User Navigation Behavior Research Articles

Effective Web personalization system using Modified Fuzzy Possibilistic C Means

Due to the rapid growth and development there is an urgent need for the novel technique for the online information system. Web site personalization can be defined as the process of customizing the content and structure of a Web site to the specific and individual needs of each user taking advantage of the user's navigational behavior. A Web personalization system that dynamically suggests interesting URLs for the current user is a major research area of great interest. Clustering plays a very important role in the development of the web personalization. Fuzzy clustering techniques are found to be very efficient in the clustering accuracy. In this paper, a novel technique is proposed for the development of the web personalization system using the Modified Fuzzy Possibilistic C Means (MFPCM). The performance of the proposed Web Personalization system is evaluated based on various parameters. It is observed from the experimental result that the proposed system with MFPCM is effective in providing better and interesting URLs .

Learning by foraging: The impact of individual knowledge and social tags on web navigation processes

The World Wide Web provides a tremendously large quantity of information. When users search for information or products on the Web, they will presumably be inclined to choose their path of navigation on the basis of their prior knowledge. In those cases in which the prior knowledge of users is incorrect, however, this navigation process is assumed to lead to suboptimal search results. In an experimental study with 180 participants, we examined to what extent both the users’ prior knowledge and social tags – which capture the collective knowledge of a Web community in tag clouds – influenced the navigation of users and triggered incidental learning processes during the Web search. The results supported the assumption that the users’ prior knowledge is indeed crucial for navigation, as users followed those tags which corresponded to their internal associations. Moreover, we found that social tags also affected the navigation behavior of users, as a strong collective association of social tags led to a high selection rate for these tags. Finally, the results showed that social tags triggered incidental learning processes, as those internal associations which corresponded to tags with a strong collective association were strengthened during navigation. The implications of these findings for further research are discussed.

Towards a collaborative ranking mechanism for efficient and personalized internet search service provisioning

The aim of this paper is, in accordance with efficient web search service operation objectives, to propose enhancements to the sophistication of the functionality that can be offered by search engine services. A meta-search third-party result ranking mechanism is proposed, which is capable of adapting over the continuous changes that occur on the web, providing in parallel personalized information acquisition considering the user's navigation behaviour. Transparency is achieved for both personalization and web evolution adaptation mechanisms, requiring virtually none effort from the user's part. In essence, the proposed meta search engine rates, re-organises and combines the results acquired from search services for a specific user information resource request in accordance with a weighted combination of a performance related factor (tightly related to the ranking of the web result as given by the search engine service) and a reliability related factor (corresponding to the user satisfaction stemming from the specific web result that he/she browses), while the performance of each search engine with respect to adequately adapting to the web evolution is taken into account. For the evaluation of the web results reliability, a collaborative reputation mechanism is utilized, which helps estimating their quality and predicting their future usability, taking into account their past performance in consistently satisfying user expectations. A set of results indicative of the efficiency of our proposed scheme is provided. The Internet search services used were Google, MSN and Yahoo!

Analysis of Users’ Web Navigation Behavior using GRPA with Variable Length Markov Chains

the never-ending growth of Web services and Web-based information systems, the volumes of click stream and user data collected by Web-based organizations in their daily operations has reached enormous proportions. Analyzing such huge data can help to evaluate the effectiveness of promotional campaigns, optimize the functionality of Web-based applications, and provide more personalized content to visitors. In the previous work, we had proposed a method, Grey Relational Pattern Analysis using Markov chains, which involves to discovering the meaningful patterns and relationships from a large collection of data, often stored in Web and applications server access logs, proxy logs etc. Herein, we propose a novel approach to analyse the navigational behavior of User using GRPA with Variable-Length Markov Chains. A VLMC is a model extension that allows variable length history to be captured. GRPA with Variable- Length Markov Chains, which reflects on sequential information in Web usage data effectively and efficiently, and it can be extended to allow integration with a Web user navigation behavior prediction model for better Web Usage mining Applications.

