Main Article Content


The fast increasing volume of information databases made some difficulties for a user to find the information that they need. Its important for researchers to find the best method for challenging this problem. user intention detection can be used to increase the relevancies of information delivered from the information retrieval system. This research used a systematic mapping process to identify what area, approaches, and models that mostly used to detect user intention in information retrieval in four years later. the result of this research identified that item-based approach is still the most approach researched by researchers to identify intent diversity in information retrieval. The used of item-based approach still increasing from 2015 until 2017. 34% paper used topic models in their research. It means that Topic models still the necessary models explored by the researchers in this study.

Article Details

How to Cite
Mustakim, M., Wardoyo, R., & Mustofa, K. (2018). Review of Intent Diversity in Information Retrieval : Approaches, Models and Trends. Journal of Information Technology and Computer Science, 3(2), 132–145.


  1. Y. Kang, J. Li, J. Yang, Q. Wang, and Z. Sun, “Semantic Analysis for Enhanced Medical Retrieval,†2017 IEEE Int. Conf. Syst. Man, Cybern., 2017.
  2. J. Cesar, S. Paulo, R. Bonacin, C. T. I. R. Archer, and S. Paulo, “Intention-based Information Retrieval of Electronic Health Records,†25th IEEE Int. Conf. Enabling Technol. Infrastruct. Collab. Enterp., 2016.
  3. J. Wasilewski and N. Hurley, “Intent-Aware Diversification Using a Constrained PLSA,†Proc. 10th ACM Conf. Recomm. Syst. - RecSys ’16, pp. 39–42, 2016.
  4. T. Wahyuningrum and K. Mustofa, “A Systematic Mapping Review of Software Quality Measurement: Research Trends, Model, and Method,†Int. J. Electr. Comput. Eng., vol. 7, no. 5, p. 2847, 2017.
  5. K. Petersen, R. Feldt, S. Mujtaba, and M. Mattsson, “Systematic Mapping Studies in Software Engineering,†12th Int. Conf. Eval. Assess. Softw. Eng., no. February, pp. 1–10, 2008.
  6. M. Oriol, J. Marco, and X. Franch, “Quality models for web services: A systematic mapping.,†Inf. Softw. Technol., vol. 56, no. 10, pp. 1167–1182, 2014.
  7. R. N. Patil, A. D. Kadam, S. B. Vanjale, D. Thakore, and S. Joshi, “A semi-supervised research approach to web-Image Re-ranking: Semantic image search engine,†Int. Conf. Electr. Electron. Optim. Tech. ICEEOT 2016, pp. 1883–1891, 2016.
  8. Y. Su, S. Yang, H. Sun, M. Srivatsa, S. Kase, and M. Vanni, “Exploiting Relevance Feedback in Knowledge Graph Search,†Proc. 21th ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 1135–1144, 2015.
  9. H. Lei, G. Luo, Y. Li, and S. Lin, “3D Model Retrieval Based on Hand Drawn Sketches Using LDA Model,†Proc. - 2016 Int. Conf. Digit. Home, ICDH 2016, pp. 261–266, 2017.
  10. R. Malgaonkar, A. Kadam, D. Prakash, S. Z. Gawali, and S. S. Pawar, “Image re-ranking semantic search engine : Reinforcement learning methodology,†Int. Conf. Electr. Electron. Optim. Tech. ICEEOT 2016, pp. 1829–1837, 2016.
  11. Y. Shan, M. Li, and Y. Chen, “Constructing target-aware results for keyword search on knowledge graphs,†Data Knowl. Eng., vol. 110, no. February, pp. 1–23, 2017.
  12. R. Jarrar and M. Belkhatir, “On the coupled use of signal and semantic concepts to bridge the semantic and user intention gaps for visual content retrieval,†Int. J. Multimed. Inf. Retr., vol. 5, no. 3, pp. 165–172, 2016.
  13. M. Soleymani, M. Riegler, and P. Halvorsen, “Multimodal analysis of user behavior and browsed content under different image search intents,†Int. J. Multimed. Inf. Retr., vol. 7, no. 1, pp. 29–41, 2018.
  14. Y. Zhang, P. K. Srimani, and J. Z. Wang, “Combining MeSH Thesaurus with UMLS in pseudo relevance feedback to improve biomedical information retrieval,†2016 IEEE Int. Conf. Knowl. Eng. Appl. ICKEA 2016, pp. 67–71, 2016.
