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Journal articles on the topic 'PERSONALIZED QUERY'

Author: Grafiati

Published: 11 September 2023

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1

Shen, Chih-Ya, Shao-Heng Ko, Guang-Siang Lee, Wang-Chien Lee, and De-Nian Yang. "Density Personalized Group Query." Proceedings of the VLDB Endowment 16, no. 4 (December 2022): 615–28. http://dx.doi.org/10.14778/3574245.3574249.

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Research on new queries for finding dense subgraphs and groups has been actively pursued due to their many applications, especially in social network analysis and graph mining. However, existing work faces two major weaknesses: i) incapability of supporting personalized neighborhood density, and ii) inability to find sparse groups. To tackle the above issues, we propose a new query, called Density-Customized Social Group Query (DCSGQ), that accommodates the need for personalized density by allowing individual users to flexibly configure their social tightness (and sparseness) for the target group. The proposed DCSGQ is general due to flexible in configuration of neighboring social density in queries. We prove the NP-hardness and inapproximability of DCSGQ, formulate an Integer Program (IP) as a baseline, and propose an efficient algorithm, FSGSel-RR, by relaxing the IP. We then propose a fixed-parameter tractable algorithm with a performance guarantee, named FSGSel-TD, and further combine it with FSGSel-RR into a hybrid approach, named FSGSel-Hybrid, in order to strike a good balance between solution quality and efficiency. Extensive experiments on multiple large real datasets demonstrate the superior solution quality and efficiency of our approaches over existing subgraph and group queries.

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Chen, Wanyu, Zepeng Hao, Taihua Shao, and Honghui Chen. "Personalized query suggestion based on user behavior." International Journal of Modern Physics C 29, no. 04 (April 2018): 1850036. http://dx.doi.org/10.1142/s0129183118500365.

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Query suggestions help users refine their queries after they input an initial query. Previous work mainly concentrated on similarity-based and context-based query suggestion approaches. However, models that focus on adapting to a specific user (personalization) can help to improve the probability of the user being satisfied. In this paper, we propose a personalized query suggestion model based on users’ search behavior (UB model), where we inject relevance between queries and users’ search behavior into a basic probabilistic model. For the relevance between queries, we consider their semantical similarity and co-occurrence which indicates the behavior information from other users in web search. Regarding the current user’s preference to a query, we combine the user’s short-term and long-term search behavior in a linear fashion and deal with the data sparse problem with Bayesian probabilistic matrix factorization (BPMF). In particular, we also investigate the impact of different personalization strategies (the combination of the user’s short-term and long-term search behavior) on the performance of query suggestion reranking. We quantify the improvement of our proposed UB model against a state-of-the-art baseline using the public AOL query logs and show that it beats the baseline in terms of metrics used in query suggestion reranking. The experimental results show that: (i) for personalized ranking, users’ behavioral information helps to improve query suggestion effectiveness; and (ii) given a query, merging information inferred from the short-term and long-term search behavior of a particular user can result in a better performance than both plain approaches.

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Yao, Jing, Zhicheng Dou, and Ji-Rong Wen. "Clarifying Ambiguous Keywords with Personal Word Embeddings for Personalized Search." ACM Transactions on Information Systems 40, no. 3 (July 31, 2022): 1–29. http://dx.doi.org/10.1145/3470564.

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Personalized search tailors document ranking lists for each individual user based on her interests and query intent to better satisfy the user’s information need. Many personalized search models have been proposed. They first build a user interest profile from the user’s search history, and then re-rank the documents based on the personalized matching scores between the created profile and candidate documents. In this article, we attempt to solve the personalized search problem from an alternative perspective of clarifying the user’s intention of the current query. We know that there are many ambiguous words in natural language such as “Apple.” People with different knowledge backgrounds and interests have personalized understandings of these words. Therefore, we propose a personalized search model with personal word embeddings for each individual user that mainly contain the word meanings that the user already knows and can reflect the user interests. To learn great personal word embeddings, we design a pre-training model that captures both the textual information of the query log and the information about user interests contained in the click-through data represented as a graph structure. With personal word embeddings, we obtain the personalized word and context-aware representations of the query and documents. Furthermore, we also employ the current session as the short-term search context to dynamically disambiguate the current query. Finally, we use a matching model to calculate the matching score between the personalized query and document representations for ranking. Experimental results on two large-scale query logs show that our designed model significantly outperforms state-of-the-art personalization models.

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Jiang, Dan-yang, and Hong-hui Chen. "Cohort-based personalized query auto-completion." Frontiers of Information Technology & Electronic Engineering 20, no. 9 (September 2019): 1246–58. http://dx.doi.org/10.1631/fitee.1800010.

