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Journal articles on the topic 'Query suggestion'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Zha, Zheng-Jun, Linjun Yang, Tao Mei, et al. "Visual query suggestion." ACM Transactions on Multimedia Computing, Communications, and Applications 6, no. 3 (2010): 1–19. http://dx.doi.org/10.1145/1823746.1823747.

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2

Slifkin, Lawrence, and Marilyn Vogel. "Lubrication Article Prompts Suggestion and Suggestive Query." Physics Today 52, no. 11 (1999): 82. http://dx.doi.org/10.1063/1.882889.

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Kato, Makoto P., Tetsuya Sakai, and Katsumi Tanaka. "When do people use query suggestion? A query suggestion log analysis." Information Retrieval 16, no. 6 (2013): 725–46. http://dx.doi.org/10.1007/s10791-012-9216-x.

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Meng, Lingling. "A Survey on Query Suggestion." International Journal of Hybrid Information Technology 7, no. 6 (2014): 43–56. http://dx.doi.org/10.14257/ijhit.2014.7.6.04.

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Hamzaoui, Amel, Pierre Letessier, Alexis Joly, Olivier Buisson, and Nozha Boujemaa. "Object-based visual query suggestion." Multimedia Tools and Applications 68, no. 2 (2013): 429–54. http://dx.doi.org/10.1007/s11042-012-1340-5.

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6

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 (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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Peng, Xue-Ping, Zhen-Dong Niu, and Sheng Huang. "Query Suggestion Based on the Query Semantics and Clickthrough Data." Advanced Science Letters 9, no. 1 (2012): 748–53. http://dx.doi.org/10.1166/asl.2012.2517.

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Tuan, Luu Anh, and Jung-Jae Kim. "Automatic Suggestion for PubMed Query Reformulation." Journal of Computing Science and Engineering 6, no. 2 (2012): 161–67. http://dx.doi.org/10.5626/jcse.2012.6.2.161.

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9

Jiang, Di, Kenneth Wai-Ting Leung, Lingxiao Yang, and Wilfred Ng. "Query suggestion with diversification and personalization." Knowledge-Based Systems 89 (November 2015): 553–68. http://dx.doi.org/10.1016/j.knosys.2015.09.003.

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Anagnostopoulos, Ioannis, Gerasimos Razis, Phivos Mylonas, and Christos-Nikolaos Anagnostopoulos. "Semantic query suggestion using Twitter Entities." Neurocomputing 163 (September 2015): 137–50. http://dx.doi.org/10.1016/j.neucom.2014.12.090.

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Esch, Maria, Jinbo Chen, Stephan Weise, Keywan Hassani-Pak, Uwe Scholz, and Matthias Lange. "A Query Suggestion Workflow for Life Science IR-Systems." Journal of Integrative Bioinformatics 11, no. 2 (2014): 15–26. http://dx.doi.org/10.1515/jib-2014-237.

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Summary Information Retrieval (IR) plays a central role in the exploration and interpretation of integrated biological datasets that represent the heterogeneous ecosystem of life sciences. Here, keyword based query systems are popular user interfaces. In turn, to a large extend, the used query phrases determine the quality of the search result and the effort a scientist has to invest for query refinement. In this context, computer aided query expansion and suggestion is one of the most challenging tasks for life science information systems. Existing query front-ends support aspects like spelling correction, query refinement or query expansion. However, the majority of the front-ends only make limited use of enhanced IR algorithms to implement comprehensive and computer aided query refinement workflows. In this work, we present the design of a multi-stage query suggestion workflow and its implementation in the life science IR system LAILAPS. The presented workflow includes enhanced tokenisation, word breaking, spelling correction, query expansion and query suggestion ranking. A spelling correction benchmark with 5,401 queries and manually selected use cases for query expansion demonstrate the performance of the implemented workflow and its advantages compared with state-of-the-art systems.

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Zhang, Xiaojuan, Xixi Jiang, and Jiewen Qin. "Time-aware query suggestion diversification for temporally ambiguous queries." Electronic Library 38, no. 4 (2020): 725–44. http://dx.doi.org/10.1108/el-12-2019-0296.

