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08.02.2018

Interactive resource recommendation algorithm based on tag information

verfasst von: Qing Xie, Feng Xiong, Tian Han, Yongjian Liu, Lin Li, Zhifeng Bao

Erschienen in: World Wide Web | Ausgabe 6/2018

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Abstract

With the popularization of social media and the exponential growth of information generated by online users, the recommender system has been popular in helping users to find the desired resources from vast amounts of data. However, the cold-start problem is one of the major challenges for personalized recommendation. In this work, we utilized the tag information associated with different resources, and proposed a tag-based interactive framework to make the resource recommendation for different users. During the interaction, the most effective tag information will be selected for users to choose, and the approach considers the users’ feedback to dynamically adjusts the recommended candidates during the recommendation process. Furthermore, to effectively explore the user preference and resource characteristics, we analyzed the tag information of different resources to represent the user and resource features, considering the users’ personal operations and time factor, based on which we can identify the similar users and resource items. Probabilistic matrix factorization is employed in our work to overcome the rating sparsity, which is enhanced by embedding the similar user and resource information. The experiments on real-world datasets demonstrate that the proposed algorithm can get more accurate predictions and higher recommendation efficiency.

Vorheriger Artikel Aggregate location recommendation in dynamic transportation networks

Nächster Artikel Self-representation and PCA embedding for unsupervised feature selection

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Some tag categories may fail to fully divide the entire resource set due to miss tagging, so we only consider those categories covering all resource items

Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17, 734–749 (2005)CrossRef

Ali, S.M., Ghani, I., Latiff, M.S.A.: Interaction-based collaborative recommendation: a personalized learning environment (ple) perspective. Trans. Internet Inf. Syst. 9, 446–465 (2015)

Barkan, O., Koenigstein, N.: Item2vec: neural item embedding for collaborative filtering. In: The 26th IEEE International Workshop on Machine Learning for Signal Processing, pp 1–6 (2016)

Bennett, J., Lanning, S., Netflix, N.: The netflix prize. In: KDD Cup and Workshop in Conjunction with KDD (2009)

Chatti, M.A., Dakova, S., Thus, H., Schroeder, U.: Tag-based collaborative filtering recommendation in personal learning environments. IEEE Trans. Learn. Technol. 6, 337–349 (2013)CrossRef

Cheng, Y., Qiu, G., Bu, J., Liu, K., Han, Y., Wang, C., Chen, C.: Model bloggers’ interests based on forgetting mechanism. In: International Conference on World Wide Web, pp 1129–1130 (2008)

De Gemmis, M., Lops, P., Semeraro, G., Basile, P.: Integrating tags in a semantic content-based recommender. In: ACM Conference on Recommender Systems (2008)

Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)CrossRef

Firan, C.S., Nejdl, W., Paiu, R.: The benefit of using tag-based profiles. In: American Web Conference, pp 32–41 (2007)

10.

Guo, L., Ma, J., Chen, Z.: A social recommendation algorithm based on the association between recommended objects. Journal of Computers 37(1), 219–228 (2014)

11.

Han, T., Liu, Y., Xie, Q.: Tagtour: a personalized tourist resource recommendation system. In: The 18th Asia-Pacific Web Conference (2016)CrossRef

12.

He, T., Chen, Z., Liu, J., Zhou, X., Du, X., Wang, W.: An empirical study on user-topic rating based collaborative filtering methods. World Wide Web: Internet and Web Information Systems 20, 815–829 (2017)CrossRef

13.

Hotho, A., Jaschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: search and ranking. In: European Semantic Web Conference, pp 411–426 (2006)

14.

Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: The 4th ACM Conference on Recommender Systems, pp 135–142 (2010)

15.

Jin, J., Chen, Q.: A trust-based top-k recommender system using social tagging network. In: International Conference on Fuzzy Systems and Knowledge Discovery, pp 1270–1274 (2012)

16.

Kuramoto, I., Yasuda, A., Minakuchi, M., Tsujino, Y.: Recommendation system based on interaction with multiple agents for users with vague intention. In: International Conference on Human Computer Interaction, pp 351–357 (2011)

17.

Li, J., Lu, K., Huang, Z., Shen, H.T.: Two birds one stone: on both cold-start and long-tail recommendation. In: The 25th ACM International Conference on Multimedia (2017)

18.

Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2010)CrossRef

19.

