Acquisition method of users’ browsing behavior preference based on the fusion of social network link and theme model

Abstract

For the acquisition of user behavior preference in social network, usually a data mining will be conducted on the nearest neighborhood users or latestprojects based on the user’s historical behavior data, so as to find similar behaviorrelationship for quantitative analysis; it can also focus on the awareness on the user-related context information based on cognitive psychology, so as to find its internal links forthe potential mining. However, these methods ignore the intrinsic link between the browsing behavior and the preferred topics in the user link connection, resulting in the limited precision and accuracy of the preference acquisition. Inspired by the theory of complex network link prediction and the topic model, anacquisition method for users’ browsing behavior preference was proposed in this paper. In the multi-dimensional network link environment, by measuring the importance of the node via network centrality and search the social network link via setting the similarity threshold, the real-time multi-link information and the big data about users’ browsing under each link were acquired, then the data were filteredand cleaned by using adjustable parameters. On this basis, according to the least squares criterion the data underwent a fusion and were used to construct a data node distribution model for user browsing behavior, then the frequent feature items of user browsing behavior preference were extracted. Based on the extracted feature terms, the variational Bayes approximation reasoning method was used to construct the preference topic model. Finally, the hierarchical VSM model representation method was used to establish the preference acquisition model of user browsing behavior, and the model was updated in real time by user feedback processing mechanism. The experimental results on the real data set showed that the link search method and the preference topic model provided by this paper are accurate and efficient. Compared with the classical cooperative filtering method and the context-awareness method, the precision, accuracy and effectiveness of the preference acquisition model provided this paper are significantly improved, and its adaptability has been significantly strengthened.

Keywords

Social network link topic model user browsing behavior preference acquisition

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References

Maier ,

Laumer ,

Eckhardt and

Weitzel , Giving too much social support: Social overload on social networking sites, European Journal of Information Systems24(5) (2015), 447–464.

Misra and

Stokols , Psychological and health outcomes of perceived information overload, Environment & Behavior44(6) (2012), 737–759.

Branke ,

Corrente ,

Greco ,

Słowiński and

Zielniewicz , Using choquet integral as preference model in interactive evolutionary multiobjective optimization, European Journal of Operational Research250(3) (2016), 884–901.

Polo ,

Nguez ,

Berrueta ,

Ruiz and

Mez , Manuel, User preferences in the web of data, Semantic Web5(1) (2014), 67–75.

Yang ,

Zhang ,

V.W.

Zheng and

Yu , Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns, IEEE Transactions on Systems Man & Cybernetics Systems45(1) (2014), 129–142.

Guo ,

Zhang and

Yorke-Smith , TrustSVD: Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings, Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI Press, 2015, pp. 123–129.

Goferman ,

Zelnikmanor and

Tal , Context-aware saliency detection, IEEE Transactions on Pattern Analysis & Machine Intelligence34(10) (2012), 1915.

L.O.

Colombo-Mendoza ,

Valencia-García ,

Rodríguez-González ,

Alor-Hernández and

J.J.

Samper-Zapater , Recommetz: A context-aware knowledge-based mobile recommender system for movie showtimes, Expert Systems with Applications42(3) (2015), 1202–1222.

Panniello ,

Tuzhilin and

Gorgoglione , Comparing context-aware recommender systems in terms of accuracy and diversity, User Modeling & User-Adapted Interaction24(1-2) (2014), 35–65.

10.

T.D.

Pessemier ,

Courtois ,

Vanhecke ,

K.V.

Damme ,

Martens and

L.D.

Marez , A user-centric evaluation of context-aware recommendations for a mobile news service, Multimedia Tools & Applications75(6) (2016), 3323–3351.

11.

Biancalana ,

Gasparetti ,

Micarelli and

Sansonetti , An approach to social recommendation for context-aware mobile services, Acm Transactions on Intelligent Systems & Technology4(1) (2013), 1–31.

12.

Z.D.

Champiri ,

S.R.

Shahamiri and

S.S.B.

Salim , A systematic review of scholar context-aware recommender systems, Expert Systems with Applications42(3) (2015), 1743–1758.

13.

Sattar ,

Karray ,

Kamel ,

Nassar and

Golestan , Recent advances on context-awareness and data/information fusion in its, International Journal of Intelligent Transportation Systems Research14(1) (2016), 1–19.

14.

Nasir ,

Y.H.

Yoo ,

D.H.

Kim and

J.H.

