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recommendation Algorithm Based on Probabilistic Matrix Factorization with Adaboost

Computers, Materials & Continua 2020年第11期65卷 1591-1603页

作者： Hongtao Bai Xuan Li Lili He Longhai Jin Chong Wang Yu Jiang College of Computer Science and Technology Jilin UniversityChangchun130012China Symbol Computation and Knowledge Engineer of Ministry of Education Jilin UniversityChangchun130012China Department of Engineering Mechanics State Marine Technical University of St.PetersburgSt.Petersburg190008Russia

A current problem in diet recommendation systems is the matching of food preferences with nutritional requirements,taking into account individual characteristics,such as body weight with individual health conditions,such as *** dietary recommendations employ association rules,content-based collaborative filtering,and constraint-based methods,which have several *** limitations are due to the existence of a special user group and an imbalance of non-simple *** use of traditional dietary recommendation algorithm researches,we combine the Adaboost classifier with probabilistic matrix *** present a personalized diet recommendation algorithm by taking advantage of probabilistic matrix factorization via Adaboost.A probabilistic matrix factorization method extracts the implicit factors between individual food preferences and nutritional *** this,we can make use of those features with strong influence while discarding those with little *** incorporating these changes into our approach,we evaluated our algorithm’s *** results show that our method performed better than others at matching preferred foods with dietary requirements,benefiting user health as a *** algorithm fully considers the constraint relationship between users’attributes and nutritional characteristics of *** many complex factors in our algorithm,the recommended food result set meets both health standards and users’dietary preferences.A comparison of our algorithm with others demonstrated that our method offers high accuracy and interpretability.

关键词： recommendation probabilistic matrix factorization Adaboost characteristics correlation

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recommendation　ON　SPEEDING　UP　SHALLOW　GAS　EXPLORATION　IN　CHINA

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天然气工业 1997年第1期 92-97页

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ＲＥＣＯＭＭＥＮＤＡＴＩＯＮＯＮＳＰＥＥＤＩＮＧＵＰＳＨＡＬＬＯＷＧＡＳＥＸＰＬＯＲＡＴＩＯＮＩＮＣＨＩＮＡＸｕＷａｎｇ（ＲｅｓｅａｒｃｈＩｎｓｔｉｔｕｔｅｏｆＰｅｔｒｏｌｅｕｍＥｘｐｌｏｒａｔｉｏｎａｎｄＤｅｖｅｌｏｐｍｅｎｔ，ＣＮＰＣ，Ｂｅｉｊｉ...

关键词： GAS SHALLOW UP SPEEDING recommendation CHINA EXPLORATION

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Combining long-term and short-term user interest for personalized hashtag recommendation

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Frontiers of Computer Science 2015年第4期9卷 608-622页

作者： Jianjun YU Tongyu ZHU Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China State Key Lab of Software Development Environment B eihang University Beijing 100190 China

Hashtags, terms prefixed by a hash-symbol #, are widely used and inserted anywhere within short messages （tweets） on micro-blogging systems as they present rich sen- timent information on topics that people are interested in. In this paper, we focus on the problem of hashtag recommenda- tion considering their personalized and temporal aspects. As far as we know, this is the first work addressing this issue spe- cially to recommend personalized hashtags combining long- term and short-term user interest. We introduce three features to capture personal and temporal user interest： 1） hashtag textual information; 2） user behavior; and 3） time. We of- fer two recommendation models for comparison： a linear- combined model, and an enhanced session-based temporal graph （STG） model, Topic-STG, considering the features to learn user preferences and subsequently recommend person- alized hashtags. Experiments on two real tweet datasets illus- trate the effectiveness of the proposed models and algorithms.

关键词： recommendation hashtag time-sensitive userinterest

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RepoLike: a multi-feature-based personalized recommendation approach for open-source repositories

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Frontiers of Information Technology & Electronic Engineering 2019年第2期20卷 222-237页

作者： Cheng YANG Qiang FAN Tao WANG Gang YIN Xun-hui ZHANG Yue YU Hua-min WANG Key Laboratory of Parallel and Distributed Computing National University of Defense Technology

With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness,a large number of external contributors have been attracted to the open-source communities. They are playing a significant role in open-source development. However, the open-source development online is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks from thousands of open-source projects in the communities to work on. In this paper, we propose a new approach called "RepoLike," to recommend repositories for developers based on linear combination and learning to rank. It uses the project popularity, technical dependencies among projects, and social connections among developers to measure the correlations between a developer and the given projects. Experimental results show that our approach can achieve over 25% of hit ratio when recommending 20 candidates, meaning that it can recommend closely correlated repositories to social developers.