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LIANG, Yaopei, and Dingming WU. "Location-aware personalized keyword query recommendation." Journal of Shenzhen University Science and Engineering 36, no. 04 (July 1, 2019): 467–72. http://dx.doi.org/10.3724/sp.j.1249.2019.04467.

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Khemmarat, Samamon, and Lixin Gao. "Predictive and Personalized Drug Query System." IEEE Journal of Biomedical and Health Informatics 21, no. 4 (July 2017): 1146–55. http://dx.doi.org/10.1109/jbhi.2016.2562183.

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7

Han, Meng, and Xiao Hu Qiu. "Personalized Search Engineer Model." Advanced Materials Research 268-270 (July 2011): 1216–21. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1216.

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To improve the accuracy of query result of search engineer and satisfy personalized requirements of users, we proposed the method of building and updating user personalized model. This method based on certain information which mine from users’ behaviors and customs in using search engineer. Through mining information from users’ query customs, visit frequency and browse Web in using Chinese search engineer, we pick up characters of use and interest of users, and then build personalized interest model of users. This paper studies technique details of building and updating personalized model. Set up a personalized Chinese search engineer model.

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8

Zhu, Qiuyu, Dongmei Li, Cong Dai, Qichen Han, and Yi Lin. "PLSA-Based Personalized Information Retrieval with Network Regularization." Journal of Information Technology Research 12, no. 1 (January 2019): 105–16. http://dx.doi.org/10.4018/jitr.2019010108.

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With the rapid development of the Internet, the information retrieval model based on the keywords matching algorithm has not met the requirements of users, because people with various query history always have different retrieval intentions. User query history often implies their interests. Therefore, it is of great importance to enhance the recall ratio and the precision ratio by applying query history into the judgment of retrieval intentions. For this sake, this article does research on user query history and proposes a method to construct user interest model utilizing query history. Coordinately, the authors design a model called PLSA-based Personalized Information Retrieval with Network Regularization. Finally, the model is applied into academic information retrieval and the authors compare it with Baidu Scholar and the personalized information retrieval model based on the probabilistic latent semantic analysis topic model. The experiment results prove that this model can effectively extract topics and retrieves back results more satisfied for users' requirements. Also, this model improves the effect of retrieval results apparently. In addition, the retrieval model can be utilized not only in the academic information retrieval, but also in the personalized information retrieval on microblog search, associate recommendation, etc.

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Xie, Jin, Fuxi Zhu, Huanmei Guan, Jiangqing Wang, Hao Feng, and Lin Zheng. "Personalized query recommendation using semantic factor model." China Communications 18, no. 8 (August 2021): 169–82. http://dx.doi.org/10.23919/jcc.2021.08.012.

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10

Chunguang Ma, Lei Zhang, Songtao Yang, Xiaodong Zheng, and Pinhui Ke. "Achieve personalized anonymity through query blocks exchanging." China Communications 13, no. 11 (November 2016): 106–18. http://dx.doi.org/10.1109/cc.2016.7781722.

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11

Kolli, Santhi, V. Rama chandran, and Ru pa. "Personalized Query Results using User Search Logs." International Journal of Engineering Trends and Technology 4, no. 9 (September 25, 2013): 4227–36. http://dx.doi.org/10.14445/22315381/ijett-v4i9p197.

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12

Zhu, Zheng Yu, Chun Lei Yu, Shu Jia Dong, and Jie He. "A Query Expansion Method Based on User's Historical Interested Web Pages and Historical Query Terms." Applied Mechanics and Materials 52-54 (March 2011): 1218–25. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.1218.

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Current popular search engines are built to serve all users, independent of the needs of any individual user. A personalized query expansion method based on user's historical interested Web pages (UHIWPs) and user’s historical query terms (UHQTs) is proposed in this paper. When a user submits a query keyword to a search engine, the new algorithm can automatically locate the current user’s implicit search intention and compute the term-term associations dynamically according to the user’s UHIWPs and UHQTs. More personalized expansion terms then will be generated and submitted to the search engine together with the query keyword. As a result, different search results can be returned to different users even though they input the same query keywords. Experimental results show that this method is better than the current algorithm in average precision.

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Wu, Yanping, Jun Zhao, Renjie Sun, Chen Chen, and Xiaoyang Wang. "Efficient Personalized Influential Community Search in Large Networks." Data Science and Engineering 6, no. 3 (April 29, 2021): 310–22. http://dx.doi.org/10.1007/s41019-021-00163-3.