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Purpose The purpose of this study is to generate diversified results for temporally ambiguous queries and the candidate queries are ensured to have a high coverage of subtopics, which are derived from different temporal periods. Design/methodology/approach Two novel time-aware query suggestion diversification models are developed by integrating semantics and temporality information involved in queries into two state-of-the-art explicit diversification algorithms (i.e. IA-select and xQuaD), respectively, and then specifying the components on which these two models rely on. Most importantly, first explored is how to explicitly determine query subtopics for each unique query from the query log or clicked documents and then modeling the subtopics into query suggestion diversification. The discussion on how to mine temporal intent behind a query from query log is also followed. Finally, to verify the effectiveness of the proposal, experiments on a real-world query log are conducted. Findings Preliminary experiments demonstrate that the proposed method can significantly outperform the existing state-of-the-art methods in terms of producing the candidate query suggestion for temporally ambiguous queries. Originality/value This study reports the first attempt to generate query suggestions indicating diverse interested time points to the temporally ambiguous (input) queries. The research will be useful in enhancing users’ search experience through helping them to formulate accurate queries for their search tasks. In addition, the approaches investigated in the paper are general enough to be used in many domains; that is, experimental information retrieval systems, Web search engines, document archives and digital libraries.

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ha, Rek, and Sushil Kumar. "Design of Query Suggestion using Rank Updater." International Journal of Computer Trends and Technology 11, no. 5 (2014): 220–27. http://dx.doi.org/10.14445/22312803/ijctt-v11p147.

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Banu, W. Aisha, P. Sheik Abdul Khader, and R. Shriram. "Query Suggestion Generation Methods for Mobile Phones." Information Technology Journal 11, no. 8 (2012): 1056–62. http://dx.doi.org/10.3923/itj.2012.1056.1062.

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Xu, Zheng, Xiangfeng Luo, Jie Yu, and Weimin Xu. "Mining Web search engines for query suggestion." Concurrency and Computation: Practice and Experience 23, no. 10 (2010): 1101–13. http://dx.doi.org/10.1002/cpe.1689.

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Meng, Lingling, Runqing Huang, and Junzhong Gu. "Query Suggestion Based on Theme and Context." International Journal of u- and e-Service, Science and Technology 7, no. 4 (2014): 263–76. http://dx.doi.org/10.14257/ijunesst.2014.7.4.24.

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Satriady, Wildhan, Moch Arif Bijaksana, and Kemas M. Lhaksmana. "Quranic Latin Query Correction as a Search Suggestion." Procedia Computer Science 157 (2019): 183–90. http://dx.doi.org/10.1016/j.procs.2019.08.156.

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18

Bar-Yossef, Ziv, and Maxim Gurevich. "Mining search engine query logs via suggestion sampling." Proceedings of the VLDB Endowment 1, no. 1 (2008): 54–65. http://dx.doi.org/10.14778/1453856.1453868.

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19

Tahery, Saedeh, and Saeed Farzi. "Customized query auto-completion and suggestion — A review." Information Systems 87 (January 2020): 101415. http://dx.doi.org/10.1016/j.is.2019.101415.

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20

Chen, Wanyu, Fei Cai, Honghui Chen, and Maarten de Rijke. "Hierarchical neural query suggestion with an attention mechanism." Information Processing & Management 57, no. 6 (2020): 102040. http://dx.doi.org/10.1016/j.ipm.2019.05.001.

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21

Kim, Young-An, and Gun-Woo Park. "An Efficient Extended Query Suggestion System Using the Analysis of Users' Query Patterns." Journal of Korean Institute of Communications and Information Sciences 37, no. 7C (2012): 619–26. http://dx.doi.org/10.7840/kics.2012.37.7c.619.

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22

Li, Sheng, and Junhu Wang. "An XSketch-based spelling suggestion approach for XML keyword search." International Journal of Web Information Systems 10, no. 3 (2014): 245–62. http://dx.doi.org/10.1108/ijwis-03-2014-0008.

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Purpose – The purpose of this paper is to study the spelling suggestion (SS) problem for extensible markup language (XML) keyword search, which provides users with alternative queries that may better express users search intention. Design/methodology/approach – To return the suggested queries more efficiently, the authors evaluate the quality of the query by estimating the selectivity and quality of each query pattern. The selectivity estimation is based on the XSketch synopsis, which summarizes the structure and value distribution of the original XML data source. The authors propose an approach to generating the top-K query candidates. Findings – Experiments with real datasets verify the effectiveness and efficiency of the authors' approach. Originality/value – The authors proposed a SS approach based on the XSketch summary.