关键词： Social coding Open-source software Personal recommendation GitHub

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Phrase-based hashtag recommendation for microblog posts

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Science China(Information Sciences) 2017年第1期60卷 132-144页

作者： Yeyun GONG Qi ZHANG Xiaoying HAN Xuanjing HUANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University

In microblogs, authors use hashtags to mark keywords or topics. These manually labeled tags can be used to benefit various live social media applications(e.g., microblog retrieval, classification). However,because only a small portion of microblogs contain hashtags, recommending hashtags for use in microblogs are a worthwhile exercise. In addition, human inference often relies on the intrinsic grouping of words into ***, existing work uses only unigrams to model corpora. In this work, we propose a novel phrase-based topical translation model to address this problem. We use the bag-of-phrases model to better capture the underlying topics of posted microblogs. We regard the phrases and hashtags in a microblog as two different languages that are talking about the same thing. Thus, the hashtag recommendation task can be viewed as a translation process from phrases to hashtags. To handle the topical information of microblogs, the proposed model regards translation probability as being topic specific. We test the methods on data collected from realworld microblogging services. The results demonstrate that the proposed method outperforms state-of-the-art methods that use the unigram model.

关键词： recommendation topic model translation phrase extraction hashtag

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A Heuristic Approach for Context-Aware recommendation Using Rough Set Theory

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Chinese Journal of Electronics 2018年第3期27卷 500-506页

作者： HE Ming DONG Tao LIU Yi College of Computer Science Beijing University of Technology

Context-aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. However, not all contextual information might be relevant or useful for recommendation purposes, and little work has been done on measuring how important the contextual information for *** propose a heuristic optimization algorithm based on rough set theory and collaborative filtering to using contextual information more efficiently for boosting recommendation. Our approach involves three processes. First, significant attributes to represent contextual information are extracted and measured to identify recommended items using rough set theory. Second, the user similarity is evaluated in a target context consideration. Third, collaborative filtering is applied to recommend appropriate *** perform an empirical comparison of three approaches on two real-world data sets. The experimental results show that the proposed approach generates more accurate predictions.

关键词： recommendation Context Context-aware recommendation Collaborative filtering Rough set

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Exploiting Structural and Temporal Influence for Dynamic Social-Aware recommendation

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Journal of Computer Science & Technology 2020年第2期35卷 281-294页

作者： Yang Liu Zhi Li Wei Huang Tong Xu En-Hong Chen Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230027China

Recent years have witnessed the rapid development of online social platforms,which effectively support the business intelligence and provide services for massive *** this line,large efforts have been made on the socialaware recommendation task,i.e.,leveraging social contextual information to improve recommendation *** existing methods have treated social relations in a static way,but the dynamic influence of social contextual information on users'consumption choices has been largely *** that end,in this paper,we conduct a comprehensive study to reveal the dynamic social influence on users'preferences,and then we propose a deep model called Dynamic Social-Aware Recommender System(DSRS)to integrate the users'structural and temporal social contexts to address the dynamic socialaware recommendation *** consists of two main components,i.e.,the social influence learning(SIL)and dynamic preference learning(DPL).Specifically,in the SIL module,we arrange social graphs in a sequential order and borrow the power of graph convolution networks(GCNs)to learn social ***,we design a structural-temporal attention mechanism to discriminatively model the structural social influence and the temporal social ***,in the DPL part,users'individual preferences are learned dynamically by recurrent neural networks(RNNs).Finally,with a prediction layer,we combine the users'social context and dynamic preferences to generate *** conduct extensive experiments on two real-world datasets,and the experimental results demonstrate the superiority and effectiveness of our proposed model compared with the state-of-the-art methods.

