Web Information retrieval aims to satisfy users' search needs which are highly dependent on their interests and preferences. In this context, web personalization provides an adapted information retrieval. Specific computational modeling is required to address the large amount of heterogeneous electronic documents and the lack of semantic annotations on the web which makes knowledge discovery challenging. In this paper, a novel system, based on fuzzy ontological user profile is proposed. The latter, is composed of history, positive and negative preferences. An implicit relevance judgments method is also introduced. Furthermore, the system is context-aware by integrating novel contextual similarity measures and supporting semantic fuzzification. Our proposal has been implemented and has endured a twofold evaluation. The results show that the proposed system can provide more personalized results and confirm the interest of the context-aware search.
AdomaviciusG. and TuzhilinA., Personalization technologies: A process-oriented perspective, Commun ACM48(10) (2005), 83-90.
2.
AhmadlouM. and AdeliH., Fuzzy synchronization likelihood with application to attention-deficit/hyperactivity disorder, Clinical EEG and Neuroscience42(1) (2011), 6-13.
3.
AhmadlouM., AdeliH. and AdeliA., Fuzzy synchronization likelihood-wavelet methodology for diagnosis of autism spectrum disorder, Journal of Neuroscience Methods211(2) (2012), 203-209.
4.
AnilN.K., KurianS.B., TA.A. and VargheseS.M., Multidimensional user data model for web personalization, CoRR (2013).
5.
Baazaoui-ZghalH. and GhézalaH.B., A fuzzy-ontology-driven method for a personalized query reformulation, in: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE, 2014, Beijing, China, (Jul 6-11, 2014), 1640-1647.
6.
BaiL., YanL. and MaZ.M., Querying fuzzy spatiotemporal data using xquery, Integr Comput-Aided Eng21(2) (2014), 147-162.
7.
BaoJ. and HonavarV., Divide and conquer semantic web with modular ontologies - A brief review of modular ontology language formalisms, in: Proceedings of the 1st International Workshop on Modular Ontologies, WoMO'06, Co-located with the International Semantic Web Conference, ISWC'06, USA, (2006).
8.
BennettP.N., WhiteR.W., ChuW., DumaisS.T., BaileyP., BorisyukF. and CuiX., Modeling the impact of short- and long-term behavior on search personalization, in: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, USA, (2012), 185-194.
9.
BesbesG. and Baazaoui-ZghalH., Modular ontologies and cbr-based hybrid system for web information retrieval, Multimedia Tools and Applications74(18) (2015), 8053-8077.
10.
BollegalaD., MatsuoY. and IshizukaM., Measuring semantic similarity between words using web search engines, in: Proceedings of the 16th International Conference on World Wide Web, WWW '07, New York, NY, USA, (2007), 757-766.
11.
BoscarinoC., de VriesA.P., HollinkV. and van OssenbruggenJ., Implicit relevance feedback from a multi-step search process: A use of query-logs, in: ECIR 2011 Workshop on Information Retrieval Over Query Sessions (SIR), (2011).
12.
BouzeghoubM. and KostadinovD., Personnalisation de l'information: Aperçu de l'état de l'art et définition d'un modèle flexible de profils, in: CORIA, (2005), 201-218.
13.
CaiH., ShiM., XuB. and YuM., Semantic annotation for web3d scene based on three-layer ontology, Integrated Computer-Aided Engineering22(1) (2015), 87-101.
14.
CalegariS. and CiucciD., Fuzzy ontology, fuzzy description logics and fuzzy-owl, in: WILF of Lecture Notes in Computer Science, MasulliF., MitraS. and PasiG., eds, Springer45782007, pp. 118-126.
15.
CalegariS. and PasiG., Personal ontologies: Generation of user profiles based on the YAGO ontology, Information Processing and Management49(3) (2013), 640-658.
16.
CilibrasiR.L. and VitanyiP.M.B., The google similarity distance, IEEE Trans on Knowl and Data Eng19(3) (2007), 370-383.
17.
DaoudM., TamineL. and BoughanemM., A Personalized Graph-based Document Ranking Model Using a Semantic User Profile, UMAP'10, Springer-Verlag, Berlin, Heidelberg, (2010), 171-182.
18.
d'AquinM., SchlichtA., StuckenschmidtH. and SabouM., Criteria and evaluation for ontology modularization techniques, in: Modular Ontologies, Lecture Notes in Computer Science, Springer5445 (2009), 67-89.
19.
DeyA.K. and HakkilaJ., Context-awareness and mobile devices, User Interface Design and Evaluation for Mobile Technology1 (2008), 205-217.
