Acquisition and Modelling of Short-Term User Behaviour on theWeb: A Survey

  • Ondrej Kassak Faculty of Informatics and Information Technologies, Slovak University of Technology, Ilkovicova 2, Bratislava, 841 04, Slovakia
  • Michal Kompan Faculty of Informatics and Information Technologies, Slovak University of Technology, Ilkovicova 2, Bratislava, 841 04, Slovakia
  • Maria Bielikova Faculty of Informatics and Information Technologies, Slovak University of Technology, Ilkovicova 2, Bratislava, 841 04, Slovakia
Keywords: User modelling, Short-term user behaviour, Session, User preference, Web-site mining, Usage data mining


User behaviour in data intensive applications such as the Web-based applications represents a complex set of actions influenced by plenty of factors. Thanks to this complexity, it is extremely hard for human to be able to understand all its aspects. Despite of this, by observing user actions from multiple views, we are able to extract and to model typical behaviour and its deviations on theWeb. The website itself, together with transaction server logs, includes information about the site structure, content and about the actual user actions (clicks) within the site. User actions logically reflect the behaviour, while other sources indicate his/her context. Combination of these data sources allows to model the typical user behaviour and his/her preferences. The longterm behaviour describes relatively stable user preferences based on extensive user history.As theWeb has become more and more dynamic, modelling user behaviour from the long-term perspective does not satisfy requirements of current Web based applications. On the other side, the short-term behaviour describes current user activity and his/her actual intent. However, this source of information is often noisy. To address these shortcomings the state-ofthe- art combines both perspectives, which allows to meaningful and timely modelling of user behaviour. In this paper,we provide a comprehensive survey of user modelling techniques. We analyse types of data sources used for the modelling and approaches for its acquisition. Additionally, we discuss approaches considering actual trends of dynamically changing websites. This trend brings new challenges, which have to be addressed in design and implementation of novelWeb applications.



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Abel, F., Herder, E., Houben, G. J., Henze, N., and Krause, D. (2013).

Cross-system user modeling and personalization on the social web.

User Modeling and User-Adapted Interaction, 23(2–3), 169–209.

Adar, E., Teevan, J., Dumais, S. T., and Elsas, J. L. (2009). The web

changes everything: understanding the dynamics of web content. In

Proceedings of the Second ACM International Conference on Web

Search and Data Mining (pp. 282–291). ACM.

Alexander, J., and Cockburn,A. (2008)An empirical characterisation of

electronic document navigation. In Proceedings of Graphics Interface

(GI ’08), Canadian Information Processing Society, Toronto,

Ontario, Canada, (pp 123–130).

AlSumait, L., Barbará, D., and Domeniconi, C. (2008). On-line lda:

Adaptive topic models for mining text streams with applications to

topic detection and tracking. In Eighth IEEE International Conference

on Data Mining. ICDM’08. (pp. 3–12). IEEE.

Armstrong, S., Church K., and Pierre I. (2014). Natural Language Processing

UsingVery Large Corpora, Springer-Verlag, Berlin, Heidelberg.

Aswani, R., Ghrera, S. P., Chandra, S., and Kar, A. K. (2017). Outlier

Detection Among Influencer Blogs Based on off-Site Web Analytics

Data. In Conference on e-Business, e-Services and e-Society (pp. 251–

. Springer, Cham.

Baeza-Yates, R., and Boldi, P. (2010). Web structure mining. In

Advanced Techniques in Web Intelligence-I (pp. 113–142). Springer,

Berlin, Heidelberg.

Baeza-Yates, R., Castillo, C., and Efthimiadis, E. (2007), Characterization

of national Web domains, ACM Transactions on Internet

Technology, 7(2), 1–32.

Baeza-Yates, R., and Poblete, B. (2006). Dynamics of the Chilean web

structure, Computer Networks: The International Journal of Computer

and Telecommunications Networking – Web dynamics, 50(10), 1464–

Barbieri, N., Manco, G., Ritacco, E., Carnuccio, M., and Bevacqua,

A. (2013). Probabilistic topic models for sequence data. Machine

Learning, 93(1), 5–29.