  15. M. R. Alsulmi and B. A. Carterette, “Learning to Rate Clinical Concepts Using Simulated Clinician Feedback,†Proc. 22nd Int. Conf. Intell. User Interfaces - IUI ’17, pp. 505–509, 2017.
  16. C.-M. Chen, M.-F. Tsai, Y.-C. Lin, and Y.-H. Yang, “Query-based Music Recommendations via Preference Embedding,†Proc. 10th ACM Conf. Recomm. Syst. - RecSys ’16, pp. 79–82, 2016.
  17. F. Diaz and Fernando, “Spotify,†Proc. 40th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. - SIGIR ’17, pp. 1349–1349, 2017.
  18. S. Whiting, J. M. Jose, and O. Alonso, “SOGOU-2012-CRAWL: A Crawl of Search Results in the Sogou 2012 Chinese Query Log,†Proc. 39th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 709–712, 2016.
  19. L. Zhang, M. Färber, and A. Rettinger, “XKnowSearch! Exploiting knowledge bases for entity-based cross-lingual information retrieval,†Int. Conf. Inf. Knowl. Manag. Proc., vol. 24–28–Octo, pp. 2425–2428, 2016.
  20. D. Yan, L. Zhang, and X. Zhao, “Design and Implementation of a Multidimensional Data Retrieval Sorting Optimization Model,†2016 IEEE Int. Congr. Big Data (BigData Congr., pp. 244–250, 2016.
  21. E. Amer, H. M. Khalil, and T. El-shistawy, “Enhancing Semantic Arabic Information Retrieval via Arabic Wikipedia Assisted Search Expansion Layer,†vol. 639, 2018.
  22. M. Grbovic et al., “Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising,†no. January, pp. 375–384, 2016.
  23. H. Fu, “Query Reformulation Patterns of Mixed Language Queries in Different Search Intents,†Proc. 2017 Conf. Conf. Hum. Inf. Interact. Retr. - CHIIR ’17, pp. 249–252, 2017.
  24. L. Chen, J. Xu, X. Lin, C. S. Jensen, and H. Hu, “Answering why-not spatial keyword top-k queries via keyword adaption,†2016 IEEE 32nd Int. Conf. Data Eng. ICDE 2016, pp. 697–708, 2016.
  25. N. Dragovic, I. Madrazo Azpiazu, and M. S. Pera, “‘Is Sven Seven?,’†Proc. 39th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. - SIGIR ’16, no. 1, pp. 885–888, 2016.
  26. T. Konishi, T. Ohwa, S. Fujita, K. Ikeda, and K. Hayashi, “Extracting Search Query Patterns via the Pairwise Coupled Topic Model,†Proc. Ninth ACM Int. Conf. Web Search Data Min. - WSDM ’16, pp. 655–664, 2016.
  27. I. Keles, S. Saltenis, and C. S. Jensen, “Synthesis of partial rankings of points of interest using crowdsourcing,†Proc. 9th Work. Geogr. Inf. Retr. - GIR ’15, pp. 1–10, 2015.
  28. M. Hasanuzzaman, “Understanding Temporal Query Dynamics.pdf,†SIGIR’15, pp. 823–826, 2015.
  29. S. Remi and S. C. Varghese, “Domain ontology driven fuzzy semantic information retrieval,†Procedia Comput. Sci., vol. 46, no. Icict 2014, pp. 676–681, 2015.
  30. Y. Shang et al., “Scalable user intent mining using a multimodal Restricted Boltzmann Machine,†2015 Int. Conf. Comput. Netw. Commun. ICNC 2015, pp. 618–624, 2015.
  31. B. Mansouri, M. S. Zahedi, M. Rahgozar, and R. Campos, “Detecting Seasonal Queries Using Time Series and Content Features,†Proc. ACM SIGIR Int. Conf. Theory Inf. Retr. - ICTIR ’17, pp. 297–300, 2017.
  32. X. Shao, Q. Li, Y. Lin, and B. Zhou, “A meta-search group recommendation mechanism based on user intent identification,†Proc. 6th Int. Conf. Softw. Comput. Appl. - ICSCA ’17, no. 2, pp. 102–106, 2017.