20.
GilR.J. and Martin-BautistaM.J., A novel integrated knowledge support system based on ontology learning: Model specification and a case study, Know-Based Syst36 (2012), 340-352.
21.
HannakA., SapiezynskiP., Molavi KakhkiA., KrishnamurthyB., LazerD., MisloveA. and WilsonC., Measuring personalization of web search, in: Proceedings of the 22Nd International Conference on World Wide Web, WWW '13, New York, NY, USA, (2013), 527-538.
22.
HoppeA., Automatic ontology-based user profile learning from heterogeneous web resources in a big data context, PVLDB6(12) (2013), 1428-1433.
23.
JärvelinK. and KekäläinenJ., Cumulated gain-based evaluation of ir techniques, ACM Trans Inf Syst20(4) (2002), 422-446.
24.
KhribiM., JemniM., NasraouiO., GrafS.and Kinshuk, Toward a fully automatic learner modeling based on web usage mining with respect to educational preferences and learning styles, in: Advanced Learning Technologies (ICALT), 2013 IEEE 13th International Conference on, (Jul 2013), 403-407.
25.
KimK.-J. and ChoS.-B., Personalized mining of web documents using link structures and fuzzy concept networks, Appl Soft Comput7(1) (2007), 398-410.
26.
KnijnenburgB.P. and KobsaA., Making decisions about privacy: Information disclosure in context-aware recommender systems, ACM Trans Interact Intell Syst3(3) (2013), 20:1-20:23.
27.
KoolenM., KazaiG., PremingerM. and DoucetA., Overview of the INEX 2013 social book search track, in: Information Access Evaluation Meets Multilinguality, Multimodality and Visualization - Fourth International Conference of the Cross-Language Evaluation Forum, CLEF 2013, Valencia, Spain, (2013).
28.
LiuF., YuC.T. and MengW., Personalized web search for improving retrieval effectiveness, IEEE Trans Knowl Data Eng16(1) (2004), 28-40.
29.
MaZ., LiuZ. and WeiZ., Formalized representation of specifications for construction cost estimation by using ontology, Comp-Aided Civil and Infrastruct Engineering31(1) (2016), 4-17.
30.
MehtaP., Adaptive personalization, International Journal of Engineering Development and Research, (2013).
31.
MendozaL.A.F., VellascoM.M.B.R. and FigueiredoK., Intelligent multiagent coordination based on reinforcement hierarchical neuro-fuzzy models, Int J Neural Syst24(8) (2014).
32.
MillerG.A. and CharlesW.G., Contextual correlates of semantic similarity, Language and Cognitive Processes6(1) (1991), 1-28.
33.
MnasserH., GargouriF. and AbedM., Performance of system based on two ontologies in order to supply personalised information, IJBPSCM6(3/4) (2014), 208-227.
34.
MylonasP., ValletD., CastellsP., FernándezM. and AvrithisY., Personalized information retrieval based on context and ontological knowledge, Knowl Eng Rev23(1) (2008), 73-100.
35.
MylonasP.,ValletD.,FernándezM.,CastellsP. andAvrithisY.,Ontology-based personalization for multimedia content,3rd European Semantic Web Conference - Semantic Web Personalization Workshop, Budva, Montenegro, (11-14 Jun 2006), 2006.
36.
PalD., MitraM. and DattaK., Improving query expansion using wordnet, JASIST65(12) (2014), 2469-2478.
37.
PasiG. and BordognaG., The role of fuzzy sets in information retrieval, On Fuzziness: A Homage to ZadehL.A., Springer Berlin Heidelberg, Berlin, Heidelberg22013, pp. 525-532.
38.
PathakJ., JohnsonT.M. and ChuteC.G., Survey of modular ontology techniques and their applications in the biomedical domain, Integr Comput-Aided Eng16(3) (2009), 225-242.
39.
PedersenT., PakhomovS.V.S., PatwardhanS. and ChuteC.G., Measures of semantic similarity and relatedness in the biomedical domain, Journal of Biomedical Informatics40(3) (2007), 288-299.
40.
PintoT., ValeZ., SousaT.M., PraçaI., SantosG. and MoraisH., Adaptive learning in agents behaviour: A framework for electricity markets simulation, Integr Comput-Aided Eng21(4) (2014), 399-415.
41.
Ponz-TiendaJ.L., PellicerE., Benlloch-MarcoJ. and Andrés-RomanoC., The fuzzy project scheduling problem with minimal generalized precedence relations, Computer-Aided Civil and Infrastructure Engineering30(11) (2015), 872-891.