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AbstractCommunity search, which aims to retrieve important communities (i.e., subgraphs) for a given query vertex, has been widely studied in the literature. In the recent, plenty of research is conducted to detect influential communities, where each vertex in the network is associated with an influence value. Nevertheless, there is a paucity of work that can support personalized requirement. In this paper, we propose a new problem, i.e., maximal personalized influential community search. Given a graph G, an integer k and a query vertex u, we aim to obtain the most influential community for u by leveraging the k-core concept. To handle larger networks efficiently, two algorithms, i.e., top-down algorithm and bottom-up algorithm, are developed. In real-life applications, there may be a lot of queries issued. Therefore, an optimal index-based approach is proposed in order to meet the online requirement. In many scenarios, users may want to find multiple communities for a given query. Thus, we further extend the proposed techniques for the top-r case, i.e., retrieving r communities with the largest influence value for a given query. Finally, we conduct extensive experiments on 6 real-world networks to demonstrate the advantage of proposed techniques.

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14

Shan, Xiaohuan, Haihai Li, Chunjie Jia, Dong Li, and Baoyan Song. "Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs." Complexity 2021 (June 29, 2021): 1–18. http://dx.doi.org/10.1155/2021/9274429.

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Interesting subgraph query aims to find subgraphs that are isomorphic to the given query graph from a data graph and rank the subgraphs according to their interestingness scores. However, the existing subgraph query approaches are inefficient when dealing with large-scale labeled data graph. This is caused by the following problems: (i) the existing work mainly focuses on unweighted query graphs, while ignoring the impact of query constraints on query results. (ii) Excessive number of subgraph candidates or complex joins between nodes in the subgraph candidates reduce the query efficiency. To solve these problems, this paper proposes an intelligent solution. Firstly, an Isotype Structure Graph Compression (ISGC) strategy is proposed to compress similar nodes in a graph to reduce the size of the graph and avoid unnecessary matching. Then, an auxiliary data structure Supergraph Topology Feature Index (STFIndex) is designed to replace the storage of the original data graph and improve the efficiency of an online query. After that, a partition method based on Edge Label Step Value (ELSV) is proposed to partition the index logically. In addition, a novel Top-K interest subgraph query approach is proposed, which consists of the multidimensional filtering (MDF) strategy, upper bound value (UBV) (Size-c) matching, and the optimizational join (QJ) method to filter out as many false subgraph candidates as possible to achieve fast joins. We conduct experiments on real and synthetic datasets. Experimental results show that the average performance of our approach is 1.35 higher than that of the state-of-the-art approaches when the query graph is unweighted, and the average performance of our approach is 2.88 higher than that of the state-of-the-art approaches when the query graph is weighted.

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15

Gao, Qian, and Young Im Cho. "A Multi-Agent Personalized Query Refinement Approach for Academic Paper Retrieval in Big Data Environment." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 7 (November 20, 2012): 874–80. http://dx.doi.org/10.20965/jaciii.2012.p0874.

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This paper proposes a multi-agent query refinement approach to realize personalized query expansion effective for academic paper retrieval in a Big Data environment. First, we use Hadoop as a platform to develop a formalized model to represent different types of large caches of data in order to analyze and process Big Data efficiently. Second, we use a client agent to verify user identities and monitor whether a device is ready to run a query-expanded task. We then use a query expansion agent to determine the domain that the initial query belongs to by applying a knowledgebased query expansion strategy and comprehensively considering users’ interests according to the intelligent devices they use by implementing a user-device-based query expansion strategy and a weighted query expansion strategy in order to obtain the optimized query expansion set. We compare our method with the conceptual retrieval method as well as other two lexical methods for query expansion, and we prove that our method has better average recall and average precision ratios.

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16

Zhang, Guopeng, Xuebin Chen, Yuanli Jia, and Ran Zhai. "Support Personalized Weighted Local Differential Privacy Skyline Query." Security and Communication Networks 2022 (September 14, 2022): 1–15. http://dx.doi.org/10.1155/2022/9075470.

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The potential privacy risks in certain situations are of concern because of the frequent sharing of data during skyline queries, leading to leakage of users’ private information. The most common privacy-preserving technique is to anonymize data by removing or changing certain information, for which an attack with specific background knowledge would render the privacy protection ineffective. To overcome these difficulties, this study proposes a personalized weighted local differential privacy method (PWLDP) to protect data privacy during skyline querying. Compared with existing studies of skyline queries under privacy protection, the degree of privacy protection can be quantitatively analyzed, and the processing of data privacy lies with the user, who quantitatively perturbs the processing according to the sensitivity of the weights of different attributes to avoid substantial information loss. The performance of the proposed PWLDP is verified by comparing PWLDP and LDP on different datasets, the average privacy leakage reduction of 62.22% and 51.67% is obtained for experiments conducted on different datasets relative to the iDP-SC algorithm, and the experimental results demonstrate the efficiency and advantages of the proposed method.

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17

Yoon, Sung Hee. "Personalized Web Search using Query based User Profile." Journal of the Korea Academia-Industrial cooperation Society 17, no. 2 (February 29, 2016): 690–96. http://dx.doi.org/10.5762/kais.2016.17.2.690.