Navigation behavior models for link structure optimization

Analysis of existing methods for automatic optimization of link structures shows that these methods rely heavily on assumptions about the preferences and navigation behavior of users. Authors often do not state these assumptions explicitly and do not evaluate whether the assumptions are consistent with the actual behavior of the users of the site. This is a serious deficiency as experiments with simulated users show that incorrect assumptions can easily lead to inefficient link structures. In this work we present a framework that gives a systematic overview of alternative assumptions. On the basis of the framework we can select a set of assumptions that best matches the navigation behavior of the users in the site’s log files. We also present a method for optimizing hierarchical navigation menus on the basis of the selected assumptions. This method can be used interactively under full control of a web master. The system proposes modifications of the structure and explains why these modifications lead to more efficient menus. Evaluation by means of a case study shows that the modifications that are proposed effectively reduce the expected navigation time while preserving the coherence of the menu structure.

Efficient Reduction of Access Latency through Object Correlations in Virtual Environments

Object correlations are common semantic patterns in virtual environments. They can be exploited to improve the effectiveness of storage caching, prefetching, data layout, and disk scheduling. However, we have little approaches for discovering object correlations in VE to improve the performance of storage systems. Being an interactive feedback-driven paradigm, it is critical that the user receives responses to his navigation requests with little or no time lag. Therefore, we propose a class of view-based projection-generation method for mining various frequent sequential traversal patterns in the virtual environments. The frequent sequential traversal patterns are used to predict the user navigation behavior and, through clustering scheme, help to reduce disk access time with proper patterns placement into disk blocks. Finally, the effectiveness of these schemes is shown through simulation to demonstrate how these proposed techniques not only significantly cut down disk access time, but also enhance the accuracy of data prefetching.

SeqPAM

With the growth in the number of Web users and necessity for making information available on the Web, the problem of Web personalization has become very critical and popular. Developers are trying to customize a Web site to the needs of specific users with the help of knowledge acquired from user navigational behavior. Since user page visits are intrinsically sequential in nature, efficient clustering algorithms for sequential data are needed. In this chapter, we introduce a similarity preserving function called sequence and set similarity measure S3M that captures both the order of occurrence of page visits as well as the content of pages. We conducted pilot experiments comparing the results of PAM, a standard clustering algorithm, with two similarity measures: Cosine and S3M. The goodness of the clusters resulting from both the measures was computed using a cluster validation technique based on average levensthein distance. Results on pilot dataset established the effectiveness of S3M for sequential data. Based on these results, we proposed a new clustering algorithm, SeqPAM for clustering sequential data. We tested the new algorithm on two datasets namely, cti and msnbc datasets. We provided recommendations for Web personalization based on the clusters obtained from SeqPAM for msnbc dataset.

VU-Flow: A Visualization Tool for Analyzing Navigation in Virtual Environments

This paper presents a tool for the visual analysis of navigation patterns of moving entities, such as users, virtual characters, or vehicles in 3D Virtual Environments (VEs). The tool, called VU-Flow, provides a set of interactive visualizations that highlight interesting navigation behaviors of single or groups of moving entities that were the VE together or separately. The visualizations help to improve the design of VEs and to study the navigation behavior of users, e.g., during controlled experiments. Besides VEs, the proposed techniques could also be applied to visualize real-world data recorded by positioning systems, allowing one to employ VU-Flow in domains such as urban planning, transportation, and emergency response.

Using prefetching to improve walkthrough latency

AbstractObject correlations are common semantic patterns in walkthrough (WT) systems. They can be exploited for improving the effectiveness of storage caching, prefecthing, data layout, and minimization of query‐response times. Previous approaches for reducing I/O access time are seldom investigated. On the other side, data mining techniques extract implicit, previously unknown and potentially useful information from the databases. However, those methods are presented for typical data mining datasets and not suitable for our WT system datasets. This paper proposes a class of novel and efficient pattern‐growth method for mining various frequent sequential traversal patterns in the WT. Our pattern‐growth method adopts a divide‐and‐conquer approach to decompose both the mining tasks and the databases. The frequent sequential traversal patterns are used to predict the user navigation behavior and help to reduce disk access time with proper placement patterns into disk blocks. We also define the terminologies such as paths, views, and objects used in the model. We have done extensive experiments to demonstrate how these proposed techniques not only significantly cut down disk access time, but also enhance the accuracy of data prefetching. Copyright © 2006 John Wiley & Sons, Ltd.