M. Barla (2010), Towards Social-based User Modeling and Personalization,

PhD thesis. Faculty of Informatics and Information

Technologies STU, Bratislava, Slovakia.

Barla, M. (2011). Towards social-based user modeling and personalization.

Information Sciences and Technologies Bulletin of the ACM

Slovakia, 3(1), 52–60.

Berkhin, P. (2006). A survey of clustering data mining techniques.

In Grouping Multidimensional Data (pp. 25–71). Springer, Berlin,


Billsus, D., and Pazzani, M. J. (2007). Adaptive news access. In The

Adaptive Web (pp. 550–570). Springer, Berlin, Heidelberg.

Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent dirichlet

allocation. Journal of Machine LEARNING Research, 3, 993–1022.

Blei, D. M., and Lafferty, J. D. (2006). Dynamic topic models. In

Proceedings of the 23rd International Conference on Machine Learning

(pp. 113–120). ACM.

Bondy, J. A., and Murty,U. S. R. (1976). Graph theory with applications

(Vol. 290). London: Macmillan.

Brin, S., and Page, L. (1998). The anatomy of a large-scale hypertextual

web search engine. Computer Networks and ISDN Systems, 30(1–7),


Broder, A. (2002). A taxonomy of web search. In ACM SIGIR Forum,

(2), 3–10. ACM.

Broder, A. Z. (1997). On the resemblance and containment of documents.

In Compression and Complexity of Sequences. Proceedings (pp.

–29). IEEE.

Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia.

User Modeling AND User-Adapted Interaction, 6(2–3), 87–129.

Cahn, A., Alfeld, S., Barford, P., and Muthukrishnan, S. (2016). An

empirical study of web cookies. In Proceedings of the 25th International

Conference on World Wide Web, (WWW ‘16), (pp. 891–901).

InternationalWorldWideWeb Conferences Steering Committee.

Cai, D., Yu, S., Wen, J. R., and Ma, W. Y. (2003). Vips: a vision-based

page segmentation algorithm. Technical Report MSR-TR-2003-79,

Microsoft Research.

Cantador, I., Fernández-Tobías, I., Berkovsky, S., and Cremonesi, P.

(2015). Cross-domain recommender systems. In Recommender Systems

Handbook (pp. 919–959). Springer, Boston, MA.

Carpineto, C., and Romano, G. (2012). A survey of automatic query

expansion in information retrieval. ACM Computing Surveys (CSUR),

(1), 1.

Catledge, L. D., and Pitkow, J. E. (1995). Characterizing browsing

strategies in the World-Wide Web, in Proceedings of the Third

International World-Wide Web conference on Technology, tools and

applications, 27, Elsevier, 1065–1073.

Chakrabarti, S., et al. (1999). Mining the link structure of the World

WideWeb. IEEE Computer, 32(8), 60–67.

Chang, J., Rosenn, I., Backstrom, L., and Marlow, C. (2010). ePluribus:

Ethnicity on social networks, in Fourth International AAAI Conference

on Weblogs and Social Media (ICWSM), 10, 18–25. AAAI Press.

Chovanak, T., Kassak, O., Kompan, M., and Bielikova, M. (2018). Fast

Streaming Behavioural Pattern Mining. New Generation Computing,


Cena, F., Gena, C., and Picardi, C. (2016). An Experimental Study in

Cross-Representation Mediation of User Models. In Proceedings of the

Conference on User Modeling Adaptation and Personalization,

(WWW ‘16). (pp. 289–290). ACM.

Cheng, Y., Qiu, G., Bu, J., Liu, K., Han, Y., Wang, C., and Chen, C.

(2008). Model bloggers’ interests based on forgetting mechanism. In

Proceedings of the 17th International Conference on World Wide Web

(WWW ‘08). (pp. 1129–1130). ACM.

Cheng, Z., Gao, B., and Liu, T. Y. (2010). Actively predicting diverse

search intent from user browsing behaviors. In Proceedings of the 19th

International Conference on World Wide Web (WWW ‘10). (pp. 221–

. ACM.