Liu, N., Jiang, Q., Chen, H., Wang, B.: Personalized recommendation using implicit interaction information. In: IEEE International Conference on Computer Science & Education, pp 1340–1345 (2011)

20.

Liu, S., Liu, Y., Xie, Q.: Personalized resource recommendation based on regular tag and user operation. In: The 18th Asia-Pacific Web Conference (2016)CrossRef

21.

Ma, H., King, I., Lyu, M.R.: Learning to recommend with social trust ensemble. In: International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 203–210 (2009)

22.

Mathes, A.: Folksonomies - cooperative classification and communication through shared matadata. Comput.-Mediat. Commun. 47(10), 1–13 (2004)

23.

Mishne, G.: Autotag: a collaborative approach to automated tag assignment for weblog posts. In: International Conference on World Wide Web (2006)

24.

Nepal, S., Paris, C., Freyne, P.A.P.J., Bista, S.K.: Interaction based content recommendation in online communities. In: International Conference on User Modeling, Adaptation and Personalization, pp 14–24 (2013)

25.

Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: The 21st Annual Conference on Neural Information Processing System, pp 1257–1264 (2007)

26.

Sarwar, B.M., Karypis, G., Konstan, J. A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: The 10th International Conference on World Wide Web, pp 285–295 (2001)

27.

Shan, H., Banerjee, A.: Generalized probabilistic matrix factorizations for collaborative filtering. In: The IEEE 10th International Conference on Data Mining, pp 1025–1030 (2010)

28.

Song, Y., Zhang, L., Giles, C.L.: Automatic tag recommendation algorithms for social recommender systems. ACM Trans. Web 5, 4:1–4:31 (2011)CrossRef

29.

Sun, G., Liu, G., Zhao, L., Xu, J., Liu, A., Zhou, X.: A social trust path recommendation system in contextual online social networks. In: Proceedings of 16th Asia-Pacific Web Conference, pp 652–656 (2014)

30.

Wartena, C., Brussee, R., Wibbels, M.: Using tag co-occurrence for recommendation. In: The 9th International Conference on Intelligent Systems Design and Applications (2009)

31.

Wu, Y., Yao, Y., Xu, F., Tong, H., Lu, J.: Tag2word: using tags to generate words for content based tag recommendation. In: The 25th ACM International Conference on Information and Knowledge Management, pp 2287–2292 (2016)

32.

Xiong, F., Liu, Y., Xie, Q.: Recommendations based on collaborative filtering by tag weights. In: The 13th International Conference on Semantics, Knowledge and Grids on Big Data (2017)

33.

Yang, X., Steck, H., Guo, Y., Liu, Y.: On top-k recommendation using social networks. In: The 6th ACM Conference on Recommender Systems, pp 67–74 (2012)

34.

Yang, X., Steck, H., Liu, Y.: Circle-based recommendation in online social networks. In: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1267–1275 (2012)

35.

Yin, H., Zhou, X., Cui, B., Wang, H., Zheng, K., Hung, N.Q.V.: Adapting to user interest drift for poi recommendation. IEEE Trans. Knowl. Data Eng. 28, 2566–2581 (2016)CrossRef

36.

Zhang, F., Yuan, N.J., Zheng, K., Lian, D., Xie, X., Rui, Y.: Exploiting dining preference for restaurant recommendation. In: The 25th World Wide Web Conference (2016)

37.

Zhang, J., Peng, Q., Sun, S., Liu, C.: Collaborative filtering recommendation algorithm based on user preference derived from item domain features. Physica A: Statistical Mechanics and Its Applications 296, 66–76 (2014)CrossRef

38.

Zheng, B., Su, H., Zheng, K., Zhou, X.: Landmark-based route recommendation with crowd intelligence. Data Science and Engineering 1, 86–100 (2016)CrossRef

39.

Zheng, K., Su, H., Zheng, B., Shang, S., Xu, J., Liu, J., Zhou, X.: Interactive top-k spatial keyword queries. In: The 31st IEEE International Conference on Data Engineering (2015)

Titel: Interactive resource recommendation algorithm based on tag information
verfasst von: Qing Xie
Feng Xiong
Tian Han
Yongjian Liu
Lin Li
Zhifeng Bao
Publikationsdatum: 08.02.2018
Verlag: Springer US
Erschienen in: World Wide Web / Ausgabe 6/2018
Print ISSN: 1386-145X
Elektronische ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-018-0532-y

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