  33. F. Nikolaev, A. Kotov, and N. Zhiltsov, “Parameterized Fielded Term Dependence Models for Ad-hoc Entity Retrieval from Knowledge Graph,†Proc. 39th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. - SIGIR ’16, pp. 435–444, 2016.
  34. F. Ren and H. Yu, “Role-explicit query extraction and utilization for quantifying user intents,†Inf. Sci. (Ny)., vol. 329, pp. 568–580, 2016.
  35. M. Sah and V. Wade, “Personalized concept-based search on the Linked Open Data,†J. Web Semant., vol. 36, pp. 32–57, 2016.
  36. D. Burkhardt, S. Pattan, K. Nazemi, and A. Kuijper, “Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications,†Procedia Comput. Sci., vol. 104, no. December 2016, pp. 539–547, 2016.
  37. E. Negm, S. AbdelRahman, and R. Bahgat, “PREFCA: A portal retrieval engine based on formal concept analysis,†Inf. Process. Manag., vol. 53, no. 1, pp. 203–222, 2017.
  38. P. Ren et al., “User session level diverse reranking of search results,†Neurocomputing, vol. 274, pp. 66–79, 2018.
  39. D. Zhou, W. Zhao, X. Wu, S. Lawless, and J. Liu, “An iterative method for personalized results adaptation in cross-language search,†Inf. Sci. (Ny)., vol. 430–431, pp. 200–215, 2018.
  40. S. Akuma, R. Iqbal, C. Jayne, and F. Doctor, “Comparative analysis of relevance feedback methods based on two user studies,†Comput. Human Behav., vol. 60, pp. 138–146, 2016.
  41. I. Khennak and H. Drias, “Strength Pareto fi tness assignment for pseudo-relevance feedback : application to MEDLINE,†Front. Comput. Sci, vol. 12, no. 1, pp. 163–176, 2018.
  42. A. Keikha, F. Ensan, and E. Bagheri, “Query expansion using pseudo relevance feedback on wikipedia,†J. Intell. Inf. Syst., pp. 1–24, 2017.
  43. D. Palomera and A. Figueroa, “Leveraging linguistic traits and semi-supervised learning to single out informational content across how-to community question-answering archives,†Inf. Sci. (Ny)., vol. 381, pp. 20–32, 2017.
  44. O. Banouar, “Personalized information retrieval through alignment of ontologies,†pp. 153–158, 2017.
  45. M. Mitsui, J. Liu, N. J. Belkin, and C. Shah, “Predicting Information Seeking Intentions from Search Behaviors,†Proc. 40th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. - SIGIR ’17, pp. 1121–1124, 2017.
  46. R. Pramanik, S. Pal, and M. Chakraborty, “What the user does not want?,†Proc. Second ACM IKDD Conf. Data Sci. - CoDS ’15, pp. 116–117, 2015.
  47. X. Wang, Z. Dou, T. Sakai, and J.-R. Wen, “Evaluating Search Result Diversity using Intent Hierarchies,†Proc. 39th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. - SIGIR ’16, pp. 415–424, 2016.
  48. S. Shinde, A. D. Kadam, S. Joshi, P. Salunkhe, and D. Thakore, “A decision support engine: Heuristic review analysis on information extraction system and mining comparable objects from comparable concepts (Decision support engine),†Int. Conf. Electr. Electron. Optim. Tech. ICEEOT 2016, pp. 4541–4548, 2016.
  49. J. Park and M. Y. Yi, “Graph-based retrieval model for semi-structured data,†2016 Int. Conf. Big Data Smart Comput. BigComp 2016, pp. 361–364, 2016.
  50. M. Sloan and J. Wang, “Dynamic Information Retrieval : Theoretical Framework and Application,†pp. 61–70, 2015.
  51. J. Singh, W. Nejdl, and A. Anand, “History by Diversity: Helping Historians search News Archives,†CHIIR ’16 Proc. 2016 ACM Conf. Hum. Inf. Interact. Retr., pp. 183–192, 2016.
  52. Y. Zhao and C. Hauff, “Temporal Query Intent Disambiguation using Time-Series Data,†Proc. 39th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. - SIGIR ’16, pp. 1017–1020, 2016.
  53. J.-Y. Jiang and P.-J. Cheng, “Classifying User Search Intents for Query Auto-Completion,†Proc. 2016 ACM Int. Conf. Theory Inf. Retr. - ICTIR ’16, pp. 49–58, 2016.
