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Personalizing search results using hierarchical RNN with query-aware attention

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arXiv 2019年

作者： Ge, Songwei Dou, Zhicheng Jiang, Zhengbao Nie, Jian-Yun Wen, Ji-Rong School of Information Renmin University of China DIRO Université de Montréal Beijing Key Laboratory of Big Data Management and Analysis Methods National Engineering Laboratory of Big Data System Software Beijing Institute of Technology Key Laboratory of Data Engineering and Knowledge Engineering MOE

Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query-aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training. Copyright © 2019, The Authors. All rights reserved.

关键词： Search engines

PSSL: Self-supervised learning for personalized search with contrastive sampling

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arXiv 2021年

作者： Zhou, Yujia Dou, Zhicheng Zhu, Yutao Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China School of Information Renmin University of China Université de Montréal MontréalQC Canada Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE

Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors. Previous studies have made great progress in extracting personal signals from the query log and learning user representations. However, neural personalized search is extremely dependent on sufficient data to train the user model. data sparsity is an inevitable challenge for existing methods to learn high-quality user representations. Moreover, the overemphasis on final ranking quality leads to rough data representations and impairs the generalizability of the model. To tackle these issues, we propose a Personalized Search framework with Self-supervised Learning (PSSL) to enhance data representations. Specifically, we adopt a contrastive sampling method to extract paired self-supervised information from sequences of user behaviors in query logs. Four auxiliary tasks are designed to pre-train the sentence encoder and the sequence encoder used in the ranking model. They are optimized by contrastive loss which aims to close the distance between similar user sequences, queries, and documents. Experimental results on two datasets demonstrate that our proposed model PSSL achieves state-of-the-art performance compared with existing *** Codes 68P20 Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

Single attribute Join Queries within latest sampling periods in sensor networks

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Single attribute Join Queries within latest sampling periods...

IEEE Symposium on Computers and Communications (ISCC)

作者： Shangfeng Mo Hong Chen Yinglong Li School of Math and Computing Science Hunan University of Science and Technology Xiangtan China Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing China

Join processing in wireless sensor networks is a challenging problem. Current solutions are not involved in the join operation among tuples of the latest sampling periods. In this article, we proposed a continuous Single attribute Join Queries within latest sampling Periods (SJQP) for wireless sensor networks. The main idea of our filter-based framework is to discard non-matching tuples, and our scheme can guarantee the result is correct independent of the filters. Experiments based on real-world sensor data show that our method performs close to a theoretical optimum and consistently outperforms the centralized join algorithm.

关键词： Base stations Wireless sensor networks Probes Filtering algorithms Temperature sensors Information filters

One chatbot per person: Creating personalized chatbots based on implicit user profiles

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arXiv 2021年

作者： Ma, Zhengyi Dou, Zhicheng Zhu, Yutao Zhong, Hanxun Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China School of Information Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE Université de Montréal MontréalQC Canada

Personalized chatbots focus on endowing chatbots with a consistent personality to behave like real users, give more informative responses, and further act as personal assistants. Existing personalized approaches tried to incorporate several text descriptions as explicit user profiles. However, the acquisition of such explicit profiles is expensive and time-consuming, thus being impractical for large-scale real-world applications. Moreover, the restricted predefined profile neglects the language behavior of a real user and cannot be automatically updated together with the change of user interests. In this paper, we propose to learn implicit user profiles automatically from large-scale user dialogue history for building personalized chatbots. Specifically, leveraging the benefits of Transformer on language understanding, we train a personalized language model to construct a general user profile from the user's historical responses. To highlight the relevant historical responses to the input post, we further establish a key-value memory network of historical post-response pairs, and build a dynamic post-aware user profile. The dynamic profile mainly describes what and howthe user has responded to similar posts in history. To explicitly utilize users' frequently used words, we design a personalized decoder to fuse two decoding strategies, including generating a word from the generic vocabulary and copying one word from the user's personalized vocabulary. Experiments on two real-world datasets show the significant improvement of our model compared with existing methods. Our code is available at https://***/zhengyima/DHAP. Copyright © 2021, The Authors. All rights reserved.

