Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising

Lee, Juhnyoung; Podlaseck, Mark; Schonberg, Edmond; Hoch, Robert

doi:10.1007/978-1-4615-1627-9_4

Cited by 59 publications

(53 citation statements)

References 6 publications

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“…Instead of simply assigning 1 to the purchased items, a rating function is defined that computes rating values based on the launch time and purchase time of items to reflect the user's preferences to achieve better recommendation accuracy. Some works derive user's preference to products by analysing user's navigational and behavioural data such as clickstream data [3], [8]- [11], [15]. [9] proposed a Collaborative Filtering based…”

Section: Related Workmentioning

confidence: 99%

Integrating Collaborative Filtering and Matching-based Search for Product Recommendations

Abdullah

Geva

2013

J. theor. appl. electron. commer. res.

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Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user's query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user's preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely RoundRobin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user's query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user's neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user's neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications.

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Section: Related Workmentioning

confidence: 99%

Integrating Collaborative Filtering and Matching-based Search for Product Recommendations

Abdullah

Geva

2013

J. theor. appl. electron. commer. res.

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“…Several additional examples of visualizing data and data mining models are presented in [7] and [8].…”

Section: Figure 1 -A Visualization Of Naive Bayesmentioning

confidence: 99%

Emerging trends in business analytics

Kohavi¹,

Rothleder

Simoudis³

2002

Commun. ACM

235

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IntroductionThe field of business analytics has improved significantly over the last few years, providing business users with better insights, particularly from operational data stored in transactional systems. As an illustrative example, analysis of e-commerce data has recently come to be considered a killer-app for data mining [1,2]. The data sets created by integrating clickstream records generated by web sites with demographic and other behavioral data dwarf, in size and complexity, the largest data warehouses of a few years ago [3], creating massive databases that require a mix of automated analysis techniques and human effort in order to provide business users with critical insight about the activity on the site and the characteristics of the site's visitors and customers. With many millions of clickstream records being generated on a daily basis and aggregated to records with hundreds of attributes, there is a clear need for automated techniques to find patterns in the data. In this paper we discuss the technology and enterprise-adoption trends in the area of business analytics.

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“…A session is typically the set of pages (URLS) visited by a user from the moment the user enters a web site to the moment the same user leaves it [19]. Clickstream is a generic term to describe visitors' paths through one or more web sites [13]. Analysis of clickstream data can show how visitors navigate and use the web site over time.…”

Section: Introductionmentioning

confidence: 99%

Clickstream Visualization Based on Usage Patterns

Kannappady

Mudur

Shiri

2006

Computer Vision, Graphics and Image Processing

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Abstract. Most clickstream visualization techniques display web users' clicks by highlighting paths in a graph of the underlying web site structure. These techniques do not scale to handle high volume web usage data. Further, historical usage data is not considered. The work described in this paper differs from other work in the following aspect. Fuzzy clustering is applied to historical usage data and the result imaged in the form of a point cloud. Web navigation data from active users are shown as animated paths in this point cloud. It is clear that when many paths get attracted to one of the clusters, that particular cluster is currently "hot." Further as sessions terminate, new sessions are incrementally incorporated into the point cloud. The complete process is closely coupled to the fuzzy clustering technique and makes effective use of clustering results. The method is demonstrated on a very large set of web log records consisting of over half a million page clicks.

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Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising

Cited by 59 publications

References 6 publications

Integrating Collaborative Filtering and Matching-based Search for Product Recommendations

Integrating Collaborative Filtering and Matching-based Search for Product Recommendations

Emerging trends in business analytics

Clickstream Visualization Based on Usage Patterns

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