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23

Bonart, Malte, Anastasiia Samokhina, Gernot Heisenberg, and Philipp Schaer. "An investigation of biases in web search engine query suggestions." Online Information Review 44, no. 2 (2019): 365–81. http://dx.doi.org/10.1108/oir-11-2018-0341.

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Purpose Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017. Design/methodology/approach This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test the framework, the authors collected data from the Google, Bing and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party or age and with regards to the stability of the suggestions over time. Findings By using the framework, the authors located three semantic clusters within the data set: suggestions related to politics and economics, location information and personal and other miscellaneous topics. Among other effects, the results of the analysis show a small bias in the form that male politicians receive slightly fewer suggestions on “personal and misc” topics. The stability analysis of the suggested terms over time shows that some suggestions are prevalent most of the time, while other suggestions fluctuate more often. Originality/value This study proposes a novel framework to automatically identify biases in web search engine query suggestions for person-related searches. Applying this framework on a set of person-related query suggestions shows first insights into the influence search engines can have on the query process of users that seek out information on politicians.

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24

Qi, Shuyao, Dingming Wu, and Nikos Mamoulis. "Location Aware Keyword Query Suggestion Based on Document Proximity." IEEE Transactions on Knowledge and Data Engineering 28, no. 1 (2016): 82–97. http://dx.doi.org/10.1109/tkde.2015.2465391.

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25

Li, Lusong, and Jing Li. "MQSS: multimodal query suggestion and searching for video search." Multimedia Tools and Applications 54, no. 1 (2010): 55–68. http://dx.doi.org/10.1007/s11042-010-0540-0.

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26

Fattahi, Rahmatollah, Mehri Parirokh, Mohammd Hosien Dayyani, Abdolrasoul Khosravi, and Mojgan Zareivenovel. "Effectiveness of Google keyword suggestion on users’ relevance judgment." Electronic Library 34, no. 2 (2016): 302–14. http://dx.doi.org/10.1108/el-03-2015-0035.

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Purpose – One of the most effective ways information retrieval (IR) systems including Web search engines can improve relevance performance is to provide their users with tools for facilitating query expansion. Search engines such as Google provide users with keyword suggest tools. This paper aims to investigate users’ criteria in relevance judgment regarding Google’s keywords suggest tool and to see how such keywords would lead to more relevant results from the viewpoint of users. Design/methodology/approach – Through a mixed method approach, quantitative and qualitative data were collected from 60 postgraduate students at Ferdowsi University of Mashhad, Iran, using four different instruments (questionnaire, thinking aloud technique, query logs and interviews). Findings – Among other criteria, the “relation between suggested keywords and the information need” (with the mean rate of 3.53 of four) was considered the most important by searchers in selecting suggested keywords for query expansion. Also, the “relation between suggested Keywords and the retrieved items” (with the mean rate of 3.62) was considered the second most important criterion in judging the relevance of the retrieved results. The participants agreed that the suggested keywords by Google improved the retrieval relevance. The content analysis of the participants’ aloud-thinking sessions and the interviews approved such findings. Originality/value – This research makes a contribution to the need of designers of IR systems regarding the use of add words for query expansion. It also helps librarians how to instruct searchers with expanding their queries to retrieve more relevant results. Another contribution of the study is the identification of a number of new relevance judgment criteria for Web-based environments.

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27

Chen, Yan, and Yan Qing Zhang. "A personalised query suggestion agent based on query-concept bipartite graphs and Concept Relation Trees." International Journal of Advanced Intelligence Paradigms 1, no. 4 (2009): 398. http://dx.doi.org/10.1504/ijaip.2009.026761.

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Qumsiyeh, Rani, and Yiu-Kai Ng. "Assisting web search using query suggestion based on word similarity measure and query modification patterns." World Wide Web 17, no. 5 (2013): 1141–60. http://dx.doi.org/10.1007/s11280-013-0235-3.

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Song, Jun, Jun Xiao, Fei Wu, et al. "Hierarchical Contextual Attention Recurrent Neural Network for Map Query Suggestion." IEEE Transactions on Knowledge and Data Engineering 29, no. 9 (2017): 1888–901. http://dx.doi.org/10.1109/tkde.2017.2700392.

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Ungrangsi, Rachanee, Chutiporn Anutariya, and Vilas Wuwongse. "Enhancing Folksonomy-Based Content Retrieval with Semantic Web Technology." International Journal on Semantic Web and Information Systems 6, no. 1 (2010): 19–38. http://dx.doi.org/10.4018/jswis.2010010102.