Chiu, B. C., and Webb, G. I. (2005), Using decision trees for agent

modeling: improving prediction performance, User Modeling and

User-Adapted Interaction, 8, Springer, 131–152.

Cho, J., and Garcia-Molina, H. (2000). The evolution of the Web

and implications for an incremental crawler. In Proceedings of the

th International Conference on Very Large Data Bases (VLDB ’00),

Morgan Kaufmann Publishers Inc., 200–209.

Chudá, D., and Krátky, P. (2014). Usage of computer mouse characteristics

for identification in web browsing. In Proceedings of the

th International Conference on Computer Systems and Technologies

(CompSysTech ’14), ACM, 218–225.

Cooley, R., Mobasher, B., and Srivastava, J. (1999). Data preparation for

mining world wide web browsing patterns. Knowledge and Information

Systems, 1(1), 5–32.

Cover, T., and Hart, P. (1967). Nearest Neighbor pattern classification,

IEEE Transactions on Information Theory, 13, 21–27.

De Bra, P., et al. (2003). AHA! The adaptive hypermedia architecture.

In Proceedings of the fourteenth ACM conference on Hypertext and

hypermedia (HYPERTEXT ’03), ACM, 81–84.

Debnath, S., Ganguly, N., and Mitra, P. (2011). Feature weighting in

content based recommendation system using social network analysis,

in Proceedings of the 17th International Conference on World Wide

Web (WWW ’08), ACM, 1041–1042.

Dell, R. F., Roman, P. E., and Velasquez, J. D. (2008).Web user session

reconstruction using integer programming. In Proceedings of the 2008

IEEE/WIC/ACM International Conference on Web Intelligence and

Intelligent Agent Technology, Vol. 01 (pp. 385–388). IEEE Computer


Desikan, P., and Srivastava, J. (2004). Mining temporally evolving

graphs. In Proceedings of the 6th WEBKDD Workshop in Conjunction

with the 10th ACM SIGKDD Conference, (WebKDD’04). (Vol. 22).

Dill, S., Kumar, R., Mccurley, K. S., Rajagopalan, S., Sivakumar D.,

and Tomkins, A. (2016). Self-similarity in the web, ACM Transactions

on Internet Technology (TOIT), 2(3), 205–223.

Doddegowda, B. J., Raju, G. T., and Manvi, S. K. S. (2016). Extraction

of behavioral patterns from pre-processed web usage data for web

personalization, in IEEE International Conference on Recent Trends in

Electronics, Information and Communication Technology (RTEICT),


Doudalis, S., Mehrotra, S., Haney, S., and Machanavajjhala, A. (2016).

Releasing True Data with Formal Privacy Guarantees. In Privacy-

Preserving IR Workshop at SIGIR..

Downey, D., Dumais, S. T., and Horvitz, E. (2007). Models of searching

and browsing: languages, studies and applications, in Proceedings

of the 20th International Joint Conference on Artifical Intelligence

(IJCAI’07), Morgan Kaufmann Publishers Inc., 2740–2747.

Duda, R., and Hart, P. (1973). Pattern Classification and SceneAnalysis,

Wiley and Sons.

Ferrara, E., De Meo, P., Fiumara, G., and Baumgartner, R. (2014).Web

data extraction, applications and techniques: A survey. Knowledgebased

Systems, 70, 301–323.

Fetterly, D., Manasse, M., Najork, M., and Wiener, J. (2003). A largescale

study of the evolution of web pages. In Proceedings of the 12th

International Conference on World Wide Web (WWW ’03), (pp. 669–

. ACM.

Gao, Q., Abel, F., Houben, G. J., and Tao, K. (2011). Interweaving trend

and user modeling for personalized news recommendation. In 2011

IEEE/WIC/ACM International Conference on Web Intelligence and

Intelligent Agent Technology (WI-IAT), (Vol. 1, pp. 100–103). IEEE.

Gambhir, M., and Gupta, V. (2017). Recent automatic text summarization

techniques: a survey. Artificial Intelligence Review, 47(1),


Gayo-Avello, D. (2009). A survey on session detection methods in

query logs and a proposal for future evaluation. Information Sciences,

(12), 1822–1843.