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18

Barman, Debaditya, Ritam Sarkar, Anil Tudu, and Nirmalya Chowdhury. "Personalized query recommendation system : A genetic algorithm approach." Journal of Interdisciplinary Mathematics 23, no. 2 (February 17, 2020): 523–35. http://dx.doi.org/10.1080/09720502.2020.1731964.

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19

Meng, Xiangfu, Pan Li, and Xiaoyan Zhang. "A Personalized and Approximated Spatial Keyword Query Approach." IEEE Access 8 (2020): 44889–902. http://dx.doi.org/10.1109/access.2020.2977996.

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20

Zhou, Minqi, Heng Tao Shen, Xueqing Gong, Weining Qian, and Aoying Zhou. "Personalized query evaluation in ring-based P2P networks." Information Sciences 220 (January 2013): 463–82. http://dx.doi.org/10.1016/j.ins.2012.07.028.

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21

Zhang, Yujia, Michael Kampffmeyer, Xiaoguang Zhao, and Min Tan. "Deep Reinforcement Learning for Query-Conditioned Video Summarization." Applied Sciences 9, no. 4 (February 21, 2019): 750. http://dx.doi.org/10.3390/app9040750.

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Query-conditioned video summarization requires to (1) find a diverse set of video shots/frames that are representative for the whole video, and that (2) the selected shots/frames are related to a given query. Thus it can be tailored to different user interests leading to a better personalized summary and differs from the generic video summarization which only focuses on video content. Our work targets this query-conditioned video summarization task, by first proposing a Mapping Network (MapNet) in order to express how related a shot is to a given query. MapNet helps establish the relation between the two different modalities (videos and query), which allows mapping of visual information to query space. After that, a deep reinforcement learning-based summarization network (SummNet) is developed to provide personalized summaries by integrating relatedness, representativeness and diversity rewards. These rewards jointly guide the agent to select the most representative and diversity video shots that are most related to the user query. Experimental results on a query-conditioned video summarization benchmark demonstrate the effectiveness of our proposed method, indicating the usefulness of the proposed mapping mechanism as well as the reinforcement learning approach.

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Chawla, Suruchi. "Application of Genetic Algorithm and Back Propagation Neural Network for Effective Personalize Web Search-Based on Clustered Query Sessions." International Journal of Applied Evolutionary Computation 7, no. 1 (January 2016): 33–49. http://dx.doi.org/10.4018/ijaec.2016010103.

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In this paper novel method is proposed using hybrid of Genetic Algorithm (GA) and Back Propagation (BP) Artificial Neural Network (ANN) for learning of classification of user queries to cluster for effective Personalized Web Search. The GA- BP ANN has been trained offline for classification of input queries and user query session profiles to a specific cluster based on clustered web query sessions. Thus during online web search, trained GA –BP ANN is used for classification of new user queries to a cluster and the selected cluster is used for web page recommendations. This process of classification and recommendations continues till search is effectively personalized to the information need of the user. Experiment was conducted on the data set of web user query sessions to evaluate the effectiveness of Personalized Web Search using GA optimized BP ANN and the results confirm the improvement in the precision of search results.

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23

Zhu, Shengrong, Xiangguang Meng, Feixiang Chen, and Xuan Tian. "Personalized Semantic Query Expansion Based on Dynamic User Query Profile and Spreading Activation Model." International Journal of Hybrid Information Technology 10, no. 6 (June 30, 2017): 33–46. http://dx.doi.org/10.14257/ijhit.2017.10.6.04.

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24

KECHID, SAMIR, and HABIBA DRIAS. "PERSONALIZING THE SOURCE SELECTION AND THE RESULT MERGING PROCESS." International Journal on Artificial Intelligence Tools 18, no. 02 (April 2009): 331–54. http://dx.doi.org/10.1142/s0218213009000159.

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The World Wide Web knows an incessant and very fast development. Currently, finding useful information on the Web is a time consuming process. In this paper, we present PIRS a personalized Information Retrieval System in a distributed environment. Most prior research in distributed information access focused on selecting and merging information that has the most relevant content according to the query but ignored the user's specific needs. The underlying idea is that different users have different backgrounds, goals and interests when seeking information and thus, the same query may cover different specific information needs according to who emitted it. However, with the ever expanding Web, users are faced with a huge number of information resources. Consequently, such query-based information access strategies lead to inaccurate query results. PIRS extends the state of the art in a Web-based information retrieval system in distributed environment. First, it develops models for representing both user and information source using feature based profiles. Second, PIRS expands a user query according to his profile. Third, it develops algorithms for source selection and results merging that personalize the computation of the relevance score of a document in response to the user's query. PIRS has been experimented with several known information source. The experimental results obtained show the effectiveness of our approach.