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While Flickr, a widely-known photo sharing system, allows users to describe their own photos with tags (aka. folksonomy tags) for indexing purposes, its tag-based photo retrieval function is severely hampered by the inherent nature of folksonomy tags. This paper presents SemFlickr, an application which enhances the search in Flickr with its semantic query suggestion feature. SemFlickr employs SQORE, an ontology retrieval system, to retrieve relevant ontologies from the Semantic Web and then derives query term suggestions from those ontologies. To ensure that the highly related photos will appear at the top of the results, SemFlickr takes the ontological relations among the given query terms to assign tag scores and then generates its ranked results. Experimental outcomes are encouraging and reveal a number of useful insights for developing applications that integrate the Semantic Web and Web 2.0 together.

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Palekar, Vikas R., Miss Shital P. Dhok, and Prof Dinesh D. Gawande. "Assisting Text Annotation based on Attributes Suggestion Strategy using Content and Query." IJARCCE 6, no. 3 (2017): 1030–34. http://dx.doi.org/10.17148/ijarcce.2017.63239.

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LI, Ya-Nan, Bin WANG, Jin-Tao LI, and Peng LI. "Indexing the World Wide Web: Intelligent Query Suggestion Based on Term Relation Network." Journal of Software 22, no. 8 (2011): 1771–84. http://dx.doi.org/10.3724/sp.j.1001.2011.03852.

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Liao, Zhen, Daxin Jiang, Enhong Chen, Jian Pei, Huanhuan Cao, and Hang Li. "Mining Concept Sequences from Large-Scale Search Logs for Context-Aware Query Suggestion." ACM Transactions on Intelligent Systems and Technology 3, no. 1 (2011): 1–40. http://dx.doi.org/10.1145/2036264.2036281.

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Chen, Lin‐Chih. "Term suggestion with similarity measure based on semantic analysis techniques in query logs." Online Information Review 35, no. 1 (2011): 9–33. http://dx.doi.org/10.1108/14684521111113560.

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Ruotsalo, Tuukka, Giulio Jacucci, and Samuel Kaski. "Interactive faceted query suggestion for exploratory search: Whole‐session effectiveness and interaction engagement." Journal of the Association for Information Science and Technology 71, no. 7 (2019): 742–56. http://dx.doi.org/10.1002/asi.24304.

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Rossiello, Gaetano, Annalina Caputo, Pierpaolo Basile, and Giovanni Semeraro. "Modeling concepts and their relationships for corpus-based query auto-completion." Open Computer Science 9, no. 1 (2019): 212–25. http://dx.doi.org/10.1515/comp-2019-0015.

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AbstractQuery auto-completion helps users to formulate their information needs by providing suggestion lists at every typed key. This task is commonly addressed by exploiting query logs and the approaches proposed in the literature fit well in web scale scenarios, where usually huge amounts of past user queries can be analyzed to provide reliable suggestions. However, when query logs are not available, e.g. in enterprise or desktop search engines, these methods are not applicable at all. To face these challenging scenarios, we present a novel corpus-based approach which exploits the textual content of an indexed document collection in order to dynamically generate query completions. Our method extracts informative text fragments from the corpus and it combines them using a probabilistic graphical model in order to capture the relationships between the extracted concepts. Using this approach, it is possible to automatically complete partial queries with significant suggestions related to the keywords already entered by the user without requiring the analysis of the past queries. We evaluate our system through a user study on two different real-world document collections. The experiments show that our method is able to provide meaningful completions outperforming the state-of-the art approach.

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Kuriakose, Aju Tom, and Sobhana N.V. "Spatiotemporal Keyword Query Suggestion Based On Document Proximity and K-Means Method– A Review." IJARCCE 6, no. 3 (2017): 671–75. http://dx.doi.org/10.17148/ijarcce.2017.63157.

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Vidinli, I. Bahattin, and Rifat Ozcan. "New query suggestion framework and algorithms: A case study for an educational search engine." Information Processing & Management 52, no. 5 (2016): 733–52. http://dx.doi.org/10.1016/j.ipm.2016.02.001.

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39

Rehman Khan, Haseeb Ur, Chen Kim Lim, Minhaz Farid Ahmed, Kian Lam Tan, and Mazlin Bin Mokhtar. "Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism." Sustainability 13, no. 15 (2021): 8141. http://dx.doi.org/10.3390/su13158141.