Kim , User preference-based dual-memory neural model with memory consolidation approach, IEEE Transactions on Neural Networks & Learning SystemsPP(99) (2017), 1–15.

15.

Zschache , Producing public goods in networks: Some effects of social comparison and endogenous network change, Social Networks34(4) (2012), 539–548.

16.

Lancichinetti ,

M.I.

Sirer ,

J.X.

Wang ,

Acuna ,

Krding and

L.A.N.

Amaral , A high-reproducibility and high-accuracy method for automated topic classification, Physical Review X5(11007) (2016).

17.

Kou ,

Du ,

He and

Ye , Social network search based on semantic analysis and learning, Caai Transactions on Intelligence Technology1(4) (2016), 293–302.

18.

Kearns ,

Roth ,

Z.S.

Wu and

Yaroslavtsev , Private algorithms for the protected in social network search, Proceedings of the National Academy of Sciences of the United States of America113(4) (2016), 913.

19.

Kimura ,

Saito ,

Ohara and

Motoda , Speeding-up node influence computation for huge social networks, International Journal of Data Science & Analytics1(1) (2016), 3–16.

20.

Zhao ,

Niu ,

Chen ,

Shi ,

Niu and

Liu , A hybrid approach of topic model and matrix factorization based on two-step recommendation framework, Journal of Intelligent Information Systems44(3) (2015), 335–353.

21.

Hasegawa and

Itoh , Optimal Online Algorithms for the Multi-objective Time Series Search Problem, International Workshop on Algorithms and Computation, Springer International Publishing, 2016, pp. 301–312.

22.

L.I.

Yong-Li ,

Peng , and

Shu-Rui . Link prediction in social networks based on decision analysis. Journal of Management Sciences in China20(1) (2017), 64–74.

23.

Kalaï ,

C.A.

Zayani ,

Amous ,

Abdelghani , and

Sédes . Social collaborative service recommendation approach based on user's trust and domain-specific expertise. Future Generation Computer Systems, 80 (2018), 355–367.

24.

Xie and

Wang . Web page recommendation via twofold clustering: considering user behavior and topic relation. Neural Computing and Applications, 29(1) (2018), 235–243.

25.

Bai ,

H.D.

Qi and

Xiu , Constrained best euclidean distance embedding on a sphere: A matrix optimization approach, Siam Journal on Optimization25(1) (2015), 439–467.

26.

K.W.

Jorgensen and

L.K.

Hansen , Model selection for gaussian kernel pca denoising, IEEE Transactions on Neural Networks & Learning Systems23(1) (2012), 163.

27.

Liang ,

Liao and

Liu , A social ties-based approach for group decision-making problems with incomplete additive preference relations, Knowledge-Based Systems119 (2016).

28.

Ma and

Ding , Recursive computational formulas of the least squares criterion functions for scalar system identification, Applied Mathematical Modelling38(1) (2014), 1–11.

29.

Feddaoui ,

Felhi and

Akaichi , EXTRACT: New extraction algorithm of association rules from frequent itemsets, Ieee/acm International Conference on Advances in Social Networks Analysis and Mining IEEE, 2016, pp. 752–756.

30.

S.T.

Timmer ,

J.J.C.

Meyer ,

Prakken ,

Renooij and

Verheij . A two-phase method for extracting explanatory arguments from Bayesian networks. International Journal of Approximate Reasoning, 80 (2017), 475–494.

31.

Xie ,

Li ,

Xiao and

Peng , Novel classification method for remote sensing images based on information entropy discretization algorithm and vector space model, Computers & Geosciences89(C) (2016), 252–259.

32.

Yu ,

Umashankar and

V.R.

Rao , Choosing the right target: Relative preferences for resource similarity and complementarity in acquisition choice, Strategic Management Journal37(8) (2016), 1808–1825.

33.

Liu ,

Li ,

Liu ,

Yu and

Cheng , Design of high-performance water-in-glass evacuated tube solar water heaters by a high-throughput screening based on machine learning: A combined modeling and experimental study, Solar Energy142 (2017), 61–67.

34.

Harman ,

Brown ,

Johnson ,

Rinderle-Ma and

Kannengiesser , Augmenting process elicitation with visual priming: An empirical exploration of user behaviour and modelling outcomes, Information Systems62(C) (2016), 242–255.

35.

Liu ,

Cheng ,

Li ,

Cao ,

Wu and

Shi , Exploring the potential relationship between indoor air quality and the concentration of airborne culturable fungi: A combined experimental and neural network modeling study, Environmental Science and Pollution Research25(4) (2018), 3510–3517.