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25

Cai, Fei, Shangsong Liang, and Maarten de Rijke. "Prefix-Adaptive and Time-Sensitive Personalized Query Auto Completion." IEEE Transactions on Knowledge and Data Engineering 28, no. 9 (September 1, 2016): 2452–66. http://dx.doi.org/10.1109/tkde.2016.2568179.

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26

Zhou, Dong, Séamus Lawless, and Vincent Wade. "Improving search via personalized query expansion using social media." Information Retrieval 15, no. 3-4 (February 24, 2012): 218–42. http://dx.doi.org/10.1007/s10791-012-9191-2.

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27

Hahm, Gyeong June, Mun Yong Yi, Jae Hyun Lee, and Hyo Won Suh. "A personalized query expansion approach for engineering document retrieval." Advanced Engineering Informatics 28, no. 4 (October 2014): 344–59. http://dx.doi.org/10.1016/j.aei.2014.04.002.

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28

Mu, Dejun, Lantian Guo, Xiaoyan Cai, and Fei Hao. "Query-Focused Personalized Citation Recommendation With Mutually Reinforced Ranking." IEEE Access 6 (2018): 3107–19. http://dx.doi.org/10.1109/access.2017.2787179.

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Khalifi, Hamid, Walid Cherif, Abderrahim El Qadi, and Youssef Ghanou. "Query expansion based on clustering and personalized information retrieval." Progress in Artificial Intelligence 8, no. 2 (March 4, 2019): 241–51. http://dx.doi.org/10.1007/s13748-019-00178-y.

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30

Xu, Zheng, Hai-Yan Chen, and Jie Yu. "Generating Personalized Web Search Using Semantic Context." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/462782.

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The “one size fits the all” criticism of search engines is that when queries are submitted, the same results are returned to different users. In order to solve this problem, personalized search is proposed, since it can provide different search results based upon the preferences of users. However, existing methods concentrate more on the long-term and independent user profile, and thus reduce the effectiveness of personalized search. In this paper, the method captures the user context to provide accurate preferences of users for effectively personalized search. First, the short-term query context is generated to identify related concepts of the query. Second, the user context is generated based on the click through data of users. Finally, a forgetting factor is introduced to merge the independent user context in a user session, which maintains the evolution of user preferences. Experimental results fully confirm that our approach can successfully represent user context according to individual user information needs.

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Wang, Hanzhi, Zhewei Wei, Junhao Gan, Ye Yuan, Xiaoyong Du, and Ji-Rong Wen. "Edge-based local push for personalized PageRank." Proceedings of the VLDB Endowment 15, no. 7 (March 2022): 1376–89. http://dx.doi.org/10.14778/3523210.3523216.

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Personalized PageRank (PPR) is a popular node proximity metric in graph mining and network research. A single-source PPR (SSPPR) query asks for the PPR value of each node on the graph. Due to its importance and wide applications, decades of efforts have been devoted to the efficient processing of SSPPR queries. Among existing algorithms, LocalPush is a fundamental method for SSPPR queries and serves as a cornerstone for subsequent algorithms. In LocalPush , a push operation is a crucial primitive operation, which distributes the probability at a node u to ALL u 's neighbors via the corresponding edges. Although this push operation works well on unweighted graphs, unfortunately, it can be rather inefficient on weighted graphs. In particular, on unbalanced weighted graphs where only a few of these edges take the majority of the total weight among them, the push operation would have to distribute "insignificant" probabilities along those edges which just take the minor weights, resulting in expensive overhead. To resolve this issue, in this paper, we propose the EdgePush algorithm, a novel method for computing SSPPR queries on weighted graphs. EdgePush decomposes the aforementioned push operations in edge-based push , allowing the algorithm to operate at the edge level granularity. As a result, it can flexibly distribute the probabilities according to edge weights. Furthermore, our EdgePush allows a fine-grained termination threshold for each individual edge, leading to a superior complexity over LocalPush. Notably, we prove that EdgePush improves the theoretical query cost of LocalPush by an order of up to O ( n ) when the graph's weights are unbalanced. Our experimental results demonstrate that EdgePush significantly outperforms state-of-the-art baselines in terms of query efficiency on large motif-based and real-world weighted graphs.

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Zhang, Shu Dong, Yan Chen, and Bei Bei Gao. "Personalized Intelligent Information Retrieval Entrance Mechanism." Advanced Materials Research 108-111 (May 2010): 216–21. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.216.

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In order to solve the problems of difficulties and differences in expression existed in traditional information retrieval, this paper presents a personalized intelligent information retrieval entrance mechanism based on domain ontology, which takes full account of various types of users' domain knowledge level and provide relevant retrieval methods for guiding personalized information, makes all kinds of users in a particular query environment can fully and effectively express their queries intentions.