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Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.

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Huang, Chien-Kang, Lee-Feng Chien, and Yen-Jen Oyang. "Relevant term suggestion in interactive web search based on contextual information in query session logs." Journal of the American Society for Information Science and Technology 54, no. 7 (2003): 638–49. http://dx.doi.org/10.1002/asi.10256.

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Patil, Asst Prof Sonal, and Ankush Mahajan. "Design and Architecture for Web Graph Mining Base Recommender System for Query, Image and Social Network using Query Suggestion Algorithm and Heat Diffusion Method." IOSR Journal of Computer Engineering 16, no. 1 (2014): 16–23. http://dx.doi.org/10.9790/0661-16181623.

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Gueddana, Amor, Rihab Chatta, and Moez Attia. "CNOT-based design and query management in quantum relational databases." International Journal of Quantum Information 12, no. 04 (2014): 1450023. http://dx.doi.org/10.1142/s0219749914500233.

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In this work, we propose a complete design of a quantum relational multi-tables database. We illustrate how to perform basic and advanced queries to insert, update, delete and select records from single or joined digitized tables. A suggestion of a Quantum Query Language (QQL) is then addressed and we illustrate for a simple quantum database how QQL performs. We highlight the used scheme allowing to traduce the QQL semantics to the corresponding CNOT-based implementation and we underline the evolution of the amplitude of probability of the superposed states contained in the tables.

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Li, Ping, Ming Liang Cui, Zhen Shan Hou, Liu Liu Wei, Wen Hao Ying, and Wan Li Zuo. "Session Segmentation Method Based on Naïve Bayes Model." Advanced Engineering Forum 6-7 (September 2012): 576–82. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.576.

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Session segmentation can not only contribute a lot to the further and deeper analysis of user’s search behavior but also act as the foundation of other retrieval process researches based on users’ complicated search behaviors. This paper proposes a session boundary discrimination model utilizing time interval and query likelihood on the basis of Naive Bayes Model. Compared with previous study, the model proposed in this paper shows a prominent improvement through experiment in three aspects, which is: recall ratio, precision ratio and value F. Owing to its advantage in session boundary discrimination, the application of the model can serve as a tool in fields like personalized information retrieval, query suggestion, search activity analysis and other fields which is related to search results improvement.

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Susanto, Edi, and Fidianti SE. "ANALISIS PERBANDINGAN SISTEM ANTRIAN MODEL M/M/1 DAN M/M/S UNTUK PELAYANAN PBB DI DPKAD KABUPATEN PURWAKARTA." Eqien: Jurnal Ekonomi dan Bisnis 3, no. 2 (2018): 19–30. http://dx.doi.org/10.34308/eqien.v3i2.25.

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Research on the comparative analysis of single channel queuing system and multiple channel query system with 2 fasilties and 3 facilities. This study aims to investigate how the optimal number of facilities due to the large queues waiting for their turn receive services especially Pajak Bumi dan Bangunan in the Office of Dinas Pengelola Keuangan dan Aset Daerah Kabupaten Purwakarta.The analytical method used is the model M / M / 1 for single channel system query and M / M / S is used for multiple channel query system. based on the results of the analysis using the model found that the results of a query using a single channel system services Pajak Bumi dan Bangunan certainly not optimal due to the ability of the service itself 12 people per hour. On the other side using the model M / M / S found the average amount of time service during rush hour period 10:00 to 11:00 of 0:15 hours or can be 9 minute and an average queue length 1.0667. In contrast to the number 3 facility, the taxpayer at a busy period 10:00 to 11:00 can wait with a difference of only 0.0923 hours or 5:54 minutes and the number of queues waiting with an average of 0.1446.Suggestion research obtained in order to use the three facilities while maintaining the service with optimal. So that all service activities will not be interrupted and did not make the queue longer taxpayer.Keyword : Queue, single channel queuing system and multiple channel query system, services, tax payer

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45

Garigliotti, Darío. "Task-based support in search engines." ACM SIGIR Forum 54, no. 1 (2020): 1–2. http://dx.doi.org/10.1145/3451964.3451980.