Goel, S., Hofman, J. M., and Sirer, M. I. (2012). Who Does What on

theWeb:ALarge-Scale Study of Browsing Behavior, in Proceedings of

the 6th International AAAI Conference on Weblogs and Social Media,

AAAI, 1–8.

Gope, J., and Jain, S. K. (2017). A survey on solving cold start

problem in recommender systems, in 2017 International Conference

on Computing, Communication and Automation (ICCCA), IEEE,


Granka, L., Feusner, M., and Lorigo, L. (2008). Eye monitoring in

online search. In Passive eye monitoring (pp. 347–372). Springer,

Berlin, Heidelberg.

Grannis, K., and Davis, E. (2009). China internet network

information center, in 14th statistical survey report

on the internet development of china 2009. According to

Graus, M. P., andWillemsen, M. C. (2015). Improving the User Experience

during Cold Start through Choice-Based Preference Elicitation,

in Proceedings of the 9th ACM Conference on Recommender Systems

– RecSys ’15, ACM, 273–276.

Gündüz, S¸ ., and Özsu, M. T. (2003). A web page prediction model

based on clickstream tree representation of user behavior, KDD ’03:

Proceedings of the 9th ACMSIGKDD International Conference on

Knowledge Discovery and Data Mining, ACM, 535–540.

Harris, Z. S. (1954). Distributional Structure,Word, 10(2/3), 146–162.

Herder, E. (2007). An Analysis of User Behavior on the Web –

Understanding theWeb and its Users VDM Verlag.

Hill, F., Cho, K., and Korhonen, A. (2016). Learning distributed

representations of sentences from unlabelled data, In Proceedings of

NAACL-HLT, 1367–1377.

Hilas, C. S., and Sahalos, J. N. (2006)., Testing the Fraud Detection

Ability of Different User Profiles by Means of FF-NN Classifiers, in

Proceedings of the 16th International Conference on Artificial Neural

Networks, Lecture Notes in Computer Science, Part II, 4132, Springer,


Hu, J., Zeng, H. J., Li, H., Niu, C., and Chen, Z. (2007, Demographic

prediction based on user’s browsing behavior, in Proceedings of the

th International Conference onWorldWideWeb (WWW),ACM, 151–

Huang, X., Yang, Y., Hu, Y., Shen, F., and Shao, J. (2016). Dynamic

User Attribute Discovery on Social Media, In Web Technologies and

Applications: 18th Asia-PacificWeb ConferenceAPWeb, Springer, 256–

Huberman, B., Pirolli, P., Pitkow, J., and Lukose R. M. (1998). Strong

regularities in world wide web surfin, 280(5360), Science, 95–97.

Huberman B. A., and Wu, F. (2007), The economics of attention:

maximizing user value in information-rich environments,ADKDD ’07:

Proceedings of the 1st International Workshop on Data Mining and

Audience Intelligence for Advertising, ACM, 16–20.

Huntington, P. N., and Jamali, H. R. (2008). Website usage metrics:Areassessment

of session data, Information Processing and Management:

an International Journal, 44(1), 358–372.

Ipeirotis, P. G., and Gravano, L. (2004). When one sample is not

enough: improving text database selection using shrinkage, SIGMOD

’04: Proceedings of the 2004 ACM SIGMOD International Conference

on Management of Data, ACM, 767–778.

Jansen, B. J., Spink, A., Blakely, C., and Koshman, S. (2007). Defining

a session on web search engines: Research articles, Journal of

the American Society for Information Science and Technology, 58,


Joachims, T., Granka, L., Pan, B., Hembrooke, H., Radlinski, F., and

Gay, G. (2007). Evaluating the accuracy of implicit feedback from

clicks and query reformulations in web search, ACM Transactions on

Information Systems (TOIS), 25(2), ACM, 7.

Sparck Jones, K. (1972). A statistical interpretation of term specificity

and its application in retrieval. Journal of Documentation, 28(1), 11–21.

Jung, J. J., and Jo, G. S. (2004). Semantic outlier analysis for sessionizing

web logs, ECML PKDD 2004 – European Conference on

Machine Learning and Principles and Practice of Knowledge Discovery

in Databases, Springer, 13–25.