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Pan, Yaoxin, Shangsong Liang, Jiaxin Ren, Zaiqiao Meng, and Qiang Zhang. "Personalized, Sequential, Attentive, Metric-Aware Product Search." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–29. http://dx.doi.org/10.1145/3473337.

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The task of personalized product search aims at retrieving a ranked list of products given a user’s input query and his/her purchase history. To address this task, we propose the PSAM model, a Personalized, Sequential, Attentive and Metric-aware (PSAM) model, that learns the semantic representations of three different categories of entities, i.e., users, queries, and products, based on user sequential purchase historical data and the corresponding sequential queries. Specifically, a query-based attentive LSTM (QA-LSTM) model and an attention mechanism are designed to infer users dynamic embeddings, which is able to capture their short-term and long-term preferences. To obtain more fine-grained embeddings of the three categories of entities, a metric-aware objective is deployed in our model to force the inferred embeddings subject to the triangle inequality, which is a more realistic distance measurement for product search. Experiments conducted on four benchmark datasets show that our PSAM model significantly outperforms the state-of-the-art product search baselines in terms of effectiveness by up to 50.9% improvement under NDCG@20. Our visualization experiments further illustrate that the learned product embeddings are able to distinguish different types of products.

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34

Wang, Xin Yu, and Qing Song Zhang. "The Design and Research of Digital Service Platform Based on Semantic Web." Advanced Materials Research 760-762 (September 2013): 1808–11. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1808.

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With the application of semantic web technique and concept map theory, this paper constructs a personalized platform model based on semantic web, which is called digital library. The model consists of personalized module, information resource integration and process module, semantic analysis and process module, and query module. The function of each module is analyzed concretely.

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Hu, Zhao-Wei, and Jing Yang. "Trajectory Privacy Protection Based on Location Semantic Perception." International Journal of Cooperative Information Systems 28, no. 03 (September 2019): 1950006. http://dx.doi.org/10.1142/s0218843019500060.

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A personalized trajectory privacy protection method based on location semantic perception to achieve the personalized goal of privacy protection parameter setting and policy selection is proposed. The concept of user perception is introduced and a set of security samples that the user feels safe and has no risk of privacy leakage is set by the user’s personal perception. In addition, global privacy protection parameters are determined by calculating the mean values of multiple privacy protection parameters in the sample set. The concept of location semantics is also introduced. By anonymizing the real user with [Formula: see text] collaborative users that satisfy the different semantic conditions, [Formula: see text] query requests which do not have the exact same query content and contain precise location information of the user and the collaborative user are sent to ensure the accuracy of the query results and avoid privacy-leaks caused by the query content and type. Information leakage and privacy level values are tested for qualitative analysis and quantitative calculation of privacy protection efficacy to find that the proposed method indeed safeguards the privacy of mobile users. Finally, the feasibility and effectiveness of the algorithm are verified by simulation experiments.

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Sadesh, S., and R. C. Suganthe. "Effective Filtering of Query Results on Updated User Behavioral Profiles in Web Mining." Scientific World Journal 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/829126.

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Web with tremendous volume of information retrieves result for user related queries. With the rapid growth of web page recommendation, results retrieved based on data mining techniques did not offer higher performance filtering rate because relationships between user profile and queries were not analyzed in an extensive manner. At the same time, existing user profile based prediction in web data mining is not exhaustive in producing personalized result rate. To improve the query result rate on dynamics of user behavior over time, Hamilton Filtered Regime Switching User Query Probability (HFRS-UQP) framework is proposed. HFRS-UQP framework is split into two processes, where filtering and switching are carried out. The data mining based filtering in our research work uses the Hamilton Filtering framework to filter user result based on personalized information on automatic updated profiles through search engine. Maximized result is fetched, that is, filtered out with respect to user behavior profiles. The switching performs accurate filtering updated profiles using regime switching. The updating in profile change (i.e., switches) regime in HFRS-UQP framework identifies the second- and higher-order association of query result on the updated profiles. Experiment is conducted on factors such as personalized information search retrieval rate, filtering efficiency, and precision ratio.

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Chunmin Qiu, and Jie Shan. "Personalized Classification of Web Database Query Results Based on MKL." International Journal of Advancements in Computing Technology 5, no. 6 (March 31, 2013): 488–95. http://dx.doi.org/10.4156/ijact.vol5.issue6.57.

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Chawla, Suruchi. "Trust in Personalized Web Search based on Clustered Query Sessions." International Journal of Computer Applications 59, no. 7 (December 18, 2012): 36–44. http://dx.doi.org/10.5120/9563-4032.

