Sage Journals: Discover world-class research

Abstract

Nowadays all kinds of social platforms provide various recommendation services, greatly enriching people's life. In the condition that social platforms have become indispensable tools, people have more to except that the social platforms can provide more convenient and efficient services for life and work. With three dimensions of context: time, place and activity, this paper presents one new real-time recommendation methodology based on user's schedule. It transforms the schedule-based user recommendations into text clustering using K-means algorithm. Due to the predefined limit of value K in traditional K-means algorithm, the K-means algorithm is improved and an application of improved K-means algorithm in the recommendations model of schedule-based users is introduced in this paper. The results of relevant experiments and analysis indicate that the system improved the recall and precision than traditional one and has practical application value.

Keywords

K-means text clustering schedule-based user recommendation three-dimensional context

Get full access to this article

View all access options for this article.

References

Chen

, Geyer

, Dugan

, Muller

and Guy

, Make new friends but keep the old: Recommending people on social networking sits, Proceedings of the 27th International Conference on Human Factors in Computing Systems, New York, NY, USA (2009), 201-210.

Linden

, Smith

and York

, Amazon.com recommendations: Item-to-item collaborative filtering, IEEE Internet Computing 7(1) (2003), 76-80.

Sarwar

B.M.

, Karypis

, Konstan

J.A.

and Riedl

, Analysis of recommendation algortihms for e-commerce, Proceedings of the 2nd ACM Conference on Electronic Commerce, New York, NY, USA (2000), 158-167.

Arijit

and William

, Maximum likelihood function used to calculate confidence of association rules in market baskets, Journal of Computational Methods in Sciences and Engineering 3(12) (2012), 119-127.

Bach

N.X.

, Hai

N.D.

and Phuong

T.M.

, Personalized recommendation of stories for commenting in forum-based social media, Information Sciences (352) (2016), 48-60.

Sergio

A.A.

, Carolina

, Takeshi

et al., Neural expert networks for faster combined collaborative and content-based recommendation, Journal of Computational Methods in Sciences and Engineering 4(11) (2011), 161-172.

Pazzani

M.J.

and Billsus

, Content-based recommendation systems, Brusilovsky

et al., eds, The Adaptive Web, Spinger Verlag, 2007, pp. 325-341.

Piao

and Whittle

, A feasibility study on extracting twitter users' interests using NLP tools for serendipitous connections, Proceedings of the 3rd IEEE International Conf on Social Computing, Boton, MA (2011), 910-915.

Sakaguchi

, Akaho

, Takagi

et al., Recommendations in twitter using conceptual fuzzy sets, Proceedings of the 2010 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS), Toronto, Canada, (2010), 1-6.

10.

Yuan

, Cong

, Zhao

et al., Who, where, when, and what: A nonparametric bayesian approach to context-aware recommendation and search for twitter users, ACM Transactions on Information Systems 1(33) (2015).

11.

Official Facebook Blog, http://blog.facebook.com/blog.php?post=15610312130.

12.

Hannon

, Bennett

and Smyth

, Recommending twitter users to follow using content and collaborative filtering approaches, Proceeding of the 4th ACM Conference on Recommender System Barcelona Spain (2010), 199-206.

13.

Kim

and Shim

, Twitobi: A recommendation system for twitter using probabilistic modeling, Proceedings of the 2011 IEEE 11th International Conf on Data Mining, Vancouver, Canada, (2011), 340-349.

14.

Cao

, Cao

, Jin

and Liu

, Information retrieval oriented adaptive chinese word segmentation system, Journal of Software March 17(3) (2006), 356-363.

15.

Lee

C.H.

, Soo

Y.J.

, Bok

et al., Friend recommendation using user preferences and moving patterns for mobile social networks, Journal of KIISE: Databases 1(40) (2013), 79-87.

16.

Zhibo

, Liao

, Cao

et al., Friendbook: A semantic-based friend recommendation system for social networks, IEEE Transactions on Mobile Computing 3(14) (2014), 538-551.

17.

Zhou

, Yuewen

and Wei

, Proposing a new friend recommendation method Frutai to enhance social media providers' performance, Decision Support Systems (79) (2015), 46-54.

18.

X.-H.

, Application of maximum matching method in chinese segmentation technology, Journal of Anshan Normal University 10(2) (2008), 011.

19.

Lin

, Lu

, Zhao

and Zhu

, Analysis and improvement of feature weighting method TF-IDF in text categorization, Compter Engineering and Design 29(11) (2008).

20.

Kong

, Wang

and Zheng

, Design and implementation of chinese text classification system based on VSM, Journal of Tsinghua Universiy (Science and Technology) 43(9) (2003).

21.

Wang

, Liu

G.Q.

and Chen

E.-H.

, A K-means algorithm based on optimized initial center points, Pattern Reconigtgion and Aritivical Intelligence 22(2) (2009), 299-304.

Research on schedule-based user recommendation model based on improved K-means algorithm

Abstract

Keywords

Get full access to this article

References