interaction function between user latent feature vectors and item latent feature vectors. This can model the complex interaction behavior between users and items. Experimental results on real-world datasets show that our proposed algorithm outperforms traditional collaborative filtering algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the background of information overload, this is challenging to mine and organize meaningful concepts and their semantic connections from a set of related documents under the same topic in information extraction. Thus, this paper proposed a novel multi-document conceptual graph construction method based on open-domain information extraction. Firstly, documents we re ranked according to the improved TF-IDF weight of extracted topic words under the predefined topics, then the method relayed on a serious of methods, including conference resolution, weight computation, triple instance extraction steps, to extract numerous representative subject-predicate-object triples from multiple documents. For filtering out the noise of open domain information approach itself and improving the accuracy of information extraction, this paper presented a triple filtering algorithm to retain only the most salient, confident and compatible triples, which can form multiple conceptual subgraphs. Finally, in combined with the equivalent concepts and relationships across different subgraphs to connect into a fully connected conceptual graph. Experiments on signal media dataset illustrate that the proposed method has the capacity to discern key topic information corresponds to the specific topic within and across documents, and the formed conceptual graph achieves the good performance in terms of the cover age rate of topic concepts as well as the compatible triples. In actual circumstance, conceptual graph can be regarded as an important representation form of multiple documents and has the important significance for further exploring advance of the topic and generating automatic document abstraction. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The recommendation system is an important method to deal with the problem of information overload. The existing trust network-based recommendation algorithm does not fully mine the user trust relationship information and affects the recommendation effect. This paper proposed a model called CETrust, which took into account the direct trust and indirect trust among users and integrated the attribute information of the recommended project into the factorization of probability matrix. Experiments show that the recommendation accuracy of the newly proposed recommendation algorithm (H-CETrust) is higher than that of the current recommendation algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin University of Science & Technology is the property of Journal of Harbin University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a popular form of social media,micro-blog Web site provides rich information and services to users,at the same time it also brings the problem of information overload. How to use the microblogging network to recommend valuable information for users,to ease the problem of information overload,becomes increasingly important. According to the orientation,the randomness followed and other characteristics in the microblogging network,this paper proposed a microblogging network recommendation based on non-negative multiple matrix factorization,comprehensively considering the follow relationship among users,the repost relationship between users and micro-blog content,and the ownership relationship between micro-blog content and topic and other multi-source information. Experiment on Sina Weibo dataset for the micro-blog content recommendation,the results show that the method based on non-negative multiple matrix factorization,can effectively use the multidimensional information in microblogging network,significantly improve the accuracy of recommendation. This method can not only dig out the theme of micro-blog content,but also dig out the relationship among users,and can be extended to recommend friends and topics to users. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Management (0255-9838) is the property of Chinese Management Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Through the survey and research of the existing video sites，some video sites face the problem of information overload.So it is necessary for the video sites to have a recommender system.Through the study of the existing video recommender system,using open source cloud computing technologies—Hadoop,and some of its related components such as Hive and Hbase,a personalized video recommendation system based on cloud platform was designed.The system is only applicable to professional video-based website.