Kang, J., and Lee, H. (2017). Modeling user interest in social media

using news media and wikipedia. Information Systems, 65, 52–64.

Kassak, O., Kompan, M., and Bielikova, M. (2016). Student behavior

in a web-based educational system: Exit intent prediction. Engineering

Applications of Artificial Intelligence, 51, 136–149.

Kellar, M.,Watters, C., and Shepherd, M. (2007).Afield study characterizingWeb-

based information-seeking tasks. Journal of the American

Society for Information Science and Technology, 58(7), 999–1018.

Khasawneh, N., and Chan, C. (2006). Active user-based and ontologybased

web log data preprocessing for web usage mining, IEEE / WIC /

ACM International Conference onWeb Intelligence (WI 2006), IEEE,


Kleinberg, J. M. (1998). Authoritative sources in a hyperlinked environment.

In Proceedings of the ACM-SIAM Symposium on Discrete

Algorithms, ACM, 668–677.

Kompan, M., and Bieliková, M. (2013). Context-based Satisfaction

Modelling for Personalized Recommendations, In Proceedings of the

th International Workshop on Semantic and Social Media Adaptation

and Personalization (SMAP 2013), IEEE, 33–38.

Kosala, R., and Blockeel, H. (2000). Web mining research: A survey.

ACM Sigkdd Explorations Newsletter, 2(1), 1–15.

Kramar, T., Barla, M., and Bieliková, M. (2013). Personalizing Search

Using Socially Enhanced Interest Model Built from the Stream of User’s

Activity. J. Web Eng., 12(1&2), 65–92.

Krátky, P., and Chudá, D. (2018). Recognition of web users with the

aid of biometric user model. Journal of Intelligent Information Systems,


Kumar, R., and Tomkins, A. (2010). A characterization of online

browsing behavior. In Proceedings of the 19th International Conference

on World wide web (pp. 561–570). ACM.

Labaj, M., and Bieliková, M. (2013). Tabbed browsing behavior as

a source for user modeling. In International Conference on User

Modeling, Adaptation, and Personalization (pp. 388–391). Springer,

Berlin, Heidelberg.

Lee, Y., Rajasekar, V. C. S., and Kasula, P. R. (2018). Accessibility of

Website for Visually Challenged: Combined Tree Structure and XML

Metadata, GSTF Journal on Computing (JoC), 2(1), 1–11.

Li, Y., Feng, B., and Mao, Q. (2008). Research on path completion

technique in web usage mining. In International Symposium on Computer

Science and Computational Technology, ISCSCT’08. (Vol. 1, pp.

–559). IEEE.

Li, X., Ouyang, J., Lu, Y., Zhou, X., and Tian, T. (2015). Group topic

model: organizing topics into groups. Information Retrieval Journal,

(1), 1–25.

Lika, B., Kolomvatsos, K., and Hadjiefthymiades, S. (2014). Facing

the cold start problem in recommender systems. Expert Systems with

Applications, 41(4), 2065–2073.

Liu, C., White, R. W., and Dumais, S. (2010). Understanding

web browsing behaviors through Weibull analysis of dwell

time. In Proceedings of the 33rd International ACM SIGIR Conference

on Research and development in Information Retrieval

(pp. 379–386). ACM.

Liu, K., Chen,W., Bu, J., Chen, C., and Zhang, L. (2007). User Modeling

for Recommendation in Blogspace, WI-IATW’07 Proceedings of the

IEEE/WIC/ACM International Conferences on Web Intelligence

and Intelligent Agent Technology – Workshops, IEEE, 79–82.

Loh, S., Lorenzi, F., Granada, R., Lichtnow, D., Wives, L. K., and de

Oliveira, J. P. M. (2009). Identifying Similar Users by their Scientific

Publications to Reduce Cold Start in Recommender Systems, in

Proceedings of the 5th International Conference on Web Information

Systems and Technologies – WEBIST, 593–600.

Loyola, P., Liu, C., and Hirate, Y. (2017). Modeling User Session

and Intent with an Attention-based Encoder-Decoder Architecture.

