Sequence Of Web Pages Research Articles

DECT: Distributed Evolving Context Tree for Understanding User Behavior Pattern Evolution

Internet user behavior models characterize user browsing dynamics or the transitions among web pages. The models help Internet companies improve their services by accurately targeting customers and providing them the information they want. For instance, specific web pages can be customized and prefetched for individuals based on sequences of web pages they have visited. Existing user behavior models abstracted as time-homogeneous Markov models cannot efficiently model user behavior variation through time. This demo presents DECT, a scalable time-variant variable-order Markov model. DECT digests terabytes of user session data and yields user behavior patterns through time. We realize DECT using Apache Spark and deploy it on top of Yahoo! infrastructure. We demonstrate the benefits of DECT with anomaly detection and ad click rate prediction applications. DECT enables the detection of higher-order path anomalies and provides deep insights into ad click rates with respect to user visiting paths.

Indexing Associated Knowledge Flow on the Web

The Associated Knowledge Flow (AKF) on the Web is an ordered sequence of Web pages that have associated relation. The associated relation from page A to page B indicates that users who have browsed page A is likely to also browse page B. The motivation of this paper is to index the AKFs on the Web and provide users AKFs instead of discrete resources. We build a scalable P2P-based Web resource-sharing system and design two kinds of ID spaces (hash ID space and semantic ID space) on it to index resources and facilitate AKF discovery. Theoretical analysis and simulations show that such a system can achieve logarithmic performance and cost.

Modeling user hidden navigational behavior for Web recommendation

Web users exhibit a variety of navigational interests through clicking a sequence of Web pages. Analyses of Web usage data will lead to discovering Web user access patterns, and in turn, facilitating users to locate more preferable Web contents via collaborative recommendation techniques. In the context of Web usage mining, Latent Semantic Analysis (LSA) based on probability inference provides a promising approach to capture not only user hidden navigational patterns, but also the associations between users, pages and hidden navigational patterns. The discovered user access patterns could be used as a usage reference base for identifying the new user's access preferences and making usage-based collaborative Web recommendations. In this paper, we propose a novel usage-based Web recommendation framework, in which Latent Dirichlet Allocation (LDA) is employed to learn the underlying task space from the training Web log data and infer the task distribution for a target user via task inference. The main advantages of the adapted LDA model are its capabilities of efficiently learning the semantic usage information from the Web log data and effectively inferring the access preference of the target user even with a few Web clicks that might be unseen in the training data. In this paper, we also investigate the determination of an optimizing task number, which is another important problem commonly encountered in latent semantic analysis. Experiments conducted on a real Web log dataset show that this approach can achieve better recommendation performance in comparison to other existing techniques. And the discovered task-simplex expression can also provide a better interpretation for Web designers or users to better understand the user navigational preference.

Mobile web searching

Searching the Web on a mobile device such as a cell phone poses unique problems for the user that cannot be easily overcome through the interface. Given the restricted display of current wireless devices, users cannot efficiently examine search results when they are presented as a long list. In our system, Hyponym, we organize search results into topics and allow the user to explore relevant resources within each topic. The data to support this kind of analysis are drawn from sequences of Web pages that users have visited while searching. We have found that queries that lead users to request similar pages tend to be topically related. From this simple observation, we form clusters or groups of queries that are used to structure the search display. Hyponym can be thought of as a collaborative filtering application that takes as its input navigation data from previous Web-searching tasks. Because of this, the lists that we return within each topic or query group tend to be much more accurate than those recommended by standard search engines. In fact, Hyponym can significantly cut the number of pages a user visits while searching the Web. We are currently extending the Hyponym framework to support personalization and location-specific information.3