The Latest Research Progress of Personalized Information Recommend Based on the Tag


Article Preview

In web2.0 era, tag marks that users have become from passive consumers into active information creators. Users can create and use any tags which represent their will freely on the Internet; they can also share all kinds of tags which other users have created. At the same time, personalized information recommendation can solve the problem of the flood of information, so how to conduct personalized information recommendation based on tags has become the focus of many scholars. This paper summarized three categories about the information recommendation model based on tags: Graph theory- based model, Tensor-based model and Topic-based model, then the author put forward the defects of the existing model and the problems that need to be solved in the future, such as how to reduce noise, how to use social network analysis method to study social tag system and so on.



Edited by:

Ming Ma




H. J. Wu et al., "The Latest Research Progress of Personalized Information Recommend Based on the Tag", Advanced Materials Research, Vol. 679, pp. 131-136, 2013

Online since:

April 2013




[1] Zhou T, Ren J, and Medo M, etal., Bipartite network projection and personal recommendation , 2012. [EB/OL] . http: /doc. rero. ch/lm. php?url =1000, 43, 2, 20071213113651 -JT/ zhang_bnp. pdf.

[2] Zi-Ke Zhang, Tao Zhou, and Yi-Cheng Zhang. Perso- nalized recommendation via integrated diffuse- ion on user-item-tag tripartite graphs[J]. PhysicaA, vol. 389(1), pp.179-186, (2010).


[3] Zi-Ke Zhang, Chuang Liu, and Yi-Cheng Zhang, etal., Solving the cold-start problem in recommender systems with social tags[J]. Informs J Comput, vol. 92(10), pp.286-295, (2010).


[4] Manuela I, Martín-Vicente, and Alberto Gil-Solla, etal., Semantic inference of user's reputation and ex- pertise to improve collaborative recommend- dations[J]. expert systems with applications , vol. 9(39), pp.8248-8258,(2012).


[5] Lopez-Nores Martin, Blanco-Fernandez Yolanda, and Pazos-Arias, etal, Automatic provision of person- alized e-commerce services in Digital TV scenarios with impermanent connectivity [J]. expert systems with applications, vol. 10 (38), pp.12691-12698, (2011).


[6] Esparza Sandra Garcia, O'Mahony Michael P., and Smyth Barry, Mining the real-time web: A novel approach to product recommendation[J]. knowledge-based systems, vol. 29 (s1), pp.3-11, (2012).


[7] Adomavicius G and Tuzhilin A, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. ieee transactions on knowledge and data eng- ineering, vol. 17(6), pp.734-749, (2005).


[8] BalabanovicM, Content-Based, Collaborative, Recommendation, 2012. [EB/OL] http: /sentra. ischool. utexas. edu/~i385q/spring2005/readings/ Balabanovic_Shoham-1997-Fab. pdf.

[9] Herlocker JL, Konstan JA, and Terveen K, etal., Evalu- ating collaborative filtering recommender syste- ms[J], ACM trasactions on information systems, vol. 22(1), pp.5-53, (2004).


[10] Upendra Shardan and Pattie Maes, Social informa- tion filtering: algorithms for automating"word of mouth", 2012. [EB/OL]http: /www. sigc- hi. org/chi95/proc eedings/papers/us_bdy. htm.

[11] Konstan J., Miller B., and Maltz, etal., GroupLens: App- lying Collaborative Filtering to Usenet News[J]. Commun ications of the ACM, vol. 40(3), pp.77-87, (1997).


[12] Lambiotte R and Ausloos M, Collaborative tag- ging as a tripartite network, 2012. [EB/OL]. ht tp: /arxiv. org/ pdf/ cs/051 2090. pdf.

[13] ZHANG Zi-ke, The structure , evolution and function of Social tag system[J], College Journal  of University of Shanghai for Science and Technology, vol. 33(5), pp.445-451, (2011).

[14] Cattuto C, Schmitz C, and Baldassarri A, etal., Network properties of folksonomies[J]. AI Communicati- ons, vol. 20(4), pp.245-262, (2007).

[15] Ghoshal G, Zlatic'V, and Caldarelli G, etal., Random hypergraphs and their applications, 2012. [EB/OL]. http: /arxiv. org /abs/0903. 0419.

[16] Zlatic'v, Ghoshal G, and Caldarelli G, Hypergraphs topological quantities for tagged social netw- orks, 2012. [EB/OL]. http: /pre. aps. org/a- bstract/PRE/v80/i3/e036118.

[17] Steffen rendle and Lars schmidit-thieme, pairwise interaction tensor factorization for personalized tag recommendation, 2012. [EB/OL]. http: /wume. cse. lehigh. edu/~ovd209/wsdm/proceedings/docs/p.81. pdf.


[18] Deerwester S, Dumais S T, and Furnas G W, etal., Indexing by latent semantic analysis, 2012. [EB/OL]. http: /www. cob. unt. edu/itds/facul ty/evangelopoulos/dsci5910/LSA_Deerwester1990. pdf.

[19] Hofmann T, Probabilistic latent semantic ind exing, 2012. [EB/OL]. http: /lvk. cs. msu. su/~bruzz/articles/feature_Selection_clustering/Hofma nn. SIGIR99. pdf.

[20] Blei D M, Ng A Y, and Jordan M I., Latent dirichlet allocation, 2012. [EB/OL]. http: /admis. fu dan. edu. cn/seminars /ppt/lecture-lda. pdf.

[21] Umbrath A S, Wetzker R, and Umbrath W, etal. , A hybrid PLSA approach for warmer cold start in folksonomy recommendation, 2012. [EB/OL]. http: /www. dai-labor. de/fileadmin/Files /Publikationen/Buchdatei/Said. pdf.

[22] CHEN Deng-ke and KONG Fan-sheng, To filter mixed Recommended Gaussian PLSA model project-based collaborative, Computer Engineering and Applications, vol. 46(23), pp.209-211, (2010).