In Proceedings of the Eleventh ACM Conference on Recommender

Systems (pp. 147–151). ACM.

Lupu, R.G., and Ungureanu, F. (2013).Asurvey of eye tracking methods

and applications. Buletinul Institutului Politehnic din Iasi, Automatic

Control and Computer Science Section, 3, 72–86.

Marujo, L., Ling,W., Ribeiro, R., Gershman,A., Carbonell, J., de Matos,

D. M., and Neto, J. P. (2016). Exploring events and distributed representations

of text in multi-document summarization. Knowledge-Based

Systems, 94, 33–42.

Manzato, M. G., et al.,. (2016). Mining unstructured content for recommender

systems: an ensemble approach. Information Retrieval Journal,

(4), 378–415.

Mathew, L., Elias, A., and Ravi, C. (2016). Total privacy

preservation and search quality improvement in personalized

web search. Journal of Web Engineering, 15(5–6),


Mezghani, M., Zayani, C. A.,Amous, I., and Gargouri, F. (2012).Auser

profile modelling using social annotations: a survey. In Proceedings of

the 21st International Conference on World Wide Web (pp. 969–976).


Mihalcea, R., andTarau, P. (2004).Textrank: Bringing order into text. In

Proceedings of the 2004 Conference on Empirical Methods in Natural

Language Processing. 404–411.

Mihalkova, L., and Mooney, R. (2009). Learning to disambiguate

search queries from short sessions. In Joint European Conference

on Machine Learning and Knowledge Discovery in Databases (pp.

–127). Springer, Berlin, Heidelberg.

Mobasher, B., Dai, H., Luo, T., and Nakagawa, M. (2002). Discovery

and evaluation of aggregate usage profiles for web personalization. Data

Mining and Knowledge Discovery, 6(1), 61–82.

Mobasher, B., Dai, H., Luo, T., and Nakagawa, M. (2001, November).

Effective personalization based on association rule discovery from web

usage data. In Proceedings of the 3rd International Workshop on Web

Information and Data Management (pp. 9–15). ACM.

Mohan, K., Kurmi, J., and Kumar, S. (2017).ASurvey onWeb Structure

Mining. International Journal of Advanced Research in Computer

Science, 8(3), 227–232.

Moro, R., and Bielikova, M. (2015). Utilizing Gaze Data in Learning:

From Reading Patterns Detection to Personalization,in Proceedings of

the 23rd Conference on User Modeling, Adaptation, and Personalization

(UMAP 2015), Springer, 1–4.

Mourão, F., Rocha, L., Araújo, C., Meira Jr,W., and Konstan, J. (2017).

What surprises does your past have for you?. Information Systems, 71,


Rosenfeld, L., and Morville, P. (2002). Information architecture for the

world wide web. “O’Reilly Media, Inc.”.

Murray, G. C., Lin, J., and Chowdhury,A. (2006). Identification of user

sessions with hierarchical agglomerative clustering. In Proceedings of

the American Society for Information Science and Technology, 43(1),


Mushtaq, N., Werner, P., Tolle, K., and Zicari, R. (2004). Building

and Evaluating Non-obvious User Profiles for Visitors of Web Sites,

in Proceedings of the IEEE International Conference on E-Commerce

Technology (CEC ’04), IEEE, 9–15.

Nasraoui, O., Soliman, M., Saka, E., Badia, A., and Germain, R. (2008)

Aweb usage mining framework for mining evolving user profiles in

dynamic web sites, IEEE Trans. on Knowl. and Data Eng., 20(2), IEEE,


Ntoulas,A., Cho, J., and Olston, C. (2004). What’s new on theWeb? The

evolution of theWeb from a search engine perspective, in Proceedings

of the 13th International Conference on World Wide Web (WWW ’04),

ACM, pp. 1–12.

Obendorf, H., Weinreich, H., Herder, E., and Mayer, M. (2007). Web

page revisitation revisited: implications of a long-term click-stream

study of browser usage. In Proceedings of the SIGCHI conference on

Human factors in computing systems (pp. 597–606). ACM.