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Web search has become a key technology on which people rely daily for getting information about almost everything. The evolution of the search experience has also shaped the expectations of people about it. Many users seem to expect today's web search engines to behave like a kind of "wise interpreter," capable of understanding the meaning behind a search query, realizing its current context, and responding to it directly and appropriately. Search by meaning, or semantic search, encompasses a large portion of information retrieval (IR) research devoted to study more meaningful representations of the information need expressed by the user query. Entity cards, direct displays, and verticals are examples of how major commercial search engines have indeed responded to user expectations, capitalizing on query understanding. Search is usually performed with a specific goal underlying the query. In many cases, this goal consists of a nontrivial task to be completed. Current search engines support a small set of basic tasks, and most of the knowledge-intensive workload for supporting more complex tasks is left to the user. Task-based search can be viewed as an information access paradigm that aims to enhance search engines with functionalities for recognizing the underlying tasks in searches and providing support for task completion. The research presented in this thesis focuses on utilizing and extending methods and techniques from semantic search in the next stage of the evolution of search engines, namely, to support users in achieving their tasks. Our work can be grouped in three grand themes: (1) Entity type information for entity retrieval : we conduct a systematic evaluation and analysis of methods for type-aware entity retrieval, in terms of three main dimensions. Also, we revisit the problem of hierarchical target type identification, present a state-of-the-art supervised learning method, and analyze the usage of automatically identified target entity types for type-aware entity retrieval; (2) Entity-oriented search intents : we propose a categorization scheme for entity-oriented search intents, and study the distributions of entity intent categories per entity type. We further develop a method for constructing a knowledge base of entity-oriented search intents; and (3) Task-based search : we design a probabilistic generative framework for task-based query suggestion, and principledly estimate each of its components. Furthermore, we introduce the problems of query-based task recommendation and mission-based task recommendation, and establish respective methods as suitable baselines.

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Park, Byung Il, and Taewoo Roh. "Chinese multinationals’ FDI motivations: suggestion for a new theory." International Journal of Emerging Markets 14, no. 1 (2019): 70–90. http://dx.doi.org/10.1108/ijoem-03-2017-0104.

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Purpose The purpose of this paper is to complement the conventional international business (IB) theory, the OLI perspective, which is good at explaining the foreign direct investments (FDIs) undertaken by developed market multinational corporations (DMNCs). This study also suggests a new theoretical framework, namely, the OILL paradigm, that is able to encompass FDIs from emerging market multinational corporations (EMNCs) toward developed economies. Design/methodology/approach The data comprising 206 Chinese MNCs, which completed international mergers and acquisitions (IMAs), were obtained from Zephyr. By using these data, logical regressions are conducted to statistically confirm that we should not omit the learning motivation if we want to adequately understand the FDI phenomenon by encompassing investment flow from developing (or emerging) to developed countries. Findings The results based on this data set indicate that EMNCs often try to enter developed economies with the motivation to seek sophisticated foreign host knowledge that is not available internally. In particular, they tend to use IMA strategies when they want to learn from heterogeneity (i.e. inter-industry mergers and acquisitions) and absorb advanced technologies from DMNCs. Research limitations/implications By shedding light on the recent new trend in FDI (i.e. FDI from emerging countries to developed economies), the study provides useful theoretical implications, as well as suggesting scholarly contributions. However, we should acknowledge that there are some limitations to this study. First, the study explores only Chinese MNCs. Second, learning motivations need to be minutely and precisely measured by other studies. Third, this study argues that FDI from EMNCs to DMNCs is triggered by the former’s motivation concerning knowledge acquisition. However, the type of knowledge should be considered, and this is perhaps another avenue for future research. Practical implications Conventional IB theories, such as the OLI paradigm and internalization theory, have long sought to answer the question of why DMNCs go for foreign markets, in spite of the presence of the liabilities of foreignness, and focused on their main investment motivations (i.e. market-seeking, efficiency-seeking and resource-seeking motivations). For this reason, these theories do not adequately capture the primary FDI motivations of EMNCs, and consequently, they are unable to see the big picture when it comes to the FDI phenomenon. Based on this idea, the authors complement the well-known triad motivations (i.e. market-seeking, efficiency-seeking and resource-seeking motivations) by adding the knowledge-seeking motive and contribute to the evolution of IB theories by suggesting a new theory, which is the OILL paradigm. Originality/value The study contributes to the extant literature in the field of IB in two key ways. First, it examines EMNCs’ central motivations in conducting FDI where empirical research is sparse. By doing this, this paper attempts to solve the query indicated above (i.e. why MNCs choose FDI in spite of the presence of the liabilities of foreignness), and it offers a new theory (i.e. the OILL paradigm).

