Two-way Recommendation Methods for Social Networks Richi Nayak - PowerPoint PPT Presentation

Two-way Recommendation Methods for Social Networks Richi Nayak Data Science Discipline Queensland University of Technology Brisbane, Australia CRICOS No. 00213J Queensland University of Technology

Introduction • Number of users and information they provide in social networks are rapidly increasing. • Eg, more than 500 million active users in Facebook, with 50% of the active users logging on in any given day. • Beside the explicit data, social matching systems keep implicit data which is a rich data source, however it usually ignored. R real a university for the CRICOS No. 00213J world

The Overload and mismatch problem R real a university for the CRICOS No. 00213J 3 world

Too specific – No results R real a university for the CRICOS No. 00213J 4 world

Solution: Recommender Systems Traditional recommendation systems • Provide one-way recommendation • Unable to handle sparse datasets effectively • Have scalability issues R real a university for the CRICOS No. 00213J world 5

Social Recommendation – 2 way Matching R real a university for the CRICOS No. 00213J 6 world

Rich Source of Information R real a university for the CRICOS No. 00213J world 7

Underlying Social Network Data: A Sample 3 Months Data Value # of distinct active users 163,050 # of female users 82,500 # of male users 80,550 # unique kiss senders 122,396 # unique successful senders 91,487 # unique kiss recipients in the network 198,293 # unique kiss recipients who are active during the chosen 83,865 period # unique kisses 886,396 # unique successful kisses 171,158 # unique negative kisses 346,193 # unique null kisses 369,045 R real a university for the CRICOS No. 00213J world 8

Evaluation Metrics Number of unique successful kisses initiated by u = SR ( u ) Number of unique kisses initated by u ∩ Number of ( Kissed partners Re commended partners ) = Re call ( u ) Number of ( Kissed Partners ) No of users who received recommenda tions Coverage = Total no of users Number of missed links FN = Number of new links Number of false predicted links FP = Number of unconnect links R real a university for the CRICOS No. 00213J world 9

Bow Tie Structure R real a university for the CRICOS No. 00213J world 10

Indegree & Outdegree R real a university for the CRICOS No. 00213J world 11

Centrality Analysis R real a university for the CRICOS No. 00213J world

All communication in Successful SN communication in SN R real a university for the CRICOS No. 00213J world

Reachability R real a university for the CRICOS No. 00213J world

Co-clustering Methods R real a university for the CRICOS No. 00213J world 15

Experimental Results R real a university for the CRICOS No. 00213J world 16

Experimental Results R real a university for the CRICOS No. 00213J world 17

Segmentation for Recommendation Method R real a university for the CRICOS No. 00213J world 18

Recommendation Strategies for Different Segment • Active User Segment – Motivation: Users send messages frequently but with 10.7% of SR. – Task: Provide advice to user, a classification task which is cheaper to implement than recommendation – Gradient Boosting is performed for the classification • Moderate User Segment – Moderate users send a moderate number of messages, the users may like to receive several more options before starting long term relations. – Task: Interaction based Co-clustering is a quick recommendation process and can achieve high success rate. • Quiet User Segment – Motivation: TSM captures the latent relations between users and features and therefore is able to provide quality and quantity recommendation. – Apply tensor decomposition algorithm, calculate core tensor, reconstruct original tensor in order to make recommendation. R real a university for the CRICOS No. 00213J world 19

Experimental Results • Active User Segment Classification Precisio Recall Method n Naïve Bayes 0.75 0.79 Neural Network 0.77 0.80 Linear Regression 0.79 0.82 Decision Tree 0.77 0.81 Gradient Boosting 0.87 0.89 • Moderate User Segment R real a university for the CRICOS No. 00213J world 20

Experimental Results Method BSR Success Rate SRI Gradient Boosting method for active user 0.107 0.39 3.6 segment Co-clustering for moderate user segment 0.133 0.58 4.4 TSM for quiet user segment 0.169 0.72 4.3 Average SR of three methods 0.136 0.56 4.1 SocialCollab 0.156 0.35 2.2 CollabNet 0.156 0.54 3.5 Adapted SimRank (CDAS) 0.156 0.36 1.8 CF 0.156 0.168 1.1 CF+ 0.156 0.30 1.9 R real a university for the CRICOS No. 00213J world 21