Olston, C., and Pandey, S. (2008). Recrawl scheduling based on information

longevity, in Proceeding of the 17th International Conference

on World Wide Web (WWW ’08), ACM, 437–446.

Page, L., Brin, S., Motwani, R., andWinograd,T. (1999). The PageRank

citation ranking: Bringing order to the web. Stanford InfoLab.

Patel, P., and Parmar, M. (2014). Improve heuristics for user session

identification through web server log in web usage mining. International

Journal of Computer Science and Information Technologies,

(3), 3562–3565.

Paukkeri, M. S., and Honkela, T. (2010). Likey: Unsupervised

language-independent keyphrase extraction, in Proceedings of the

th International Workshop on Semantic Evaluation, SemEval ’10,

Association for Computational Linguistics, 162–165.

Radlinski, F., and Joachims, T. (2005). Query chains: learning to rank

from implicit feedback. In Proceedings of the 11th ACM SIGKDD

International Conference on Knowledge Discovery in Data Mining (pp.

–248). ACM.

Roman, R. P. A. (2011), Web User Behavior Analysis, PhD thesis.

Universidad de Chile, Chile.

da Rosa, J. H., Barbosa, J. L., and Ribeiro, G. D. (2016). ORACON:

An adaptive model for context prediction. Expert Systems with

Applications, 45, 56–70.

Ruzza, M., Tiozzo, B., Mantovani, C., D’Este, F., and Ravarotto, L.

(2017). Designing the information architecture of a complex website:A

strategy based on news content and faceted classification. International

Journal of Information Management, 37(3), 166–176.

Sadagopan, N. and Li, J. (2008). Characterizing typical and atypical

user sessions in clickstreams. In Proceedings of the 17th International

Conference on World Wide Web (pp. 885–894), ACM.

Schneider-Mizell, C.M. and Sander, L.M. (2009). A generalized voter

model on complex networks. Technical Report Department of Physics,

University of Michigan.

Scott, S. and Matwin, S. (1999). Feature engineering for text classification.

In Proceedings of the 16th International Conference on Machine

Learning (ICML-99), Morgan Kaufmann Publishers Inc., pp. 379–388.

Senot, C., Kostadinov, D., Bouzid, M., Picault, J., Aghasaryan, A.

and Bernier, C. (2010). Analysis of strategies for building group

profiles. In International Conference on User Modeling, Adaptation,

and Personalization (pp. 40–51). Springer, Berlin, Heidelberg.

Sieg, A., Mobasher, B. and Burke, R. (2007). Ontological user profiles

for representing context in web search. In 2007 IEEE/WIC/ACM

International Conferences on Web Intelligence and Intelligent Agent

Technology Workshops, (pp. 91–94), IEEE.

Simko, J. and Vrba, J. (2018). Screen recording segmentation to scenes

for eye-tracking analysis. Multimedia Tools and Applications, 1–25.

Simko, M. and Bielikova, M. (2018). Lightweight domain modeling

for adaptive web-based educational system. Journal of Intelligent

Information Systems, 1–26.

Sukumar, P., Robert, L. andYuvaraj, S. (2016). Review on modern Data

Preprocessing techniques in Web usage mining (WUM). In International

Conference on Computation System and Information Technology

for Sustainable Solutions (CSITSS), (pp. 64–69), IEEE.

Spiliopoulou, M., Mobasher, B., Berendt, B. and Nakagawa, M. (2003).

A framework for the evaluation of session reconstruction heuristics in

web-usage analysis.INFORMSJournal on Computing, 15(2), 171–190.

Strohmaier, M., Kröll, M. and Körner, C. (2009). Intentional query suggestion:

making user goals more explicit during search. In Proceedings

of the 2009 Workshop on Web Search Click Data (pp. 68–74), ACM.

Sun, J., Faloutsos, C., Papadimitriou, S. and Yu, P.S. (2007). Graphscope:

parameter-free mining of large time-evolving graphs. In Proceedings

of the 13th ACM SIGKDD International Conference on

Knowledge Discovery and Data Mining (pp. 687–696), ACM.

Sun, M., Li, F., Lee, J., Zhou, K., Lebanon, G. and Zha, H. (2013).

