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Representation learning: serial-autoencoder for personalized recommendation

Frontiers of Computer Science 2024年第4期18卷 61-72页

作者： Yi ZHU Yishuai GENG Yun LI Jipeng QIANG Xindong WU School of Information Engineering Yangzhou UniversityYangzhou 225127China Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of the People’s Republic of China) Hefei University of TechnologyHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China

Nowadays,the personalized recommendation has become a research hotspot for addressing information *** this,generating effective recommendations from sparse data remains a ***,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited *** to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite ***,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model *** address these problems,we propose Serial-Autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature ***,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the ***,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating *** output rating information is used for recommendation *** experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.

关键词： personalized recommendation autoencoder representation learning collaborative filtering

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Finding top-k min-cost connected trees in databases

Finding top-k min-cost connected trees in databases

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23rd International Conference on data engineering, ICDE 2007

作者： Ding, Bolin Yu, Jeffrey Xu Wang, Shan Qin, Lu Zhang, Xiao Lin, Xuemin Chinese University of Hong Kong Hong Kong Key Laboratory of Data Engineering and Knowledge Engineering MOE of China Renmin University of China China University of New South Wales NICTA Australia

ISBN: (纸本)1424408032

It widely realized that the integration of database and information retrieval techniques will provide users with a wide range of high quality services. In this paper, we study processing an l-keyword query, p1, p2, , p1, against a relational database which can be modeled as a weighted graph, G(V, E). Here V is a set of nodes (tuples) and E is a set of edges representing foreign key references between tuples. Let Vi⊆7 V be a set of nodes that contain the keyword pi. We study finding top-k minimum cost connected trees that contain at least one node in every subset Vi, and denote our problem as GST-k When k = 1, it is known as a minimum cost group Steiner tree problem which is NP-Complete. We observe that the number of keywords, l, is small, and propose a novel parameterized solution, with l as a parameter, to find the optimal GST-1, in time complexity O(3ln + 2l ((l + log n)n + m)), where n and m are the numbers of nodes and edges in graph G. Our solution can handle graphs with a large number of nodes. Our GST-1 solution can be easily extended to support GST-k, which outperforms the existing GST-k solutions over both weighted undirected/directed graphs. We conducted extensive experimental studies, and report our finding. © 2007 IEEE.

关键词： Trees (mathematics)

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Fuzzy associative classifier for probabilistic numerical data 7

Fuzzy associative classifier for probabilistic numerical dat...

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7th International Conference on Intelligent Systems and knowledge engineering, ISKE 2012 with 1st International Conference on Cognitive Systems and Information Processing, CSIP 2012

作者： Pei, Bin Zhao, Tingting Zhao, Suyun Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China School of Information Renmin University of China Beijing China New Star Research Institute of Applied Tech Hefei China

ISBN: (纸本)9783642378287

Recently, a number of advanced data collection and processing methodologies have led to the proliferation of uncertain data. When discovering from such uncertain data, we should handle these uncertainties with caution, because classical mining algorithms may not be appropriate for uncertain tasks. This paper proposes a generic framework of fuzzy associative classifier for probabilistic numerical data, which is prevalent in the real-world applications, such as sensor networks and GPS-based location. In this paper, we first introduce an Aprioribased algorithm for mining fuzzy association rules from a probabilistic numerical dataset based on novel support and confidence measures suitable for such dataset. Then, we give fuzzy rules redundancy pruning strategy and database coverage method to build a compact fuzzy associative classifier in removing redundant rules and thus improving the accuracy of the classifier. We also redefine multiple fuzzy rules classification method for classifying new instances. Extensive experimental results show the effectiveness and efficiency of our algorithm. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Fuzzy rules

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Fuzzy Rough Based Decision Tree Constructing

Fuzzy Rough Based Decision Tree Constructing

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第十二届中国Rough集与软计算学术会议、第六届中国Web智能学术研讨会及第六届中国粒计算学术研讨会联合学术会议

作者： CHEN Wei ZHAO Su-yun CHEN Hong LI Cui-ping Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaMOE Beijin Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaMOE Beijin

