Vertical versus dynamic menus on the world wide web: Eye tracking study measuring the influence of menu design and task complexity on user performance and subjective preference

Web sites need fast and effective navigation systems. An eye tracking laboratory study with n = 120 participants was conducted to compare the influence of different navigation designs (vertical versus dynamic menus) and task complexity (simple versus complex navigation tasks) on user performance, navigation strategy, and subjective preference. With vertical menus, users needed less eye fixations, were faster and more successful. We conclude that, firstly, vertical menus fit better to perception and cognition than dynamic menus, where the navigation items are hidden and must be accessed by an additional mouse click. Secondly, navigation systems should be extended with different kinds of navigation items adapted to the complexity of the users’ navigation tasks, because users tend to switch their navigation strategy when confronted with complex tasks.     

Incremental and interactive mining of web traversal patterns

Web mining involves the application of data mining techniques to large amounts of web-related data in order to improve web services. Web traversal pattern mining involves discovering users’ access patterns from web server access logs. This information can provide navigation suggestions for web users indicating appropriate actions that can be taken. However, web logs keep growing continuously, and some web logs may become out of date over time. The users’ behaviors may change as web logs are updated, or when the web site structure is changed. Additionally, it can be difficult to determine a perfect minimum support threshold during the data mining process to find interesting rules. Accordingly, we must constantly adjust the minimum support threshold until satisfactory data mining results can be found.The essence of incremental data mining and interactive data mining is the ability to use previous mining results in order to reduce unnecessary processes when web logs or web site structures are updated, or when the minimum support is changed. In this paper, we propose efficient incremental and interactive data mining algorithms to discover web traversal patterns that match users’ requirements. The experimental results show that our algorithms are more efficient than other comparable approaches.     

Self-monitoring,honesty, and cue use on Facebook: The relationship with user extraversion and conscientiousness

The present study explored the relationship between Facebook (FB) users’ self-monitoring and self-reported FB honesty and the content of users’ profiles. It was anticipated that high self-monitors would construct a more extraverted profile and honest FB users would construct a more conscientious profile. A content analysis of 53 cues on participants’ FB profiles (N = 100) was conducted. Supporting evidence was shown in a lens model analysis of FB user profiles and strangers’ (N = 35) impressions of users’ personality. User self-monitoring was uniquely associated with three FB cues: posting a profile picture at a younger age, posting more frequently, and using more shorthand in status updates. These three cues informed strangers’ estimations of user extraversion, but were unrelated to estimations of users’ conscientiousness. Honesty on FB was uniquely associated with three cues that informed strangers’ estimations of user conscientiousness: expressing positive affect and talking more about family in status updates, and having FB friends who expressed support in response to status updates. This study demonstrated that FB use and profile page construction were associated with FB users’ personality, and the construction of profiles affected strangers’ perceptions of FB users.     

AdROSA—Adaptive personalization of web advertising

One of the greatest and most recent challenges for online advertising is the use of adaptive personalization at the same time that the Internet continues to grow as a global market. Most existing solutions to online advertising placement are based on demographic targeting or on information gained directly from the user. The AdROSA system for automatic web banner personalization, which integrates web usage and content mining techniques to reduce user input and to respect users’ privacy, is presented in the paper. Furthermore, certain advertising policies, important factors for both publishers and advertisers, are taken into consideration. The integration of all the relevant information is accomplished in one vector space to enable online and fully personalized advertising.     

Strategic self-presentation online: A cross-cultural study

Contemporary social networking sites (SNSs) make idealized self-presentation and image maintenance difficult because users’ audiences are becoming more diverse and individual users must negotiate often unanticipated other-provided information in the form of text posts and digital images on their profile pages. This cross-cultural study examines how audience-related variables affect a range of strategic self-presentation and image management behaviors online. Results from samples of Singaporean and American SNS users (N = 411) show that while Americans update their profiles with text-based wall posts more frequently, Singaporeans share significantly more photos. Audience diversity is positively associated with active management of other-provided information, and females share more photos and actively manage unwanted photo tagging. Cultural identity and the tendency to ‘friend’ unknown others interact on managing other-provided wall posts; individualistic cultural identity exhibited positive relationships with these reactions for those less likely to friend unknown others but negative ones for those more likely to friend unknown others. Implications for the theoretical understanding of and practical suggestions about self-presentation online are discussed.     