Learning multiple-question decision trees for cold-start recommendation.

In Proceedings of the 6th ACM international Conference on Web

Search and Data Mining (pp. 445–454), ACM.

Takalikar, V. and Joshi, P. (2016). Inter-page access metrics for web

site structure and performance. In 2016 International Conference

on Computational Techniques in Information and Communication

Technologies (ICCTICT), (pp. 196–203), IEEE.

Tavakol, M. and Brefeld, U. (2014). Factored MDPs for detecting

topics of user sessions. In Proceedings of the 8th ACM Conference

on Recommender Systems (pp. 33–40), ACM.

Velásquez, J.D. and Palade,V. (2008). Adaptive web sites: A knowledge

extraction from web data approach (Vol. 170), Ios Press.

Wang, P., Qian, Y., Soong, F.K., He, L. and Zhao, H. (2016). Learning

distributed word representations for bidirectional lstm recurrent neural

network. In Proceedings of the 2016 Conference of the North American

Chapter of the Association for Computational Linguistics: Human

Language Technologies (pp. 527–533).

Wang,W., Zhao, D., Luo, H. andWang, X. (2013). Mining user interests

in web logs of an online news service based on memory model. In 2013

IEEE 8th International Conference on Networking, Architecture and

Storage (NAS), (pp. 151–155), IEEE.

Wartena, C. and Brussee, R. (2008). Topic detection by clustering

keywords. In 19th International Workshop on Database and Expert

Systems Application, 2008 (DEXA’08), (pp. 54–58), IEEE.

White, R.W. and Drucker, S.M. (2007). Investigating behavioral

variability in web search. In Proceedings of the 16th International

Conference on World Wide Web (pp. 21–30), ACM.

Widyantoro, D.H., Ioerger, T.R. and Yen, J. (2001). Learning user

interest dynamics with a three−descriptor representation. Journal of

the American Society for Information Science and Technology, 52(3),


Witten, I.H., Bray, Z., Mahoui, M. and Teahan, W.J. (1999). Text

mining: A new frontier for lossless compression. In Proceedings of

the Conference on Data Compression (DCC ’99), IEEE, 198–207.

Won, S.S., Jin, J. and Hong, J.I. (2009). Contextual web history: using

visual and contextual cues to improve web browser history. In Proceedings

of the SIGCHI Conference on Human Factors in Computing

Systems (pp. 1457–1466), ACM.

Xiang, L.,Yuan, Q., Zhao, S., Chen, L., Zhang, X.,Yang, Q. and Sun, J.

(2010). Temporal recommendation on graphs via long-and short-term

preference fusion. In Proceedings of the 16th ACM SIGKDD International

Conference on Knowledge Discovery and Data Mining (pp.

–732), ACM.

Xu, S., Bao, S., Fei, B., Su, Z. andYu,Y. (2008). Exploring folksonomy

for personalized search. In Proceedings of the 31st Annual International

ACM SIGIR conference on Research and Development in Information

Retrieval (pp. 155–162). ACM.

Yang, S. H., Long, B., Smola, A. J., Zha, H. and Zheng, Z. (2011). Collaborative

competitive filtering: learning recommender using context of

user choice. In Proceedings of the 34th International ACM SIGIR Conference

on Research and Development in Information Retrieval (SIGIR

’11), ACM, 295–304.

Yang, Y. C. (2010). Web user behavioral profiling for user identification.

Decision Support Systems, 49(3), 261–271.

Yu, F., Liu, Q.,Wu, S.,Wang, L. andTan,T. (2016).Adynamic recurrent

model for next basket recommendation. In Proceedings of the 39th

International ACM SIGIR Conference on Research and Development

in Information Retrieval (SIGIR ‘16), ACM, 729–732.

Zawodny, J.D. (2002). Linux apache web server administration, Sybex,

nd Edition.

Zhou, B., Zhang, B., Liu,Y. and Xing, K. (2011). User model evolution

algorithm: forgetting and reenergizing user preference. In 2011 IEEE

International Conferences on Internet of Things, and Cyber, Physical

and Social Computing (pp.444–447). IEEE.