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Kim, Kwang-Young, Kang-Seop Shim, and Seung-Jin Kwak. "A Personalized Retrieval System Based on Classification and User Query." Journal of the Korean Society for Library and Information Science 43, no. 3 (September 30, 2009): 163–80. http://dx.doi.org/10.4275/kslis.2009.43.3.163.

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Cai, Fei, Wanyu Chen, and Xinliang Ou. "Learning search popularity for personalized query completion in information retrieval." Journal of Intelligent & Fuzzy Systems 33, no. 4 (September 22, 2017): 2427–35. http://dx.doi.org/10.3233/jifs-17565.

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Yang, Xiao, Zhaoxin Zhang, and Qiang Wang. "Personalized Recommendation Based on Co-Ranking and Query-Based Collaborative Diffusion." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 29, 2013): 1649–50. http://dx.doi.org/10.1609/aaai.v27i1.8534.

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In this paper, we present an adaptive graph-based personalized recommendation method based on co-ranking and query-based collaborative diffusion. By utilizing the unique network structure of n-partite heterogeneous graph, we attempt to address the problem of personalized recommendation in a two-layer ranking process with the help of reasonable measure of high and low order relationships and analyzing the representation of user’s preference in the graph. The experiments show that this algorithm can outperform the traditional CF methods and achieve competitive performance compared with many model-based and graph-based recommendation methods, and have better scalability and flexibility.

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Neumann, Marion, Babak Ahmadi, and Kristian Kersting. "Markov Logic Sets: Towards Lifted Information Retrieval Using PageRank and Label Propagation." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 447–52. http://dx.doi.org/10.1609/aaai.v25i1.7906.

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Inspired by “GoogleTM Sets” and Bayesian sets, we consider the problem of retrieving complex objects and relations among them, i.e., ground atoms from a logical concept, given a query consisting of a few atoms from that concept. We formulate this as a within-network relational learning problem using few labels only and describe an algorithm that ranks atoms using a score based on random walks with restart (RWR): the probability that a random surfer hits an atom starting from the query atoms. Specifically, we compute an initial ranking using personalized PageRank. Then, we find paths of atoms that are connected via their arguments, variablize the ground atoms in each path, in order to create features for the query. These features are used to re-personalize the original RWR and to finally compute the set completion, based on Label Propagation. Moreover, we exploit that RWR techniques can naturally be lifted and show that lifted inference for label propagation is possible. We evaluate our algorithm on a realworld relational dataset by finding completions of sets of objects describing the Roman city of Pompeii. We compare to Bayesian sets and show that our approach gives very reasonable set completions.

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Ran, Wei, Hui Chen, Taokai Xia, Yosuke Nishimura, Chaopeng Guo, and Youyu Yin. "Online Personalized Preference Learning Method Based on In-Formative Query for Lane Centering Control Trajectory." Sensors 23, no. 11 (May 31, 2023): 5246. http://dx.doi.org/10.3390/s23115246.

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The personalization of autonomous vehicles or advanced driver assistance systems has been a widely researched topic, with many proposals aiming to achieve human-like or driver-imitating methods. However, these approaches rely on an implicit assumption that all drivers prefer the vehicle to drive like themselves, which may not hold true for all drivers. To address this issue, this study proposes an online personalized preference learning method (OPPLM) that utilizes a pairwise comparison group preference query and the Bayesian approach. The proposed OPPLM adopts a two-layer hierarchical structure model based on utility theory to represent driver preferences on the trajectory. To improve the accuracy of learning, the uncertainty of driver query answers is modeled. In addition, informative query and greedy query selection methods are used to improve learning speed. To determine when the driver’s preferred trajectory has been found, a convergence criterion is proposed. To evaluate the effectiveness of the OPPLM, a user study is conducted to learn the driver’s preferred trajectory in the curve of the lane centering control (LCC) system. The results show that the OPPLM can converge quickly, requiring only about 11 queries on average. Moreover, it accurately learned the driver’s favorite trajectory, and the estimated utility of the driver preference model is highly consistent with the subject evaluation score.

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Zhou, Dong, Séamus Lawless, Xuan Wu, Wenyu Zhao, and Jianxun Liu. "A study of user profile representation for personalized cross-language information retrieval." Aslib Journal of Information Management 68, no. 4 (July 18, 2016): 448–77. http://dx.doi.org/10.1108/ajim-06-2015-0091.

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Purpose – With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion. Design/methodology/approach – The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as various latent semantic models trained on monolingual documents and cross-lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods. Findings – Experimental results suggest that latent semantic-weighted user profile representation techniques are superior to frequency-based methods, and are particularly suitable for users with a sufficient amount of historical data. The study also confirmed that user profiles represented by latent semantic models trained on a cross-lingual level gained better performance than the models trained on a monolingual level. Originality/value – Previous studies on personalized information retrieval systems have primarily investigated user profiles and personalization strategies on a monolingual level. The effect of utilizing such monolingual profiles for personalized CLIR remains unclear. The current study fills the gap by a comprehensive study of user profile representation for personalized CLIR and a novel personalized CLIR evaluation methodology to ensure repeatable and controlled experiments can be conducted.