Web navigation support by means of proximity‐driven assistant agents

AbstractThe explosive growth of the Web and the consequent exigency of the Web personalization domain have gained a key position in the direction of customization of the Web information to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. This work presents an agent‐based framework designed to help a user in achieving personalized navigation, by recommending related documents according to the user's responses in similar‐pages searching mode. Our agent‐based approach is grounded in the integration of different techniques and methodologies into a unique platform featuring user profiling, fuzzy multisets, proximity‐oriented fuzzy clustering, and knowledge‐based discovery technologies. Each of these methodologies serves to solve one facet of the general problem (discovering documents relevant to the user by searching the Web) and is treated by specialized agents that ultimately achieve the final functionality through cooperation and task distribution.

A mobility prediction architecture based on contextual knowledge and spatial conceptual maps

User Mobility prediction represents a key component in assisting handoff management, resource reservation, and service preconfiguration. However, most of the existing approaches presume that the user travels in an a priori known pattern with some regularity; an assumption that may not always hold. This paper presents a novel framework for user mobility prediction that can accurately predict the traveling trajectory and destination using knowledge of user's preferences, goals, and analyzed spatial information without imposing any assumptions about the availability of users' movements history. This framework thus incorporates the notion of combining user context and spatial conceptual maps in the prediction process. The main objective of this notion is to circumvent the difficulties that arise in predicting the user's future location when adequate knowledge about the history of user's traveling patterns is not available. Using concepts of evidential reasoning of Dempster-Shafer's theory, the user's navigation behavior is captured by gathering pieces of evidence concerning different groups of candidate future locations. These groups are then refined to predict the user's future location when evidence accumulates using the Dempster rule of combination. Simulation results are presented to demonstrate the performance of the proposed framework.

Web mining for web personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user's navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented.

Automatic data-gathering agents for remote navigability testing

Predicting the user's navigational behavior in the exploration process of a hypermedia device is a quite difficult task due to the high number of cognitive and technical factors involved. To measure the quality of a navigational model, testers must expose it to real users by mean of usability testing. Although powerful, classic usability techniques result too complex and expensive for the kind of data required conducting a navigability test. Usability testing also misses the effect that the performance of the user's machine has on navigation task and is unable to record user spontaneous behavior.In order to avoid these problems, we have developed ANTS, an Automatic Navigability Testing System based on data gathering agents. These agents are sent to the user's machine, where they record the user's behavior discreetly, measuring the performance of the user's computer too. This approach not only reduces the data gathering cost but also avoids the volunteer's sensation of being observed also, since the physical presence of human testers is not required.

Recommender Systems Based on User Navigational Behavior in the Internet

Starting from data available about (anonymous) visitors of a website, possibilities of extracting information as well as enriching pure browsing patterns with the help of business measures (e-metrics) and detecting possibly negative effects of web robots are sketched as prerequisites for interpretation purposes of the navigational behavior of internet users. Problems with the reconstruction of user navigation paths are explained and different heuristics for path completion are discussed. Additionally, several kinds of features of user navigation paths (e.g., sets, sequences, path fragments) have to be mentioned to prepare for an adequate theoretical background concerning recommender systems that can be used for tasks as different as site personalization, cross-/up-selling, and navigation assistance. A vocabulary to describe different kinds of recommender systems and generic quality measures for system evaluation are formulated. Then, specific recommender systems, especially systems based on frequent path features, are defined and evaluated in a final experiment. In an outlook directions for future research on recommender systems are given.

Evaluation of navigational links between images

We describe a general framework for the evaluation of sets of links designed for user navigation between images in pictorial data banks. An example is given of construction of a connection graph for a large image bank. The method used for construction of the connection graph has been shown in previous work. We describe an experiment conducted for the observation of user navigation behaviour in this set of links. The corresponding evaluation results are discussed in the framework of our evaluation model.