A Visualized Product Recommendation System using Fisheye Views and Data Adjacency

Informs Hong Kong /12 IntroductionIntroduction Recommendation System Recommendation System For web stores to gain customer loyalty, sales, and advertisement profit For web stores to gain customer loyalty, sales, and advertisement profit For consumers to search products effectively For consumers to search products effectively Research Purpose Research Purpose We present a visualized product recommendation system based on data adjacency theory and fisheye views. We present a visualized product recommendation system based on data adjacency theory and fisheye views.

Informs Hong Kong /12 Literature Review Web mining Web mining Web structure mining: summary of web structure. Web structure mining: summary of web structure. Web content mining: extract information of meaning and details in the web. Web content mining: extract information of meaning and details in the web. Web usage mining: web page reconstruction, discrimination, and finding navigation patterns. Web usage mining: web page reconstruction, discrimination, and finding navigation patterns. Personalized Recommendation Systems Personalized Recommendation Systems designed for a specific consumer or group designed for a specific consumer or group gives products or related information for a customer based on demographical data, transaction data, and web log data gives products or related information for a customer based on demographical data, transaction data, and web log data contend based filtering, collaborative filtering, rule based filtering, and web usage mining contend based filtering, collaborative filtering, rule based filtering, and web usage mining

Informs Hong Kong /12 Literature Review(cont’d) Visualization Visualization A technique that represents the information in a visual form A technique that represents the information in a visual form provides users with easy understanding of the data in a short time provides users with easy understanding of the data in a short time “Fisheye Views” represents “local detail” and “global context” differently. “Fisheye Views” represents “local detail” and “global context” differently. Data Adjacency and Adjacency Matrix Data Adjacency and Adjacency Matrix It shows that whether item i and item j are co-purchased, or the purchase of item i results in that of item j. It shows that whether item i and item j are co-purchased, or the purchase of item i results in that of item j. Based on graph theory, if two points are linked by a line, the relationship is represented by 1, and 0 if they are not linked, in an adjacency matrix. Based on graph theory, if two points are linked by a line, the relationship is represented by 1, and 0 if they are not linked, in an adjacency matrix.

Informs Hong Kong /12 Literature Review(cont’d) Fisheye Views Fisheye Views DOI fisheye (x, y) = API(x) – D(x,y) DOI fisheye (x, y) = API(x) – D(x,y) Fisheye Views Calendar

Informs Hong Kong /12 Fisheye Views and Data Adjacency Data Acquisition and Transformation Data Acquisition and Transformation The data set is collected from an internet shopping site which sells computers and computer-related items. The data set is collected from an internet shopping site which sells computers and computer-related items. As web log data has lot of information including date, IP address, server name, and time, it is important to refine the dataset on proper purposes. As web log data has lot of information including date, IP address, server name, and time, it is important to refine the dataset on proper purposes. All products in the company are assigned new serial numbers All products in the company are assigned new serial numbers ex. P1, P2, …, Pn ex. P1, P2, …, Pn

Informs Hong Kong /12 Fisheye Views and Data Adjacency(cont’d) A graph consists of two components, vertex and arc. A graph consists of two components, vertex and arc. An adjacency matrix An adjacency matrixABCDEA01111 B10110 C11001 D11001 E10110

Informs Hong Kong /12 Fisheye Views and Data Adjacency(Cont’d) CFM(connection frequency matrix) of 7 products CFM(connection frequency matrix) of 7 products This shows that customers moved from P1 to P2 45 times. However, it also shows that customers moved from P2 to P1 only three times This shows that customers moved from P1 to P2 45 times. However, it also shows that customers moved from P2 to P1 only three times P1P1 P2P2 P3P3 P4P4 P5P5 P6P6 P7P7 Sum P1P P2P P3P P4P P5P P6P P7P Sum

Informs Hong Kong /12 Fisheye Views and Data Adjacency(Cont’d) A View on Treemap A View on Treemap P1P1 P2P2 P3P3 P4P4 P5P5 P6P6 P7P7 Sum P1P P2P P3P P4P P5P P6P P7P Sum

Informs Hong Kong /12 Fisheye Views and Data Adjacency(Cont’d) For example, when a user is viewing P3, the weight on P4 can be calculated as For example, when a user is viewing P3, the weight on P4 can be calculated as W(P34) = {38 / ( )} = 38/222 = 0.17 W(P34) = {38 / ( )} = 38/222 = 0.17

Informs Hong Kong /12 Fisheye Views and Data Adjacency(Cont’d) Share change by the product In a web page

Informs Hong Kong /12 TestTest Comparison with the method by association rules Comparison with the method by association rules Association rules Association rules Rule No. Support (%) Confidence (%) LiftRule and its content P479  P477 (Samsung Laptop  LG-IBM Laptop) P479  P439 (Sony Laptop  Dell Desktop) P479  P446 (USB HDD  Samsung Laptop) P479  P102 (MP3 Player  Digital Camera) P479  P465 (Digital Camera  Cell Phone) ……………

Informs Hong Kong /12 Test(cont’d)Test(cont’d) Group A: Web Page based on Association Rules Group A: Web Page based on Association Rules

Informs Hong Kong /12 Test(cont’d)Test(cont’d) Group B: Web Page based on CFM Group B: Web Page based on CFM

Informs Hong Kong /12 Test(cont’d)Test(cont’d) Results Results Measure Subjects Groups No. of Sample Mean Std. Dev. ANOVA (F-value) Sig. Loyalty User Satisfaction A *.000 B Total Intention to Re-visit A *.000 B Total Intention to Purchase A *.000 B Total * Significant at α = 0.05, all constructs are five-point scales 1=Very Disagree, 3=Neutral, 5=Very Agree

Informs Hong Kong /12 Test(cont’d)Test(cont’d) Results Results Measure Subjects Groups No. of Sample Mean Std. Dev. ANOVA (F-value) Sig. Web Usability System Quality A *.000 B Total Information Quality A *.000 B Total Service Quality A *.004 B Total * Significant at α = 0.05, all constructs are five-point scales 1=Very Disagree, 3=Neutral, 5=Very Agree

Informs Hong Kong /12 ConclusionsConclusions Based on data adjacency and fisheye views, it is compared with a recommendation system based on association rules. Based on data adjacency and fisheye views, it is compared with a recommendation system based on association rules. The suggested method has high performance. The suggested method has high performance. Analysis from the tests confirms that it has greater loyalty and web usability compared to the other system. Analysis from the tests confirms that it has greater loyalty and web usability compared to the other system. Limitations Limitations Our method should be compared with more diverse methods in the literature of product recommendation systems. Our method should be compared with more diverse methods in the literature of product recommendation systems.