In recent years, online market places have become popular among the buyers. During this course of time, not only have they sustained the business model but also generated large amount of profit, turning it into a lucrative business model. In this paper, we take a look at a temporal dataset from one of the most successful online businesses to analyze the nature of the buying patterns of the users. Arguably, the most important purchase characteristic of such networks is follow-up purchase by a buyer, otherwise known as a co-purchase. In this paper, we also analyze the co-purchase patterns to build a knowledge-base to recommend potential co-purchase items for every item.

1. Presented by
Sohom Ghosh
3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
Sohom Ghosh, Angan Mitra, Partha Basuchowdhuri, and Sanjoy Kumar Saha
Analysis of Online Product Purchase
and Predicting Items for Co-purchase
335

2. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
Introduction
Related Works
Proposed Approach
Experimental Details
Results and Analysis
ConclusionsIntroduction
1/10
• Rapid growth of online market places
• Web based retail market is highly competitive
• Important for sellers to predict their customers’ interest and recommend
them products accordingly
• Providing discounts on items which are co-purchased frequently may provide
incentive to the buyers
• Need to comprehend the dynamic behavior of the customers to cater their needs

3. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
Introduction
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Proposed Approach
Experimental Details
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ConclusionsRelated Works
2/10
• Study of Amazon co-purchase network by Leskovec et. al. [1] revealed features
of person-to-person recommendation in viral marketing
• E-commerce demand has been explained by G. Oestreicher-Singer et al.
using Amazon co-purchase network [2]
• A community detection method was suggested by Clauset et. al CNM [4]
• Study of local communities in Amazon co-purchase network and
recommending products from it by Luo et al. [5]
• 3 and 4 node motifs had been analyzed in Amazon co purchase network
by A. Srivastava[6]
• Basuchowdhuri et al. studied the dynamics of communities in amazon
co-purchase network [13]

4. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
Introduction
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Proposed Approach
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ConclusionsProposed Approach
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• Examining Features of Co-purchase Network
– 2-Hop Degree Distributions
– Clustering Coefficient Distributions
– Triplet & Triangle Distributions
– Detection of Burst Mode
– Annual Variation in Number of Reviews
• Recommendation System
– Forming a Weighted Network
– Detecting Cliques in 5 Nearest Neighbor Network
– Collaborative Filtering
• Efficiency of Recommendation System
– Machine Learning Approach
– Precision & Recall

5. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
Introduction
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Proposed Approach
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ConclusionsExperimental Details
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• Snap shots of Amazon co-purchase networks of four time stamps where a edge
represents co-purchase between two items (treated as vertices).
• Information about Id, Title, Sales Rank, similar items, category
and reviews for every products.
• 548,552 unique products are there categorized into Books, music CDs,
DVDs and VHS video tapes mainly.
• Considering all time stamps the number of bi-directed edges are 5,853,404
while that of distinct edges are 10,847,450.
• Total number of reviews are 7,781,990.
Graph |V| |E| Month, Year
G0 262,111 1,234,877 March 02, 2003
G1 400,727 3,200,440 March 12, 2003
G2 410,263 3,356,824 May 05, 2003
G3 403,364 3,387,388 June 01, 2003

6. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
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ConclusionsResults and Analysis
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7. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
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ConclusionsResults and Analysis
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8. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
Introduction
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9. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
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• From the weighted network:-
– pairs like ‘A Christmas Carol’ and ‘Jingle All the Way’,
‘A Bend in the Road’ and ‘The Smoke Jumper’ are recommended.

10. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
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• By detecting Cliques in 5 Nearest Neighbor Network for recommendation we
observe similar products like “Ballroom Dancing”, “Much Ado About Ballroom
Dancing”, “The Complete Idiot’s Guide to Ballroom Dancing” are
recommended together. The number of 3,4,5,6 and 7 membered cliques are
79833, 32211, 7878, 872 and 4 respectively.
• Efficiency of Recommendation System is stated in the following table:-
Node Number Original Recommended Intersection Recall Precision
372787 6 5 5 0.83 1
255803 9 7 6 0.67 .86
392440 5 6 4 .80 .67

11. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
Introduction
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ConclusionsConclusions
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• Reveals how human tendencies can shape up co-purchase patterns which
bears with it temporal effects
• Recommendation systems based on nearest neighbor model
• Recommendation from graph topology based collaborative filtering
• Strategies to increase sales and maximize the profit
• Improvement of this Recommendation System using Machine Learning
techniques.

12. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
Introduction
Related Works
Proposed Approach
Experimental Details
Results and Analysis
ConclusionsReferences
• J. Leskovec, L. A. Adamic, and B. A. Huberman,
The dynamics of viral marketing, ACM Trans. Web, vol. 1, no. 1, 2007.
• G. Oestreicher-Singer and A. Sundararajan, Linking network structure to
ecommerce demand: Theory and evidence from amazon.coms copurchase
network, TPRC 2006. Available in SSRN, 2006.
• M. Newman, Modularity and community structure in networks,
Proceedings of the National Academy of Sciences, vol. 103,
no. 23, pp.85778582, 2006.
• A. Clauset, M. E. J. Newman, and C. Moore, Finding community structure in
very large networks, Physical Review E, vol. 70, p. 066111, 2004.
• F. Luo, J. Z. Wang, and E. Promislow, Exploring local community structures in
large networks, Web Intelli. and Agent Sys., vol. 6, no. 4, pp. 387400, 2008.
• A. Srivastava, Motif analysis in the amazon product co-purchasing network,
CoRR, vol. abs/1012.4050, 2010.

13. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
Introduction
Related Works
Proposed Approach
Experimental Details
Results and Analysis
ConclusionsReferences
• P. Bogdanov, M. Mongiov, and A. K. Singh, Mining heavy
subgraphs in timeevolving networks. in ICDM, D. J. Cook, J. Pei, W. W. 0010,
O. R. Zaane, and X. Wu, Eds. IEEE, 2011, pp. 8190.
• M. Lahiri and T. Y. Berger-Wolf, Structure prediction in temporal networks
using frequent subgraphs. in CIDM. IEEE, 2007, pp. 3542.
• B. Wackersreuther, P. Wackersreuther, A. Oswald, C. Bohm, and
K. M. Borgwardt, Frequent subgraph discovery in dynamic networks, in
Proceedings of the Eighth Workshop on Mining and Learning with Graphs,
ser. MLG 10. New York, NY, USA: ACM, 2010, pp. 155 162.
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networks. Knowl. Inf. Syst., vol. 24, no. 3, pp. 467497, 2010.
• J. Han, J. Pei, Y. Yin, and R. Mao, Mining frequent patterns without candidate
generation: A frequent-pattern tree approach, Data Mining and Knowledge
Discovery, vol. 8, no. 1, pp. 5387, 2004.

14. 3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)
Introduction
Related Works
Proposed Approach
Experimental Details
Results and Analysis
ConclusionsReferences
• C. Bron and J. Kerbosch, Algorithm 457: finding all cliques of an
undirected graph, Communications of the ACM, vol. 16,
Issue 9, pp. 575-577, Sept. 1973.
• P. Basuchowdhuri, M. K. Shekhawat and S. K. Saha, Analysis of
Product Purchase Patterns in a co-purchase Network, 2014

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Q and A?
3rd International Conference on Advanced Computing, Networking, and Informatics (ICACNI 2015)