  54. R. Glater, R. L. T. Santos, and N. Ziviani, “Intent-Aware Semantic Query Annotation,†Proc. 40th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. - SIGIR ’17, pp. 485–494, 2017.
  55. Q. Li, J. Li, P. Zhang, and D. Song, “Modeling Multi-query Retrieval Tasks Using Density Matrix Transformation,†Proc. 38th Int. ACM SIGIR Conf. Res. Dev. Inf. Retr. - SIGIR ’15, pp. 871–874, 2015.
  56. R. L. Hobbs and W. Dron, “Using Intelligent Agents for Social Sensing Across Disadvantaged Networks,†IEEE 12th Int. Conf. Mob. Ad Hoc Sens. Syst., vol. 12th, no. Mobile Ad Hoc and Sensor Systems, pp. 633–638, 2015.
  57. G. Jacucci, “Resourceful interaction in information discovery,†2016 IEEE 32nd Int. Conf. Data Eng. Work., pp. 161–164, 2016.
  58. A. D. Kadam, S. V. Shinde, S. D. Joshi, S. P. Medhane, S. B. Nikam, and S. R. Pawar, “Hybrid intelligent trail to search engine answering machine: Squat appraisal on pedestal technology (hybrid search machine),†Int. Conf. Electr. Electron. Signals, Commun. Optim. EESCO 2015, 2015.
  59. J. C. Dos Reis, R. Bonacin, and M. C. C. Baranauskas, “Recognizing intentions in free text messages: Studies with Portuguese language,†Proc. - 2017 IEEE 26th Int. Conf. Enabling Technol. Infrastruct. Collab. Enterp. WETICE 2017, pp. 303–307, 2017.
  60. S. Malik and M. Saleem, “Interest Indicators in Structured Scientific Articles,†Procedia Comput. Sci., vol. 116, pp. 158–165, 2017.
  61. V. Singh and A. Singh, “Learn-As-You-Go: Feedback-Driven Result Ranking and Query Refinement for Interactive Data Exploration,†Procedia Comput. Sci., vol. 125, pp. 550–559, 2018.
  62. A. Malizia, K. A. Olsen, T. Turchi, and P. Crescenzi, “An ant-colony based approach for real-time implicit collaborative information seeking,†Inf. Process. Manag., vol. 53, no. 3, pp. 608–623, 2017.
  63. B. Selvaretnam and M. Belkhatir, “A linguistically driven framework for query expansion via grammatical constituent highlighting and role-based concept weighting,†Inf. Process. Manag., vol. 52, no. 2, pp. 174–192, 2016.
  64. Z. Yang, K. Gao, and J. Huang, “External Expansion Risk Management : Enhancing Microblogging Filtering Using Implicit Query,†Wirel. Pers. Commun., 2017.
  65. H. T. Yu, A. Jatowt, R. Blanco, H. Joho, and J. M. Jose, “An in-depth study on diversity evaluation: The importance of intrinsic diversity,†Inf. Process. Manag., vol. 53, no. 4, pp. 799–813, 2017.
  66. J. Peltonen, J. Strahl, and P. Floréen, “Negative Relevance Feedback for Exploratory Search with Visual Interactive Intent Modeling,†Proc. 22nd Int. Conf. Intell. User Interfaces - IUI ’17, pp. 149–159, 2017.
  67. K. Hamada, S. Nakajima, D. Kitayama, and K. Sumiya, “Experimental evaluation of method for driving route recommendation and learning drivers’ route selection preferences,†Proc. 18th Int. Conf. Inf. Integr. Web-based Appl. Serv. - iiWAS ’16, no. Figure 1, pp. 16–25, 2016.
  68. N. Ibrahim, A. H. Chaibi, and H. Ben Ghézala, “Scientometric re-ranking approach to improve search results,†Procedia Comput. Sci., vol. 112, pp. 447–456, 2017.
  69. I. Ben Sassi, S. Mellouli, and S. Ben Yahia, “Context-aware recommender systems in mobile environment: On the road of future research,†Inf. Syst., vol. 72, pp. 27–61, 2017.
  70. J. Li, A. Sun, and Z. Xing, “Learning to answer programming questions with software documentation through social context embedding,†Inf. Sci. (Ny)., vol. 448–449, pp. 36–52, 2018.