关键词： User profile

Idea-map: A Spatiotemporal View of Research Ideas

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Idea-map: A Spatiotemporal View of Research Ideas

2011 Eighth International Conference on Fuzzy System and knowledge Discovery(第八届模糊系统与知识发现国际会议 FSKD 2011)

作者： HeHu Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering(Renmin University of China) MOE Beijing 100872 P. R. China School of Information Renmin University of China Beijing 100872 P. R. China

in this paper, we describe idea-map, a mash-up application building on top of Linked data Cloud. It reads in user's keywords (about research ideas) and executes a SPARQL query against DBLP endpoint. Spatial and temporal information is extracted and parsed from the query results and is further transformed to SIMILE/EXHIBIT to show a spatiotemporal map for the research ideas. Idea-map shows the feasibility of combing various techniques such as YQL, SIMILE/Exhibit and SPARQL query answering to provide an insightful interface to better understand the interested research ideas.

关键词： Link data Cloud DBLP Spatiotemoral View YQL

Injecting Numerical Reasoning Skills into knowledge Base Question Answering Models

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arXiv 2021年

作者： Feng, Yu Zhang, Jing Zhang, Xiaokang Liu, Lemao Li, Cuiping Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China Tencent AI Lab United States

Embedding-based methods are popular for knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions. This paper proposes a new embedding-based KBQA framework which particularly takes numerical reasoning into account. We present NumericalTransformer on top of NSM, a state-of-the-art embedding-based KBQA model, to create NT-NSM. To enable better training, we propose two pre-training tasks with explicit numerical-oriented loss functions on two generated training datasets and a template-based data augmentation method for enriching ordinal constrained QA dataset. Extensive experiments on KBQA benchmarks demonstrate that with the help of our training algorithm, NT-NSM is empowered with numerical reasoning skills and substantially outperforms the baselines in answering ordinal constrained questions. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

NUITS: a novel user interface for efficient keyword search over databases 06

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NUITS: a novel user interface for efficient keyword search o...

Proceedings of the 32nd international conference on Very large data bases

作者： Shan Wang Zhaohui Peng Jun Zhang Lu Qin Sheng Wang Jeffrey Xu Yu Bolin Ding School of Information Renmin Univ. of China Key Laboratory of Data Engineering and Knowledge Engineering MOE of China Beijing China Department of Systems Engineering and Engineering Management The Chinese Univ. of Hong Kong Hong Kong China

The integration of database and information retrieval techniques provides users with a wide range of high quality services. We present a prototype system, called NUITS, for efficiently processing keyword queries on top of a relational database. Our NUITS allows users to issue simple keyword queries as well as advanced keyword queries with conditions. The efficiency of keyword query processing and the user-friendly result display will also be addressed in this paper.

关键词：

QUANTUM LOVÁSZ LOCAL LEMMA: SHEARER’S BOUND IS TIGHT

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arXiv 2018年

作者： He, Kun Li, Qian Sun, Xiaoming Zhang, Jiapeng The Key Lab of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China University of Southern California United States

The Lovász Local Lemma (LLL) is a very powerful tool in combinatorics and probability theory to show the possibility of avoiding all bad events under some weakly dependent conditions. In a seminal paper, Ambainis, Kempe, and Sattath (JACM 2012) introduced a quantum version LLL (QLLL) which shows the possibility of avoiding all "bad" Hamiltonians under some weakly dependent condition, and applied QLLL to the random k-QSAT problem. Sattath, Morampudi, Laumann, and moessner (PNAS 2015) extended Ambainis, Kempe, and Sattath’s result and showed that Shearer’s bound is a sufficient condition for QLLL, and conjectured that Shearer’s bound is indeed the tight condition for QLLL. In this paper, we affirm this conjecture. Precisely, we prove that Shearer’s bound is tight for QLLL, i.e., the relative dimension of the smallest satisfying subspace is completely characterized by the independent set polynomial. Our result implies the tightness of Gilyén and Sattath’s algorithm (FOCS 2017), and also implies that the lattice gas partition function fully characterizes quantum satisfiability for almost all Hamiltonians with large enough qudits (Sattath, Morampudi, Laumann and moessner, PNAS 2015). The commuting LLL (CLLL), which focuses on commuting local Hamiltonians, is also investigated here. We prove that the tight regions of CLLL and QLLL are different in general. This result indicates that it is possible to design an algorithm for CLLL which is still efficient beyond Shearer’s bound. Copyright © 2018, The Authors. All rights reserved.

关键词： Hamiltonians

QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

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QGEval: Benchmarking Multi-dimensional Evaluation for Questi...

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Continuing Education Xi'an Jiaotong University Xi'an China MOE KLINNS Lab School of Automation Science and Engineering Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798891761643

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics