Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
World Wide Web
Published in: World Wide Web 5/2019

02-08-2018

A novel temporal and topic-aware recommender model

Authors: Dandan Song, Zhifan Li, Mingming Jiang, Lifei Qin, Lejian Liao

Published in: World Wide Web | Issue 5/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Individuals’ interests and concerning topics are generally changing over time, with strong impact on their behaviors in social media. Accordingly, designing an intelligent recommender system which can adapt with the temporal characters of both factors becomes a significant research task. Namely both of temporal user interests and topics are important factors for improving the performance of recommender systems. In this paper, we suppose that users’ current interests and topics are transferred from the previous time step with a Markov property. Based on this idea, we focus on designing a novel dynamic recommender model based on collective factorization, named Temporal and Topic-Aware Recommender Model (TTARM), which can express the transition process of both user interests and relevant topics in fine granularity. It is a hybrid recommender model which joint Collaborative Filtering (CF) and Content-based recommender method, thus can produce promising recommendations about both existing and newly published items. Experimental results on two real life data sets from CiteULike and MovieLens, demonstrate the effectiveness of our proposed model.
previous article Online delivery route recommendation in spatial crowdsourcing
next article IQGA: A route selection method based on quantum genetic algorithm- toward urban traffic management under big data environment

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Footnotes
Literature
1.
Bell, R.M., Koren, Y.: Lessons from the netflix prize challenge. ACM SIGKDD Explorations Newsletter 9(2), 75–79 (2007)CrossRef
2.
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATH
3.
Chen, L., Shang, S., Zhang, Z., Cao, X., Jensen, C.S., Kalnis, P.: Location-Aware Top-K Term Publish/Subscribe. In: 2018 IEEE 34Th International conference on data engineering (ICDE), IEEE (2018)
4.
Chen, Z., Cafarella, M., Jagadish, H.: Long-tail vocabulary dictionary extraction from the Web. In: Proceedings of the Ninth ACM international conference on Web search and data mining, pp. 625–634. ACM (2016)
5.
Comon, P., Luciani, X., De Almeida, A.L.: Tensor decompositions, alternating least squares and other tales. J. Chemometr. 23(7-8), 393–405 (2009)CrossRef
6.
Ding, Y., Li, X.: Time weight collaborative filtering. In: Proceedings of the 14th ACM international conference on Information and knowledge management, pp. 485–492. ACM (2005)
7.
Ding, Y., Li, X., Orlowska, M.E.: Recency-based collaborative filtering. In: Proceedings of the 17th Australasian database conference, vol. 49, pp. 99–107. Australian Computer Society, Inc. (2006)
8.
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRef
9.
He, L., Wu, F.: A time-context-based collaborative filtering algorithm. In: IEEE International conference on granular computing, 2009, GRC’09, pp. 209–213. IEEE (2009)
10.
Hofmann, T.: Probabilistic latent semantic analysis. In: Proceedings of the Fifteenth conference on uncertainty in artificial intelligence, pp. 289–296. Morgan Kaufmann Publishers Inc. (1999)
11.
Hu, Y., Koren, Y., Volinsky, C.: Collaborative filtering for implicit feedback datasets. In: Eighth IEEE international conference on data mining, pp. 263–272. IEEE (2008)
12.
Jiang, D., Tong, Y., Song, Y. : Cross-lingual topic discovery from multilingual search engine query log. ACM Trans. Inf. Syst. (TOIS) 35(2), 9 (2016)CrossRef
13.
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 426–434. ACM (2008)
14.
Koren, Y.: Collaborative filtering with temporal dynamics. Commun. ACM 53 (4), 89–97 (2010)CrossRef
15.
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRef
16.
Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of the third ACM conference on Recommender systems, pp. 61–68. ACM (2009)
17.
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)CrossRefMATH
18.
Li, B., Zhu, X., Li, R., Zhang, C., Xue, X., Wu, X.: Cross-domain collaborative filtering over time. In: Proceedings of the Twenty-Second international joint conference on artificial intelligence-Volume Volume Three, pp. 2293–2298. AAAI Press (2011)
19.
Linden, G., Smith, B., York, J.: Amazon. com recommendations: Item-to-item collaborative. Internet Comput, IEEE 7(1), 76–80 (2003)CrossRef
20.
Liu, N.N., Zhao, M., Xiang, E., Yang, Q.: Online evolutionary collaborative filtering. In: Proceedings of the fourth ACM conference on recommender systems, pp. 95–102. ACM (2010)
21.
Lü, L., Medo, M., Yeung, C.H., Zhang, Y.-C., Zhang, Z.-K., Zhou, T.: Recommender systems. Phys. Rep. 519(1), 1–49 (2012)CrossRef
22.
Mnih, A., Salakhutdinov, R.: Probabilistic matrix factorization. In: Advances in neural information processing systems, pp. 1257–1264 (2007)
23.
Rong, X., Chen, Z., Mei, Q., Adar, E.: Egoset: Exploiting word ego-networks and user-generated ontology for multifaceted set expansion. In: Proceedings of the Ninth ACM international conference on Web search and data mining, pp. 645–654. ACM (2016)
24.
Salakhutdinov, R., Mnih, A.: Bayesian probabilistic matrix factorization using markov chain monte carlo. In: Proceedings of the 25th international conference on Machine learning, pp. 880–887. ACM (2008)
25.
Shang, S., Ding, R., Yuan, B., Xie, K., Zheng, K., Kalnis, P.: User oriented trajectory search for trip recommendation. In: Proceedings of the 15th international conference on extending database technology, pp. 156–167. ACM (2012)
26.
She, J., Tong, Y., Chen, L., Cao, C.C.: Conflict-aware event-participant arrangement and its variant for online setting. IEEE Trans. Knowl. Data Eng. 28(9), 2281–2295 (2016)CrossRef
27.
Si, X., Sun, M.: Tag-lda for scalable real-time tag recommendation. J. Comput. Inf. Syst. 6(1), 23–31 (2009)CrossRef
28.
Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 650–658. ACM (2008)
29.
Song, D., Lifei, Q., Mingming, J., Lejian, L.: A temporal and topic-aware recommender model. In: 2018 IEEE International conference on big data and smart computing (Bigcomp), IEEE (2018)
30.
Tang, T.Y., Winoto, P., Chan, K.C.: Scaling down candidate sets based on the temporal feature of items for improved hybrid recommendations. In: Intelligent techniques for Web personalization, pp. 169–186. Springer (2005)
31.
Tong, Y., Cao, C.C., Chen, L.: Tcs: efficient topic discovery over crowd-oriented service data. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 861–870. ACM (2014)
32.
Tong, Y., Chen, L., Zhou, Z., Jagadish, H.V., Shou, L., Lv, W.: Slade: a smart large-scale task decomposer in crowdsourcing. IEEE Transactions on Knowledge and Data Engineering (2018)
33.
Tong, Y., Chen, Y., Zhou, Z., Chen, L., Wang, J., Yang, Q., Ye, J., Lv, W.: The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1653–1662. ACM (2017)
34.
Tong, Y., She, J., Ding, B., Chen, L., Wo, T., Xu, K.: Online minimum matching in real-time spatial data: experiments and analysis. Proceedings of the VLDB Endowment 9(12), 1053–1064 (2016)CrossRef
35.
Tong, Y., She, J., Ding, B., Wang, L., Chen, L.: Online mobile micro-task allocation in spatial crowdsourcing. In: 2016 IEEE 32Nd international conference on data engineering (ICDE), pp. 49–60. IEEE (2016)
36.
Tong, Y., She, J., Meng, R.: Bottleneck-aware arrangement over event-based social networks: the max-min approach. World Wide Web 19(6), 1151–1177 (2016)CrossRef
37.
Tong, Y., Wang, L., Zhou, Z., Ding, B., Chen, L., Ye, J., Xu, K.: Flexible online task assignment in real-time spatial data. Proceedings of the VLDB Endowment 10(11), 1334–1345 (2017)CrossRef
38.
Tong, Y.-X., She, J., Chen, L.: Towards better understanding of app functions. J. Comput. Sci. Technol. 30(5), 1130–1140 (2015)CrossRef
39.
Tuan, C.-C., Hung, C.-F., Wu, Z.-H.: Collaborative location recommendations with dynamic time periods. Pervasive Mob. Comput. 35(Complete), 1–14 (2017)CrossRef
40.
Umbrath, A.S.R.W.W., Hennig, L.: A hybrid plsa approach for warmer cold start in folksonomy recommendation. Recommender Systems & the Social Web 10–13 (2009)
41.
Vaca, C.K., Mantrach, A., Jaimes, A., Saerens, M.: A time-based collective factorization for topic discovery and monitoring in news. In: Proceedings of the 23rd international conference on World wide Web, pp. 527–538. International World Wide Web Conferences Steering Committee (2014)
42.