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Han, Jialong, Aixin Sun, Haisong Zhang, Chenliang Li, and Shuming Shi. "CASE: Context-Aware Semantic Expansion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7871–78. http://dx.doi.org/10.1609/aaai.v34i05.6293.

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In this paper, we define and study a new task called Context-Aware Semantic Expansion (CASE). Given a seed term in a sentential context, we aim to suggest other terms that well fit the context as the seed. CASE has many interesting applications such as query suggestion, computer-assisted writing, and word sense disambiguation, to name a few. Previous explorations, if any, only involve some similar tasks, and all require human annotations for evaluation. In this study, we demonstrate that annotations for this task can be harvested at scale from existing corpora, in a fully automatic manner. On a dataset of 1.8 million sentences thus derived, we propose a network architecture that encodes the context and seed term separately before suggesting alternative terms. The context encoder in this architecture can be easily extended by incorporating seed-aware attention. Our experiments demonstrate that competitive results are achieved with appropriate choices of context encoder and attention scoring function.

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Latuny, Wilma, Victor Oryon Lawalata, Daniel Bunga Pailin, and Rahman Ohoirenan. "Sentiment Analysis of Consumers for Determining the Packaging Features of Eucalyptus Oil Products." Jurnal Ilmiah Teknik Industri 20, no. 1 (2021): 71–80. http://dx.doi.org/10.23917/jiti.v20i1.13461.

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This study aims to accurately predict eucalyptus oil packaging features and extract the most features to be improved for redesigning eucalyptus oil packaging. This research begins with taking consumer comments using a power query and then processing it using the data mining method and processed using WEKA to find sentiment analysis and accuracy of consumer comments regarding eucalyptus oil products. This study obtained the tendency of comments on each attribute with an assessment of the accuracy for all classes of 83% and each positive sentiment 3% of comments and 57% of comments for negative courses. The sentiment that shows the packaging tends to be normal at 20%, which is interpreted as neutral. This research can provide a suggestion to redesign the packaging based on the commentary sentiment of eucalyptus oil.

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Yadav, Dharminder, Himani Maheshwari, and Umesh Chandra. "An Approach Towards Hotel Recommender System." Journal of Computational and Theoretical Nanoscience 17, no. 6 (2020): 2605–12. http://dx.doi.org/10.1166/jctn.2020.8936.

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Recommendation Systems (RS) suggest the right item to the right user. It predicts the user’s rating to an item and based on this rating RS provides the suggestion to users. In today’s world many online applications are already using the Recommendation system that provides a recommendation for a particular item like books, movies, music etc. in an automated fashion. This paper proposed a system that helps to find the best suitable hotel in a given geographical area according to the user query by using library “recommenderlab” in R. This study proposed a system that gives the best hotel available according to the user rating available in database. User makes their decision according to their recommendation provides by the proposed system for finding best suitable hotel from available database and shows on the map by using a leaflet map package.

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Chen, Jia, Jiaxin Mao, Yiqun Liu, et al. "A Hybrid Framework for Session Context Modeling." ACM Transactions on Information Systems 39, no. 3 (2021): 1–35. http://dx.doi.org/10.1145/3448127.

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Understanding user intent is essential for various retrieval tasks. By leveraging contextual information within sessions, e.g., query history and user click behaviors, search systems can capture user intent more accurately and thus perform better. However, most existing systems only consider intra-session contexts and may suffer from the problem of lacking contextual information, because short search sessions account for a large proportion in practical scenarios. We believe that in these scenarios, considering more contexts, e.g., cross-session dependencies, may help alleviate the problem and contribute to better performance. Therefore, we propose a novel Hybrid framework for Session Context Modeling (HSCM), which realizes session-level multi-task learning based on the self-attention mechanism. To alleviate the problem of lacking contextual information within current sessions, HSCM exploits the cross-session contexts by sampling user interactions under similar search intents in the historical sessions and further aggregating them into the local contexts. Besides, application of the self-attention mechanism rather than RNN-based frameworks in modeling session-level sequences also helps (1) better capture interactions within sessions, (2) represent the session contexts in parallelization. Experimental results on two practical search datasets show that HSCM not only outperforms strong baseline solutions such as HiNT, CARS, and BERTserini in document ranking, but also performs significantly better than most existing query suggestion methods. According to the results in an additional experiment, we have also found that HSCM is superior to most ranking models in click prediction.

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