Decision tree and fuzzy rough set are two distinct but complementary *** tree is a simple and easy-understandable rule-based classifier,whereas the tool of fuzzy rough sets are effective on attribute and sample *** is promising to propose an approach to integrate these two rule based classification tools to construct a novel decision tree based on fuzzy rough *** this paper,based on the basic concept of fuzzy rough sets,i.e.,consistence degree,we propose a fuzzy rough decision tree which is completely different from the existing classification *** three key basic elements of decision tree,i.e.,node,branch and leaf,are designed in a new way by using the notions in fuzzy rough *** then one algorithm to build fuzzy rough tree classifier is ***,experimental results show that the proposed algorithm is readily comprehensible and effective.

关键词： Fuzzy rough sets Decision tree Consistence degree Rule-based classifier

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Reflection on the popularity of MapReduce and observation of its position in a unified big data platform

Reflection on the popularity of MapReduce and observation of...

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14th International Conference on Web-Age Information Management, WAIM 2013

作者： Qin, Xiongpai Qin, Biao Du, Xiaoyong Wang, Shan MOE Key Lab. of Data Engineering and Knowledge Engineering Beijing 100872 China School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9783642395260

In recent years MapReduce has risen to be the de-facto tool for big data processing. MapReduce is a disruptive innovation. It has changed the landscape of database market, the landscape of technologies, as well as the landscape of saying power. The article will give a reflection on the popularity of the technique and some observations of its position in a unified big data platform. © 2013 Springer-Verlag.

关键词： MapReduce

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WATuning:A Workload-Aware Tuning System with Attention-Based Deep Reinforcement Learning

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Journal of Computer Science & Technology 2021年第4期36卷 741-761页

作者： Jia-Ke Ge Yan-Feng Chai Yun-Peng Chai Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Renmin University of China Beijing 100872China School of Information Renmin University of ChinaBeijing 100872China College of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030027China

Configuration tuning is essential to optimize the performance of systems(e.g.,databases,key-value stores).High performance usually indicates high throughput and low *** present,most of the tuning tasks of systems are performed artificially(e.g.,by database administrators),but it is hard for them to achieve high performance through tuning in various types of systems and in various *** recent years,there have been some studies on tuning traditional database systems,but all these methods have some *** this article,we put forward a tuning system based on attention-based deep reinforcement learning named WATuning,which can adapt to the changes of workload characteristics and optimize the system performance efficiently and ***,we design the core algorithm named ATT-Tune for WATuning to achieve the tuning task of *** algorithm uses workload characteristics to generate a weight matrix and acts on the internal metrics of systems,and then ATT-Tune uses the internal metrics with weight values assigned to select the appropriate ***,WATuning can generate multiple instance models according to the change of the workload so that it can complete targeted recommendation services for different types of ***,WATuning can also dynamically fine-tune itself according to the constantly changing workload in practical applications so that it can better fit to the actual environment to make *** experimental results show that the throughput and the latency of WATuning are improved by 52.6%and decreased by 31%,respectively,compared with the throughput and the latency of CDBTune which is an existing optimal tuning method.

关键词： attention mechanism auto-tuning system reinforcement learning(RL) workload-aware

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Diversifying Question Generation over knowledge Base via External Natural Questions 30

Diversifying Question Generation over Knowledge Base via Ext...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Guo, Shasha Zhang, Jing Ke, Xirui Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China

ISBN: (纸本)9782493814104

Previous methods on knowledge base question generation (KBQG) primarily focus on refining the quality of a single generated question. However, considering the remarkable paraphrasing ability of humans, we believe that diverse texts can express identical semantics through varied expressions. The above insights make diversifying question generation an intriguing task, where the first challenge is evaluation metrics for diversity. Current metrics inadequately assess the aforementioned diversity. They calculate the ratio of unique n-grams in the generated question, which tends to measure duplication rather than true diversity. Accordingly, we devise a new diversity evaluation metric, which measures the diversity among top-k generated questions for each instance while ensuring their relevance to the ground truth. Clearly, the second challenge is how to enhance diversifying question generation. To address this challenge, we introduce a dual model framework interwoven by two selection strategies to generate diverse questions leveraging external natural questions. The main idea of our dual framework is to extract more diverse expressions and integrate them into the generation model to enhance diversifying question generation. Extensive experiments on widely used benchmarks for KBQG show that our approach can outperform pre-trained language model baselines and text-davinci-003 in diversity while achieving comparable performance with ChatGPT. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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DSM: Question Generation over knowledge Base via Modeling Diverse Subgraphs with Meta-learner