Wang, C., Blei, D.M.: Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 448–456. ACM (2011)
43.
Wang, R.C., Cohen, W.W.: Language-independent set expansion of named entities using the Web. In: Seventh IEEE International conference on data mining, 2007. ICDM 2007, pp. 342–350. IEEE (2007)
44.
Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: 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. 723–732. ACM (2010)
45.
Xiaojun, L.: An improved clustering-based collaborative filtering recommendation algorithm. Clust. Comput. 20(2), 1281–1288 (2017)CrossRef
46.
Xiong, L., Chen, X., Huang, T.-K., Schneider, J.G., Carbonell, J.G.: Temporal collaborative filtering with bayesian probabilistic tensor factorization. In: Proceedings of the SIAM international conference on data mining, SDM, vol. 10, pp. 211–222. SIAM (2010)
47.
Xu, Y., Chen, L., Yao, B., Shang, S., Zhu, S., Zheng, K., Li, F.: Location-based top-K term querying over sliding window. In: International conference on Web information systems engineering, pp. 299–314. Springer (2017)
48.
Ye, M., Liu, X., Lee, W.-C.: Exploring social influence for recommendation: a generative model approach. In: Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, pp. 671–680. ACM (2012)
49.
Yin, H., Cui, B., Chen, L., Hu, Z., Huang, Z.: A temporal context-aware model for user behavior modeling in social media systems. In: Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pp. 1543–1554. ACM (2014)
50.
Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: Lcars: A location-content-aware recommender system. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 221–229. ACM (2013)
51.
Zhang, C., Wang, K., Yu, H., Sun, J., Lim, E.-P.: Latent factor transition for dynamic collaborative filtering. In: Proceedings of the SIAM international conference on data mining, SDM, vol. 14 (2014)
52.
Zhang, Y., Tu, Z., Wang, Q.: Temporec: Temporal-topic based recommender for social network services. Mobile Networks and Applications 22(6), 1182–1191 (2017)CrossRef
53.
Zheng, B., Su, H., Hua, W., Zheng, K., Zhou, X., Li, G.: Efficient clue-based route search on road networks. IEEE Trans. Knowl. Data Eng. 29 (9), 1846–1859 (2017)CrossRef
54.
Zheng, B., Yuan, N.J., Zheng, K., Xie, X., Sadiq, S., Zhou, X.: Approximate keyword search in semantic trajectory database. In: 2015 IEEE 31St international conference on data engineering (ICDE), pp. 975–986. IEEE (2015)
55.
Zheng, B., Zheng, K., Xiao, X., Su, H., Yin, H., Zhou, X., Li, G.: Keyword-aware continuous Knn query on road networks. In: 2016 IEEE 32Nd international conference on data engineering (ICDE), pp. 871–882. IEEE (2016)
56.
Zheng, K., Shang, S., Yuan, N.J., Yang, Y.: Towards efficient search for activity trajectories. In: 2013 IEEE 29Th international conference on data engineering (ICDE), pp. 230–241. IEEE (2013)
57.
Zheng, K., Su, H., Zheng, B., Shang, S., Xu, J., Liu, J., Zhou, X.: Interactive top-K spatial keyword queries. In: 2015 IEEE 31St international conference on data engineering (ICDE), pp. 423–434. IEEE (2015)
58.
Zheng, K., Su, H., Zheng, B., Shang, S., Xu, J., Liu, J., Zhou, X.: Interactive top-K spatial keyword queries. In: 2015 IEEE 31St international conference on data engineering (ICDE), pp. 423–434. IEEE (2015)
59.
Zheng, K., Zheng, B., Xu, J., Liu, G., Liu, A., Li, Z.: Popularity-aware spatial keyword search on activity trajectories. World Wide Web 20(4), 749–773 (2017)CrossRef
Metadata
Title
A novel temporal and topic-aware recommender model
Authors
Dandan Song
Zhifan Li
Mingming Jiang
Lifei Qin
Lejian Liao
Publication date
02-08-2018
Publisher
Springer US
Published in
World Wide Web / Issue 5/2019
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-018-0595-9

Other articles of this Issue 5/2019

World Wide Web 5/2019 Go to the issue

OriginalPaper

Layered convolutional dictionary learning for sparse coding itemsets

EditorialNotes

Guest editorial: special issue on Web data querying, mining, and privacy preserving

ReviewPaper

Online delivery route recommendation in spatial crowdsourcing

OriginalPaper

Adapting HTML5 Web applications to user privacy preferences

OriginalPaper

Interference identification in smart grid communications

OriginalPaper

Approximate spatio-temporal top-k publish/subscribe

Premium Partner

    Image Credits