Evolving semantic web with social navigation

The Semantic Web (SW) is a meta-web built on the existing WWW to facilitate its access. SW expresses and exploits dependencies between web pages to yield focused search results. Manual annotation of web pages towards building a SW is hindered by at least two user dependent factors: users do not agree on an annotation standard, which can be used to extricate their pages inter-dependencies; and they are simply too lazy to use, undertake and maintain annotation of pages. In this paper, we present an alternative to exploit web pages dependencies: as users surf the net, they create a virtual surfing trail which can be shared with other users, this parallels social navigation for knowledge. We capture and use these trails to allow subsequent intelligent search of the web.People surfing the net with different interests and objectives do not leave similar and mutually beneficial trails. However, individuals in a given interest group produce trails that are of interest to the whole group. Moreover, special interest groups will be higher motivated than casual users to rate utility of pages they browse. In this paper, we introduce our system KAPUST1.2 (Keeper And Processor of User Surfing Trails). It captures user trails as they search the internet. It constructs a semantic web structure from the trails. The semantic web structure is expressed as a conceptual lattice guiding future searches. KAPUST is deployed as an E-learning software for an undergraduate class. First results indicated that indeed it is possible to process surfing trails into useful knowledge structures which can later be used to produce intelligent searching.     

Temporal pre-fetching of dynamic web pages

Although caching has been shown as an efficient technique to reduce the delay in generating web pages to meet the page requests from web users, it becomes less effective if the pages are dynamic and contain dynamic contents. In this paper, instead of using caching, we study the effectiveness of using pre-fetching to resolve the problems in handling dynamic web pages. Pre-fetching is a proactive caching scheme since a page is cached before the receipt of any page request for the page. In addition to the problem of which pages to be pre-fetched, another equally important question is when to perform the pre-fetching. To resolve the prediction and timing problems, we explore the temporal properties of the dynamic web pages and the timing issues in accessing the pages to determine which pages to be pre-fetched and the best time to pre-fetch the pages to maximize the cache hit probability of the pre-fetched page. If the required pages can be found in the cache validly, the response times of the requests can be greatly reduced. The proposed scheme is called temporal pre-fetching (TPF) in which we prioritize pre-fetching requests based on the predicted usability of the to-be pre-fetched pages. To minimize the impact of incorrect prediction in pre-fetching on processing of on-demand page requests, a qualifying examination is performed to remove unnecessary and low usability pre-fetching requests while they are waiting to be processed and just before their processing. We have implemented the proposed TPF scheme in a web server system and experiments have been performed to study its performance characteristics compared with conventional cache-only scheme using a benchmark auction application under different system and application settings. As shown in the experiment results, the overall system performance, i.e., response time, is improved as more page requests can be served immediately using pre-fetched pages.     

An integrated decision model for evaluating educational web sites from the fuzzy subjective and objective perspectives

With advances in information and network technologies, lots of data have been digitized to reveal information for users by the construction of Web sites. Unfortunately, they are both overloading and overlapping in Internet so that users cannot distinguish their quality. To address this issue in education, Hwang, Huang, and Tseng proposed a group decision system to evaluate the quality of educational Web sites by users’ and experts’ opinions. Their investigative source is solely stemmed from human intention, called the subjective perspective, to make judgments on the quality of Web sites. However, the nature of human beings in making decisions has a gap between intention and behavior. Asking people for eliciting thought is arduous to cause this gap. Human behavior, namely the objective perspective, is the other essential source to obtain human thinking and real doings. For this reason, we can use data mining approaches to acquire the objective source. In this research, we propose an integrated decision model applied in evaluating educational Web sites from the fuzzy subjective and objective perspectives. The former source is extracted by inquiring human opinion using a questionnaire, while the latter is gained automatically by a data mining technique, fuzzy clustering. An empirical study is carried out to validate the model capability.     

An agglomerative clustering algorithm using a dynamic k-nearest-neighbor list

In this paper, a new algorithm is developed to reduce the computational complexity of Ward’s method. The proposed approach uses a dynamic k-nearest-neighbor list to avoid the determination of a cluster’s nearest neighbor at some steps of the cluster merge. Double linked algorithm (DLA) can significantly reduce the computing time of the fast pairwise nearest neighbor (FPNN) algorithm by obtaining an approximate solution of hierarchical agglomerative clustering. In this paper, we propose a method to resolve the problem of a non-optimal solution for DLA while keeping the corresponding advantage of low computational complexity. The computational complexity of the proposed method DKNNA + FS (dynamic k-nearest-neighbor algorithm with a fast search) in terms of the number of distance calculations is O(N²), where N is the number of data points. Compared to FPNN with a fast search (FPNN + FS), the proposed method using the same fast search algorithm (DKNNA + FS) can reduce the computing time by a factor of 1.90-2.18 for the data set from a real image. In comparison with FPNN + FS, DKNNA + FS can reduce the computing time by a factor of 1.92-2.02 using the data set generated from three images. Compared to DLA with a fast search (DLA + FS), DKNNA + FS can decrease the average mean square error by 1.26% for the same data set.     