DSM: Question Generation over Knowledge Base via Modeling Di...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Guo, Shasha Zhang, Jing Wang, Yanling Zhang, Qianyi Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China

Existing methods on knowledge base question generation (KBQG) learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraphs. In this work, we show that making use of the past experience on semantically similar subgraphs can reduce the learning difficulty and promote the performance of KBQG models. To achieve this, we propose a novel approach to model diverse subgraphs with meta-learner (DSM). Specifically, we devise a graph contrastive learning-based retriever to identify semantically similar subgraphs, so that we can construct the semantics-aware learning tasks for the meta-learner to learn semantics-specific and semantics-agnostic knowledge on and across these tasks. Extensive experiments on two widely-adopted benchmarks for KBQG show that DSM derives new state-of-the-art performance and benefits the question answering tasks as a means of data augmentation. Codes and datasets are available online. © 2022 Association for Computational Linguistics.

关键词： Semantics

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J+-tree: A new index structure in main memory

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12th International Conference on database Systems for Advanced Applications, DASFAA 2007

作者： Luan, Hua Du, Xiaoyong Wang, Shan Ni, Yongzhi Chen, Qiming School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing China HP Labs. China Beijing China

ISBN: (纸本)9783540717027

As the memory capacity increases and the hardware becomes cheaper, main memory databases (MMDB) have come true and been used in more and more applications, because they can provide better response time and throughputs. The advent of MMDB requires a reconsideration of data structures and algorithms of traditional DBMS. The index structure is one of the most important aspects that need be redesigned since it can affect the overall system performance heavily. Even though the T-tree index, which was proposed for main memory databases, has been widely accepted as a promising index structure. B+-tree and its variants still have their advantages in memory and are also regarded as the potential main memory database index structures. In this paper, we propose a new indexing technique called J+-tree for MMDB, inspired by the Judy structure which is an associative array data structure. Our J+-tree index not only holds the advantages of Judy (such as good single value search characteristic) but also outperforms it in many ways. For example, J +-tree can obtain better performance for range queries that are very slow in Judy structure. We compare the J+-tree index with Judy, T-tree and B+-tree on time and space aspects, and the experimental results show that J+-tree can provide better overall performance in main memory. © Springer-Verlag Berlin Heidelberg 2007.

关键词： Storage allocation (computer)

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SEED: Entity oriented information search and exploration 22

SEED: Entity oriented information search and exploration

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22nd International Conference on Intelligent User Interfaces, IUI 2017

作者： Chen, Jun Jacucci, Giulio Chen, Yueguo Ruotsalo, Tuukka School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Helsinki Institute for Information Technology HIIT Department of Computer Science University of Helsinki Finland

ISBN: (纸本)9781450348935

Entity search and exploration can enrich search user interfaces by presenting relevant information instantly and offering relevant exploration pointers to users. Previous research has demonstrated that large knowledge Graphs allow exploitation and recommendation of explicit links between the entities and other information to improve information access and ranking. However, less attention has been devoted to user interfaces for effectively presenting results, recommending related entities and explaining relations between entities. We introduce a system called SEED which is designed to support entity search and exploration in large knowledge Graphs. We demonstrate SEED using a dataset of hundreds of thousands of movie related entities from the DBpedia knowledge Graph. The system utilizes a graph embedding model for ranking entities and their relations, recommending related entities, and explaining their interrelations. Copyright is held by the author/owner(s).

关键词： knowledge graph

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