Cross-domain Recommendations based on semantically-enhanced User Web Behavior

Information seeking in the Web can be facilitated by recommender systems that guide the users in a personalized manner to relevant resources in the large space of the possible options in the Web. This work investigates how to model people\u27s Web behavior at multiple sites and learn to predict future preferences, in order to generate relevant cross-domain recommendations. This thesis contributes with novel techniques for building cross-domain recommender systems in an open Web setting

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

Modeling of Human Web Browsing Based on Theory of Interest-Driven Behavior

The ability to generate human-like Web-browsing requests is essential for testing and optimization of WWW systems. In this thesis a new model of human-browsing behavior the so-called HBB-IDT model has been proposed. The model is based on the theory of interest-driven human behavior and does not assume the availability of server-side logs (i.e., previous browsing history). The defining features of the model are: (1) human browsing on the internet is regarded as a dynamic interest-driven process; and (2) the users browsing interests are linked to actual characteristics of the visited Web pages. Given that the model does not rely on the existence of Web logs, it can be applied more generally than the previously proposed data-mining approaches. The experimental results show that the probability of generating human-like browsing sequences is much higher using the HBB-IDT model than using the pre-set request list model or the random crawling model

Interest identification from browser tab titles: A systematic literature review

Modeling and understanding users interests has become an essential part of our daily lives. A variety of business processes and a growing number of companies employ various tools to such an end. The outcomes of these identification strategies are beneficial for both companies and users: the former are more likely to offer services to those customers who really need them, while the latter are more likely to get the service they desire. Several works have been carried out in the area of user interests identification. As a result, it might not be easy for researchers, developers, and users to orient themselves in the field; that is, to find the tools and methods that they most need, to identify ripe areas for further investigations, and to propose the development and adoption of new research plans. In this study, to overcome these potential shortcomings, we performed a systematic literature review on user interests identification. We used as input data browsing tab titles. Our goal here is to offer a service to the readership, which is capable of systematically guiding and reliably orienting researchers, developers, and users in this very vast domain. Our findings demonstrate that the majority of the research carried out in the field gathers data from either social networks (such as Twitter, Instagram and Facebook) or from search engines, leaving open the question of what to do when such data is not available

Increasing Search Success Rate by Comparing Queries Formation Patterns

Abstract: Retrieving suitable information to a user's information request is a crucial task of search engines. However, most of conventional search engines that are based on pattern matching schemes tend to provide too many improper results, most of which are not corresponding to a user's request. It is due to the uncertainty of queries. To overcome this problem, we distinguished two different patterns after extensive investigation about the process of query formation. One is the general information seeking pattern and the other is the scholastic information retrieving pattern. The general information seeking pattern has a tendency to correspond to crossrelational query formation process pattern, and the scholastic information retrieving pattern corresponds to goal-oriented structured query pattern. This paper proposes a method to improve search results by utilizing these two query formation patterns. The study on the formation of general cross-relational queries, and goal-oriented structured query pattern by which comparing or modeling will improve the efficiency of navigation. These proposals will benefit not only search engine developers and library managers, but also search engine users and researchers. This paper is expected to provide more suitable searching results than those obtained by using current typical information retrieval engines