Top-k coupled keyword recommendation for relational keyword queries

Meng, Xiangfu; Cao, Longbing; Zhang, Xiaoyan; Shao, Jingyu

doi:10.1007/s10115-016-0959-3

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…Based on these check-in location information, recommendation algorithms can not only recommend tourist attractions that may be interested in to users, but also effectively help businesses recommend their information to target users. Traditional location recommendation algorithms mainly include collaborative filtering recommendation algorithm, content-based recommendation algorithm and hybrid recommendation algorithm [3] [4]. The collaborative filtering recommendation algorithm is the most widely used recommendation algorithm.…”

Section: Introductionmentioning

confidence: 99%

Exploring Diversity and Time-aware Recommendations: A LSTM-DNN Model with Bidirectional DTW Algorithm

Li,

Chen,

Sun

et al. 2023

Preprint

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With the advent of the Web 3.0 era, the number and types of data in the network have sharply increased, and the application scenarios of recommendation algorithms have also been expanded to a certain extent. Location recommendation has gradually become one of the popular application scenarios in recommendation algorithms. Traditional recommendation algorithms not only ignore the time attribute of data when recommending information to users, but also blindly pursue the recommendation accuracy, which will cause certain "information cocoon room" problems. Therefore, this article treats user historical data as a time series and proposes a LSTM-DNN model based on the bidirectional DTW algorithm. Firstly, in response to the issue of different users consuming different amounts of information, this article proposes a bidirectional DTW algorithm to calculate the similarities between different users. Secondly, this article supplements the user dataset from three perspectives: "utilization" of information, "exploration", and spatiotemporal attributes of data, which alleviates the problem of data sparsity and cold start in the dataset to a certain extent. Moreover, it effectively enhances the diversity of recommendation results. Finally, this paper constructs a LSTM-DNN neural network to dynamically obtain user interests and preferences, and proposes a new metric CSSD to measure the diversity of algorithm recommendation results. Experiments have shown that the model effectively enhances the diversity of recommendation results while ensuring recommendation accuracy.

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Section: Introductionmentioning

confidence: 99%

Exploring Diversity and Time-aware Recommendations: A LSTM-DNN Model with Bidirectional DTW Algorithm

Li,

Chen,

Sun

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Academic Paper Recommendation Method Combining Heterogeneous Network and Temporal Attributes

Chang

et al. 2021

Computer Supported Cooperative Work and Social Computing

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An Efficient Relational Database Keyword Search Scheme Based on Combined Candidate Network Evaluation

Ding

Sun

et al. 2020

IEEE Access

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Relational keyword search (R-KWS) systems provide users with a convenient means in relational database queries. There exist two main types of R-KWS: those based on Data Graphs and those based on Schema Graphs. In this paper, we focus on the latter, R-KWS based on Schema Graphs. Most existing methods are typically inefficient due to the large number of repetitive operation caused by overlapping candidate networks and the execution of lots of complex queries. We present the R-KWS approach based on combined candidate network evaluation to improve the query efficiency. The proposed Combined Candidate Network (CCN) can efficiently share the overlapping part between candidate networks, and then avoid the repetitive operation during the evaluation of candidate networks. Meanwhile, CCN possesses another important characteristic that candidate networks within a CCN are still identifiable after candidate networks being compressed into a CCN. We design an algorithm based on this characteristic to evaluate CCN for the generation of final query results. This algorithm is able to eliminate the execution of a large number of complex queries required by most existing approaches, and thus significantly improve the efficiency of keyword search. Experiments on real datasets show that our approach can improve query efficiency without any loss of the quality of query results with respect to existing approaches. INDEX TERMS Relational database, keyword query, schema graphs, combined candidate network. I. INTRODUCTION Structured Query Language (SQL) is the main means to access data from a relational database, which requires users to understand the complex SQL syntax and schema information of database [4], [27], [29], [31]. Compared with SQL, relational database keyword search (R-KWS) is simpler, which enables users to query information from a relational database by the way of search engines. Recently, lots of works about R-KWS are proposed, which can be classified into two main types: R-KWS based on Data Graphs [4]-[7], [28], [30] and R-KWS based on Schema Graphs [8]-[24]. R-KWS based on Data Graphs typically needs to preload the Data Graphs into memory, which cannot sometimes be completed at a time because of the consumption of a large amount of memory [7]. R-KWS based The associate editor coordinating the review of this manuscript and approving it for publication was Yongqiang Zhao .

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Top-k coupled keyword recommendation for relational keyword queries

Cited by 5 publications

References 32 publications

Exploring Diversity and Time-aware Recommendations: A LSTM-DNN Model with Bidirectional DTW Algorithm

Exploring Diversity and Time-aware Recommendations: A LSTM-DNN Model with Bidirectional DTW Algorithm

Academic Paper Recommendation Method Combining Heterogeneous Network and Temporal Attributes

An Efficient Relational Database Keyword Search Scheme Based on Combined Candidate Network Evaluation

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