关键词： recommender system social influence sequential recommendation

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Exploring Social Annotations with the Application to Web Page recommendation

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Journal of Computer Science & Technology 2009年第6期24卷 1028-1034页

作者：李慧倩夏粉曾大军王飞跃毛文吉 Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Department of Management Information Systems University of Arizona

Collaborative social annotation systems allow users to record and share their original keywords or tag attachments to Web resources such as Web pages, photos, or videos. These annotations are a method for organizing and labeling information. They have the potential to help users navigate the Web and locate the needed resources. However, since annotations axe posted by users under no central control, there exist problems such as spare and synonymous annotations. To efficiently use annotation information to facilitate knowledge discovery from the Web, it is advantageous if we organize social annotations from semantic perspective and embed them into algorithms for knowledge discovery. This inspires the Web page recommendation with annotations, in which users and Web pages are clustered so that semantically similar items can be related. In this paper we propose four graphic models which cluster users, Web pages and annotations and recommend Web pages for given users by assigning items to the right cluster first. The algorithms are then compared to the classical collaborative filtering recommendation method on a real-world data set. Our result indicates that the graphic models provide better recommendation performance and are robust to fit for the real applications.

关键词： graphic model EM (expectation-maximization) social annotation tag recommendation

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Leveraging proficiency and preference for online Karaoke recommendation

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Frontiers of Computer Science 2020年第2期14卷 273-290页

作者： Ming HE Hao GUO Guangyi LV Le WU Yong GE Enhong CHEN Haiping MA Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China School of Computer and Information Hefei University of TechnologyHefei 230026China Eller College of Management The University of ArizonaArizona 85721-0108USA iFlyTek Research Hefei 230026China

Recently,many online Karaoke(KTV)platforms have been released,where music lovers sing songs on these *** the meantime,the system automatically evaluates user proficiency according to their singing *** approximate songs to users can initialize singers5 participation and improve users,loyalty to these ***,this is not an easy task due to the unique characteristics of these ***,since users may be not achieving high scores evaluated by the system on their favorite songs,how to balance user preferences with user proficiency on singing for song recommendation is still ***,the sparsity of the user-song interaction behavior may greatly impact the recommendation *** solve the above two challenges,in this paper,we propose an informationfused song recommendation model by considering the unique characteristics of the singing ***,we first devise a pseudo-rating matrix by combing users’singing behavior and the system evaluations,thus users'preferences and proficiency are *** we mitigate the data sparsity problem by fusing users*and songs'rich information in the matrix factorization process of the pseudo-rating ***,extensive experimental results on a real-world dataset show the effectiveness of our proposed model.

关键词： KTV matrix factorization recommendation system

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ATLRec:An Attentional Adversarial Transfer Learning Network for Cross-Domain recommendation

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Journal of Computer Science & Technology 2020年第4期35卷 794-808页

作者： Ying Li Jia-Jie Xu Peng-Peng Zhao Jun-Hua Fang Wei Chen Lei Zhao School of Computer Science and Technology Soochow UniversitySuzhou 215006China

Entity linking is a new technique in recommender systems to link users'interaction behaviors in different domains,for the purpose of improving the performance of the recommendation ***-based cross-domain recom-mendation aims to alleviate the data sparse problem by utilizing the domain-sharable knowledge from auxiliary ***,existing methods fail to prevent domain-specific features to be transferred,resulting in suboptimal *** this paper,we aim to address this issue by proposing an adversarial transfer learning based model ATLRec,which effec-tively captures domain-sharable features for cross-domain *** ATLRec,we leverage adversarial learning to generate representations of user-item interactions in both the source and the target domains,such that the discrimina-tor cannot identify which domain they belong to,for the purpose of obtaining domain-sharable *** each domain learns its domain-specific features by a private feature *** recommendation of each domain considers both domain-specific and domain-sharable *** further adopt an attention mechanism to learn item latent factors of both domains by utilizing the shared users with interaction history,so that the representations of all items can be learned sufficiently in a shared space,even when few or even no items are shared by different *** this method,we can represent all items from the source and the target domains in a shared space,for the purpose of better linking items in different domains and capturing cross-domain item-item relatedness to facilitate the learning of domain-sharable *** proposed model is evaluated on various real-world datasets and demonstrated to outperform several state-of-the-art single-domain and cross-domain recommendation methods in terms of recommendation accuracy.

关键词： adversarial transfer learning attention mechanism cross-domain recommendation entity linking

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