42.
RattanajitbanjongN. and ManeerojS., Multi criteria pseudo rating and multidimensional user profile for movie recommender system, in: 2nd IEEE International Conference on Computer Science and Information Technology ICCSIT, (Aug 2009), 596-601.
43.
ResnikP., Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language, Journal of Artificial Intelligence Research11 (1998), 95-130.
44.
RubensteinH. and GoodenoughJ.B., Contextual correlates of synonymy, Commun ACM8(10) (1965), 627-633.
45.
SaltonG. and BuckleyC., Term-weighting approaches in automatic text retrieval, Inf Process Manage24(5) (1988), 513-523.
46.
SaltonG. and McGillM., Introduction to Modern Information Retrieval, McGraw-Hill computer science series, McGraw-Hill, (1983).
47.
ShenS., HuB., ChenW. and YangQ., Personalized click model through collaborative filtering, in: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM '12, USA, (2012), 323-332.
48.
SiddiqueN. and AdeliH., Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing, John Wiley and Sons Ltd, (2013).
49.
SiegA., MobasherB. and BurkeR.D., Learning ontology-based user profiles: A semantic approach to personalized web search, IEEE Intelligent Informatics Bulletin8(1) (2007), 7-18.
50.
SiegA., MobasherB., LytinenS. and BurkeR., Using concept hierarchies to enhance user queries in web-based information retrieval, in: Proceedings of the International Conference on Artificial Intelligence and Applications, IASTED 2004, (2004).
51.
SperettaM. and GauchS., Personalized search based on user search histories, in: The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05), (Sep 2005), 622-628.
52.
SunH., FanW., ShenW., XiaoT. and ChaiY., Ontology maintenance in high level architecture federation development and execution process, Integrated Computer-Aided Engineering20(1) (2013), 79-94.
53.
TaiebM.A.H., AouichaM.B. and HamadouA.B., Computing semantic relatedness using wikipedia features, Knowledge-Based Systems50 (2013), 260-278.
54.
TamineL., ZemirliW.N. and BahsounW., Approche statistique pour la définition du profil d'un utilisateur de système de recherche d'information, Information - Interaction - Intelligence7(1) (2007), 5-25.
55.
TaoX., LiY. and NayakR., A knowledge retrieval model using ontology mining and user profiling, Integr Comput-Aided Eng15(4) (2008), 313-329.
56.
TaoX., LiY. and ZhongN., A knowledge-based model using ontologies for personalized web information gathering, Web Intelli and Agent Sys8(3) (2010), 235-254.
57.
TeevanJ., DumaisS.T. and HorvitzE., Personalizing search via automated analysis of interests and activities, in: Proceedings of the 28th International ACM SIGIR Conference on Research and Development in Information Retrieval, USA, (2005), 449-456.
58.
TurneyP.D., Mining the web for synonyms: Pmi-ir versus lsa on toefl, in: Proceedings of the Twelfth European Conference on Machine Learning, (2001), 491-499.
59.
YanL. and MaZ.M., Conceptual design of object-oriented databases for fuzzy engineering information modeling, Integrated Computer-Aided Engineering20(2) (2013), 183-197.
60.
YanL. and MaZ.M., Extending engineering data model for web-based fuzzy information modeling, Integrated Computer-Aided Engineering20(4) (2013), 407-420.
61.
YangD. and PowersD.M.W., Measuring semantic similarity in the taxonomy of wordnet, in: Proceedings of the 28th Australasian Computer Science Conference, (2005), 315-322.
62.
ZengZ., XuJ., WuS. and ShenM., Antithetic method-based particle swarm optimization for a queuing network problem with fuzzy data in concrete transportation systems, Computer-Aided Civil and Infrastructure Engineering29(10) (2014), 771-800.
63.
ZhangF., MaZ.M. and YanL., Construction of ontologies from object-oriented database models, Integrated Computer-Aided Engineering18(4) (2011), 327-347.
64.
ZhangW., SunK., LeiC., ZhangY., LiH. and SpencerB.F., Fuzzy analytic hierarchy process synthetic evaluation models for the health monitoring of shield tunnels, Computer-Aided Civil and Infrastructure Engineering29(9) (2014), 676-688.
65.
ZhangX., LiY., ZhangS. and SchlickC.M., Modelling and simulation of the task scheduling behavior in collaborative product development process, Integr Comput-Aided Eng20(1) (2013), 31-44.