Why do people post and read personal messages in public? The motivation of using personal blogs and its effects on users’ loneliness,belonging, and well-being

The purpose of the current paper is to develop a theoretical model that identifies why people blog personal content and explains the effects of blogging in “real life.” Data from an online survey are analyzed using maximum likelihood procedures in LISREL 8.75 to test the structural model. Among 531 respondents from Cyworld, a popular social network and blogging site in South Korea, a randomly selected group of 251 users was used to develop the model. The other group of 280 users was used to confirm the usefulness of the revised model. Results (N = 251; N = 280) showed that impression management and voyeuristic surveillance are two major psychological factors that motivate individuals to post and read messages on personal blogs. Results also showed evidence for blogging’s real life consequences, measured by users’ perceived social support, loneliness, belonging, and subjective well-being.     

Age differences in online social networking – A study of user profiles and the social capital divide among teenagers and older users in MySpace

The aim of this research was to investigate age differences and similarities in the use of the social networking website MySpace, to explore potential differences in social capital among older people (users over 60 years of age) compared to teenagers (users between 13 and 19 years of age). We used locally developed web crawlers to collect data from MySpace’s user profile pages, and to quantify any differences that exist in the networks of friends of older people and teenagers. Content analysis was applied to investigate differences in social activities between the two age groups on MySpace, and the way they represent themselves on their profile pages. Our findings show a social capital divide: teenagers have larger networks of friends compared to older users of MySpace. On the other hand, we found that the majority of teenage users’ friends are in their own age range (age ± 2 years), whilst older people’s networks of friends tend to have a more diverse age distribution. In addition, our results show that teenagers tend to make more use of different media (e.g. video, music) within MySpace and use more self-references and negative emotions when describing themselves on their profile compared to older people.     

HilAnchor: Location Privacy Protection in the Presence of Users’ Preferences

Location privacy receives considerable attentions in emerging location based services.Most current practices however either ignore users’ preferences or incompletely fulfill privacy preferences.In this paper,we propose a privacy protection solution to allow users’ preferences in the fundamental query of k nearest neighbors (kNN).Particularly,users are permitted to choose privacy preferences by specifying minimum inferred region.Via Hilbert curve based transformation,the additional workload from users’ preferences is alleviated.Furthermore,this transformation reduces time-expensive region queries in 2-D space to range the ones in 1-D space.Therefore,the time efficiency,as well as communication efficiency,is greatly improved due to clustering properties of Hilbert curve.Further,details of choosing anchor points are theoretically elaborated.The empirical studies demonstrate that our implementation delivers both flexibility for users’ preferences and scalability for time and communication costs.     

A study of traffic, user behavior and pricing policies in a large campus network

Tsinghua University campus network is a large campus network in China, providing volume-based and flat-rate Internet access service for more than 31,000 students and staff. In order to better understand its traffic, user behavior and pricing policies to facilitate network planning and management, we collect a one-year-long flow-based traffic log and a 10-year-long user-based log at the boundary of this campus network, and then conduct an analysis study on these two data sets. In this paper, we first present characteristics of inbound traffic flows from the aspects of traffic prediction and inference. Then we analyze the geographical origins of incoming flows, and the result reveals that USA, Japan and Korea are the most important source countries of international traffic. Our user-based investigation shows that the properties of users have important influence on their behavior, e.g., major has stronger influence on users’ online time, while occupation has stronger influence on users’ international traffic volume. We also find that there are more and more users choosing flat rate pricing scheme instead of volume based pricing scheme, and these users tend to over-provision when they subscribe from tiered pricing options.     

Refined memorization for vertex cover

Memorization is a technique which allows to speed up exponential recursive algorithms at the cost of an exponential space complexity. This technique already leads to the currently fastest algorithm for fixed-parameter vertex cover, whose time complexity is O(k^1.2832k^1.5+kn), where n is the number of nodes and k is the size of the vertex cover. Via a refined use of memorization, we obtain an O(k^1.2759k^1.5+kn) algorithm for the same problem. We moreover show how to further reduce the complexity to O(k^1.2745k⁴+kn).     

Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study

Current recommender systems attempt to identify appealing items for a user by applying syntactic matching techniques, which suffer from significant limitations that reduce the quality of the offered suggestions. To overcome this drawback, we have developed a domain-independent personalization strategy that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relationships inferred between the user’s preferences and the available items. Our reasoning-based approach improves the quality of the suggestions offered by the current personalization approaches, and greatly reduces their most severe limitations. To validate these claims, we have carried out a case study in the Digital TV field, in which our strategy selects TV programs interesting for the viewers from among the myriad of contents available in the digital streams. Our experimental evaluation compares the traditional approaches with our proposal in terms of both the number of TV programs suggested, and the users’ perception of the recommendations. Finally, we discuss concerns related to computational feasibility and scalability of our approach.     

Chromatic distribution of k-nearest neighbors of a line segment in a planar colored point set

Let P be a set of n colored points distributed arbitrarily in R². The chromatic distribution of the k-nearest neighbors of a query line segment ? is to report the number of points of each color among the k-nearest points of the query line segment. While solving this problem, we have encountered another interesting problem, namely the semicircular range counting query. Here a set of n points is given. The objective is to report the number of points inside a given semicircular range. We propose a simple algorithm for this problem with preprocessing time and space complexity O(n³), and the query time complexity O(logn). Finally, we propose the algorithm for reporting the chromatic distribution of k nearest neighbors of a query line segment. Using our proposed technique for semicircular range counting query, it runs in O(log²n) time.     

Cache-oblivious index for approximate string matching

This paper revisits the problem of indexing a text for approximate string matching. Specifically, given a text T of length n and a positive integer k, we want to construct an index of T such that for any input pattern P, we can find all its k-error matches in T efficiently. This problem is well-studied in the internal-memory setting. Here, we extend some of these recent results to external-memory solutions, which are also cache-oblivious. Our first index occupies O((nlog^kn)/B) disk pages and finds all k-error matches with O((|P|+occ)/B+log^knloglog_Bn) I/Os, where B denotes the number of words in a disk page. To the best of our knowledge, this index is the first external-memory data structure that does not require Ω(|P|+occ+poly(logn)) I/Os. The second index reduces the space to O((nlogn)/B) disk pages, and the I/O complexity is O((|P|+occ)/B+log^k(k+1)nloglogn).     

Age differences in information finding tasks: Performance and visual exploration strategy with different web page layouts

Recent research suggests that older Internet users seem to find it more difficult to locate navigation links than to find information content in web pages. One possibility is that older Internet users’ visual exploration of web pages is more linear in nature, even when this type of processing is not appropriate for the task. In the current study, the eye movements of young and older Internet users were recorded using an ecological version of the web pages or a discursive version designed to induce a linear exploration. The older adults found more targets when performing content-oriented compared to navigation-oriented searches, thus replicating previous results. Moreover, they performed less well than young people only when required to locate navigation links and tended to outperform the younger participants in content-oriented searches. Although the type of search task and type of web page resulted in different visual strategies, little or no support was found for the hypothesis that older participants explore web pages in a more linear way in cases where this strategy was not appropriate. The main conclusion is that differences in visual exploration do not seem to mediate the specific difficulty older adults experience in navigation-oriented searches in web pages.     

CP2: Cryptographic privacy protection framework for online social networks

In Online Social Networks (OSNs), users interact with each other by sharing their personal information. One of the concerns in OSNs is how user privacy is protected since the OSN providers have full control over users’ data. The OSN providers typically store users’ information permanently; the privacy controls embedded in OSNs offer few options to users for customizing and managing the dissipation of their data over the network. In this paper, we propose an efficient privacy protection framework for OSNs that can be used to protect the privacy of users’ data and their online social relationships from third parties. The recommended framework shifts the control over data sharing back to the users by providing them with flexible and dynamic access policies. We employ a public-key broadcast encryption scheme as the cryptographic tool for managing information sharing with a subset of a user’s friends. The privacy and complexity evaluations show the superiority of our approach over previous.     

Understanding factors affecting perceived sociability of social software

Sociability is considered to be important to the success of social software. The goal of the current study is to identify factors that affect the users’ perception of the sociability of social software and to examine the impact of sociability on the users’ attitude and behavior intentions. In a pilot study, 35 web users were interviewed to gain understanding of how they use social software to supplement their social life and to explore the possible factors that influence the users’ utilization of social software. In the first study, a questionnaire was developed, and 163 valid responses were collected. From the factor analysis results, seven important factors for social software design emerged, which accounts for 63.3% of the total variance. In the second study, 246 participants were asked to evaluate one of ten popular social applications with respect to the seven factors, their perceived sociability, and their attitudes and intention regarding the use of the applications. Results show that sociability is influenced by social climate, benefits and purposes, people, interaction richness, self-presentation, and support for formal interaction. System competency is not a sociability factor, but it significantly influences the user’s experience. Sociability and system competency, when combined, can predict 43% of users’ attitude towards social software and 51% of their intentions to use social software.