The Web has become the primary source of information for people when searching for suitable travel products before traveling. The explosive growth, variety of information available on the Web, and the rapid development of new e-business services (such as buying products and product comparisons) frequently overwhelm users, leading them to make poor decisions. Recommender systems have been proven to be a promising solution to the problem of information overload. Based on a review of overseas literature during the past ten years, we discussed the concept, application, and latest research progress of travel recommender systems. We differentiate the concepts of recommender systems and information retrieval systems (e.g., search engines) based on the criteria of individualized, interesting, and useful. Information retrieval systems often offer the same results for each request while recommender systems will consider personal needs and present different results. Some travel recommender systems such as Trip@dvice (ECTRL), tripmatcher (triplehop), and MePrint (VacationCoach) are widely used by destination organizations or e-business companies. To provide some references for the research on travel recommender systems, we ranked journals found in the Springer, ScienceDirect, EBSCO (Hospitality & Tourism Complete), and IEEE Explore databases from 1999 to 2013. Some famous academic conferences are recommended as well. We classified travel recommender systems according to the recommended technology, items, and devices used by recommender systems. Focusing on the key technologies applied in tourism recommender systems, the complexity and particularity were analyzed in the tourism and travel industry. Due to the limitations of traditional methods, such as collaborative filtering and content-based filtering, knowledge-based filtering and hybrid methods were more adopted for travel recommender systems. Some applications of travel decision theory--such as the travel destination choice model, travel decision style, and the behavior framework for destination recommendation systems design--were discussed to demonstrate the theory's importance in designing a good travel recommender system. A general framework for travel recommender systems which includes user profiling, recommend computing, and results presentation is presented. When designing a travel recommender system, the following factors should be considered: (1) The system can recommend a bundling of elementary components rather than a single destination or product. (2) Both short-term and long-term preferences must influence the recommendation. Short-term preferences should have greater weight than long-term preferences. (3) The cognitive effort that the user devotes to the information search should be reduced. More implicit methods should be used to elicit the user's preferences. (4) System bootstrapping without an initial memory of rating interactions should be allowed (Unregistered users can also receive useful recommendations). (5) Human/computer interaction such as asking/answering conversational mode or proposing/criticizing conversational mode should be supported. Future studies should focus on the following aspects: (1) More information and skills should be used for comprehensive understanding of users and items. (2) More contextual information should be considered to extend the traditional two-dimensional User x Item space. (3) Recommender systems will incorporate more multi-criteria rating information into the recommendation process to improve the quality of recommendations by providing additional information and being able to represent more complex preferences of each user. (4) With the development of 3G and cloud computing technology, mobile recommender systems will be a promising area in the tourism and travel industry. Information from social networks can be integrated into recommender systems to produce more accurate recommendations. (5) User privacy protection is also a challenge for recommender systems as they often need as much personal information as possible. So methodologies for protecting user anonymity and privacy are required, and they should guarantee the effectiveness and accuracy of recommendations without compromising the privacy of user profiles and sensitive contextual information. [ABSTRACT FROM AUTHOR]

Information overload is widely exist in product design process. To alleviate the information overload, the main approach is to improve the accuracy of the search engine. The knowledge map can be used to achieve the goal by providing the knowledge association information. A semi-automation process of constructing domain knowledge map was proposed. In the process, the domain feature set was defined by domain experts to guarantee its comprehensiveness and independence, and the latent semantic analysis was adopted to eliminate the semantic overlapping in the feature set. Analyzed the distribution of the domain knowledge similarity, an ultimate domain knowledge map was built with a middle-distance strategy. At last, a knowledge map for computer numerical control (CNC) domain was constructed by the proposed method to demonstrate the feasibility. [ABSTRACT FROM AUTHOR]

The traditional Item-based collaborative filtering algorithm regards every rating as equal importance when calculating the similarity between items, and ignores the impact of the similarity between co-rated users ( users co-rate both two items) and target user on the similarity between items. This paper proposed a user-adaptive Item-based collaborative filtering recommendation algorithm, in which the rating of a co-rated user on an item was weighted by the user similarity between the co-rated user and target user, in order to select different neighbors of a certain target item for different target users, and so as to improve the recommendation accuracy. The experiment results suggest that the proposed algorithm can efficiently improve the recommendation quality. [ABSTRACT FROM AUTHOR]

Recommendation technology is an effective technology to alleviate the problem of "information overload". To agglutinate human's network behavior and network information service, a novel collaborative recommendation method based on network community was presented, which promoted the contribution of community division by weighting eigenvector of user network. As user's rating style is taken into consideration, an approach to normalize the user's rating values is proposed, and the rating based on item similarity within the same user community is predicted. The experimental results indicate that the method can achieve better recommendation performance. [ABSTRACT FROM AUTHOR]