Bernadetta Mianowska

Tuning user profiles based on analyzing dynamic preference in document retrieval systems

Modeling users’ preferences and needs is one of the most important personalization tasks in information retrieval domain. In this paper a model for user profile tuning in document retrieval systems is considered. Methods for tuning the user profile based on analysis of user preferences dynamics are experimentally evaluated to check whether with growing history of user activity the created user profile can converge to his preferences. As the statistical analysis of series of simulations has shown, proposed methods of user profile actualization are effective in the sense that the distance between user preferences and his profile becomes smaller and smaller along with time.

Using knowledge integration techniques for user profile adaptation method in document retrieval systems

Knowledge integration is a very important and useful technique to combine information from different sources and different formats. In the Information Retrieval field, integration of knowledge can be understood in many ways. In this paper a method of user personalization in information retrieval using integration technique is presented. As user delivers new knowledge about himself, this knowledge should be integrated with previous knowledge contained in the user profile. Our proposed method is analyzed in terms of integration postulates. A list of desirable properties of this method and its proofs are presented. Simulated experimental evaluation has shown that proposed method is effective and the updated profile is proper since it is closer and closer to the user preferences.

A method for user profile adaptation in document retrieval

On the Internet the number of Web pages and other documents has grown so fast that it is very hard to find needed information. Search engines are still improving their retrieval methods but still many irrelevant documents are presented in the results. A solution to this problem is to get to know the user, his interests, preferences and habits and use this information in retrieval process. In this paper a user profile and its adaptation method is proposed. To evaluate the proposed method, simulation of user behaviour is described. Performed experimental evaluation shows that the distance between created user profile and user preferences is decreasing with subsequent actualization processes steps.

A heuristic method for collaborative recommendation using hierarchical user profiles

Document recommendation in information retrieval is a well known problem. Recommending a profile in order to personalize document search is a less common approach. In this paper a specific solution to profile recommendation is proposed, by use of knowledge integration methods. A hierarchical user profile is defined to represent the user. For each new user joining an information retrieval system, a prepared non-empty profile is assigned based on other similar users. To create such a profile, knowledge integration methods are used. A set of postulates are proposed to describe such representative profile. Criteria measures are used to determine if a solution to a specific algorithm satisfies these postulates. Three integration algorithms are proposed and evaluated, including a heuristic algorithm. In future research, these algorithms will be used in a practical system.

Agent Technology for Information Retrieval in Internet

Searching for useful information is nowadays difficult because of the information overload problem in a global network consisting of heterogeneous sources. Intelligent information agents, or multiagent systems consisting of those, are one of the solutions to this problem. This paper is a short case study of existing information retrieval systems based on agent approaches. User profiles and searching issues are two main issues described and a functional categorization of independent profiling systems or independent searching systems is proposed, as well as a short description of a hybrid profiling and searching system.

A framework for data warehouse federations building

Data warehouse federations are a next step in modern information storage and processing systems. They are a system of classical data warehouses connected by an integrated schema, that allows access to all the data of the source warehouses. We propose a generic approach to create a data warehouse federation, providing methods for schema integration (in order to create the federation schema), query decomposition (in order to translate the query to the federation into queries to subsequent warehouses) and result integration (to select the answer that best suits the user information need).We test this approach by creating a basic implementation and present a working example.

Analysis of Social Network’s Structural Properties in Huge Community Portal

The structure Internet-based social network of Nasza-klasa.pl – a huge (7.5 million users) social community portal was analyzed with respect to popular social network structural properties. We have found out that the network structure is typical for affiliation network and proposed its hypergraph representation. The temporal changes of the network structure were also analyzed.