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Yoo, Donghee. "Hybrid query processing for personalized information retrieval on the Semantic Web." Knowledge-Based Systems 27 (March 2012): 211–18. http://dx.doi.org/10.1016/j.knosys.2011.10.004.

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KWON, O., and M. SHIN. "LACO: A location-aware cooperative query system for securely personalized services." Expert Systems with Applications 34, no. 4 (May 2008): 2966–75. http://dx.doi.org/10.1016/j.eswa.2007.05.022.

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Almaslukh, Abdulaziz, Yunfan Kang, and Amr Magdy. "Temporal Geo-Social Personalized Keyword Search Over Streaming Data." ACM Transactions on Spatial Algorithms and Systems 7, no. 4 (December 31, 2021): 1–28. http://dx.doi.org/10.1145/3473006.

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The unprecedented rise of social media platforms, combined with location-aware technologies, has led to continuously producing a significant amount of geo-social data that flows as a user-generated data stream. This data has been exploited in several important use cases in various application domains. This article supports geo-social personalized queries in streaming data environments. We define temporal geo-social queries that provide users with real-time personalized answers based on their social graph. The new queries allow incorporating keyword search to get personalized results that are relevant to certain topics. To efficiently support these queries, we propose an indexing framework that provides lightweight and effective real-time indexing to digest geo-social data in real time. The framework distinguishes highly dynamic data from relatively stable data and uses appropriate data structures and a storage tier for each. Based on this framework, we propose a novel geo-social index and adopt two baseline indexes to support the addressed queries. The query processor then employs different types of pruning to efficiently access the index content and provide a real-time query response. The extensive experimental evaluation based on real datasets has shown the superiority of our proposed techniques to index real-time data and provide low-latency queries compared to existing competitors.

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Li, Wanwu, Lin Liu, Hui Zhang, Jinhong Li, and Zhi Wang. "Examination Database and Online Paper Forming Algorithm for Mobile Personalized Learning Test." Indonesian Journal Of Educational Research and Review 6, no. 1 (April 12, 2023): 88–98. http://dx.doi.org/10.23887/ijerr.v6i1.54381.

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It is crucial to advance network education exam informatization and mobile learning. One of them is the creation of a database system for exams, which contains crucial information. This study aims to analyze and solves key technologies including constraint check paper forming algorithms. This study using fuzzy query methods and spatial queries design a single exam table structure and exam papers for the exam database. The process of this study including collect and sort the complete set of GIS exams at major universities across the country, design a variety of GIS single questions, and build an exam database system. The result of this research is a personalized random paper formation system and query and spatial analysis by college and area for exam papers for the first time. It describes rich functions and stable performance by testing. The built system becomes an indispensable technical support instead of paper-based exams for information exams and scientific exams of many colleges and universities.

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Li, Yiming, Yanyan Shen, Lei Chen, and Mingxuan Yuan. "Zebra: When Temporal Graph Neural Networks Meet Temporal Personalized PageRank." Proceedings of the VLDB Endowment 16, no. 6 (February 2023): 1332–45. http://dx.doi.org/10.14778/3583140.3583150.

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Temporal graph neural networks (T-GNNs) are state-of-the-art methods for learning representations over dynamic graphs. Despite the superior performance, T-GNNs still suffer from high computational complexity caused by the tedious recursive temporal message passing scheme, which hinders their applicability to large dynamic graphs. To address the problem, we build the theoretical connection between the temporal message passing scheme adopted by T-GNNs and the temporal random walk process on dynamic graphs. Our theoretical analysis indicates that it would be possible to select a few influential temporal neighbors to compute a target node's representation without compromising the predictive performance. Based on this finding, we propose to utilize T-PPR, a parameterized metric for estimating the influence score of nodes on evolving graphs. We further develop an efficient single-scan algorithm to answer the top- k T-PPR query with rigorous approximation guarantees. Finally, we present Zebra, a scalable framework that accelerates the computation of T-GNN by directly aggregating the features of the most prominent temporal neighbors returned by the top- k T-PPR query. Extensive experiments have validated that Zebra can be up to two orders of magnitude faster than the state-of-the-art T-GNNs while attaining better performance.

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Cai, Xiaoyan, Junwei Han, Shirui Pan, and Libin Yang. "Heterogeneous Information Network Embedding based Personalized Query-Focused Astronomy Reference Paper Recommendation." International Journal of Computational Intelligence Systems 11, no. 1 (2018): 591. http://dx.doi.org/10.2991/ijcis.11.1.44.

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