Discussion • Analysing the structure and features of a social network helps to understand how the recommendations in the network should be carried out. – indegree & outdegree, – bow tie structure, – reachability, – user behaviour. R real a university for the CRICOS No. 00213J world 22

Discussion • Memory-based methods vs. Model-based methods – Memory-based methods achieve lower recall than model- based methods – Most of times model-based methods outperform memory- based methods in terms of SR • Effect of input data – Interaction only vs. combined static and interaction data, using combined data achieves higher SR. • Effect of one-way vs. two-way recommendation – Two-way recommendation methods generate high success rate or precision than one-way methods, but one- way methods outperform two-way methods in recall. R real a university for the CRICOS No. 00213J world 23

Discussion • Co-clustering methods – Co-clustering presents higher quality recommendation than traditional clustering – Inclusion of both interaction data and static data enhances the results performance – Inclusion of learning algorithm enhances performance in SR. • Segmentation methods – Segmentation method helps to save computation costs – Tensor based method achieves high SR and recall. However it is time-consuming to implement. R real a university for the CRICOS No. 00213J world 24

Potential Future Work • Cold-start problem • Methods that can achieve higher recall • Scalable methods R real a university for the CRICOS No. 00213J world 25

Publication • Kutty S, Chen L, Nayak R , (2013) A people-to-people matching system using graph mining techniques. World Wide Web Journal , pp. 1- 39, online available on http://link.springer.com/article/10.1007%2Fs11280-013-0202-z • Chen L, Nayak, R (2012) Leveraging the network information for evaluating answer quality in a collaborative question answering portal. Social Network Analysis and Mining 2 (3), pp. 197-215. • Noor I, & Nayak R , (2014) Tensor-based Item Recommendation using Probabilistic Ranking in Social Tagging Systems, Proceedings of the WWW Companion 2014 . • Chen L, Nayak R , (2013) A recommendation approach dealing with multiple market segments, Proceedings of the IEEE/WIC/AMC International Conference on Web Intelligence (WI) and Intelligent Agent Technology (IAT), pp. 89-94. • Chen L, Nayak R, (2013) A reciprocal collaborative method using relevance feedback and feature importance. Proceedings of the IEEE/WIC/AMC International Conference on Web Intelligence (WI) and Intelligent Agent Technology (IAT), pp. 133-138. • Chen L, Nayak R , Kutty S, Xu Y, (2013) Users segmentations for recommendation. Proceedings of the 28th Annual ACM Symposium on Applied Computing , ACM, pp. 279-280 • Kutty S, Chen L, Nayak R, (2012) A people-to-people recommendation system using tensor space models, Proceedings of the 27th Annual ACM Symposium on Applied Computing , pp. 187-192. • Chen L, Nayak R, Xu Y, (2012) A common neighbour based two-way collaborative recommendation method, Proceedings of the 27th Annual ACM Symposium on Applied Computing , pp. 214-215. • Alsaleh, S., Nayak, R., Xu, Y.(2012) Grouping people in social networks using a weighted multi-constraints clustering method, IEEE International Conference on Fuzzy Systems , http://dx.doi.org/10.1109/FUZZ-IEEE.2012.6250799 • Chen L, Nayak R, Xu Y, (2011) A recommendation method for online dating networks based on social relations and demographic information, Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011 , pp. 407-411. Chen, L., Nayak, R. (2011) Social network analysis of an online dating network, Proceedings of C and T 2011 - 5 th International • Conference on Communities and Technologies, pp. 41-49. • Alsaleh S, Nayak R, Xu Y, (2011) Finding and matching communities in social networks using data mining, Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2011 , pp. 389-393. R real a university for the CRICOS No. 00213J world

Acknowledgment • My post docs, Phd students, Research assistants – Dr Lin Chen, Dr Slah Alsaleh, Dr Sangeetha Kutty and many others. • Industry Partner to provide us the dataset • CRC-Smart Services to provide funding R real a university for the CRICOS No. 00213J world