Magdalini Eirinaki

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 users 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.

SEWeP: using site semantics and a taxonomy to enhance the Web personalization process

Web personalization is the process of customizing a Web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the users navigational behavior. Integrating usage data with content, structure or user profile data enhances the results of the personalization process. In this paper, we present SEWeP, a system that makes use of both the usage logs and the semantics of a Web sites content in order to personalize it. Web content is semantically annotated using a conceptual hierarchy (taxonomy). We introduce C-logs, an extended form of Web usage logs that encapsulates knowledge derived from the link semantics. C-logs are used as input to the Web usage mining process, resulting in a broader yet semantically focused set of recommendations.

Feature-based opinion mining and ranking

The proliferation of blogs and social networks presents a new set of challenges and opportunities in the way information is searched and retrieved. Even though facts still play a very important role when information is sought on a topic, opinions have become increasingly important as well. Opinions expressed in blogs and social networks are playing an important role influencing everything from the products people buy to the presidential candidate they support. Thus, there is a need for a new type of search engine which will not only retrieve facts, but will also enable the retrieval of opinions. Such a search engine can be used in a number of diverse applications like product reviews to aggregating opinions on a political candidate or issue. Enterprises can also use such an engine to determine how users perceive their products and how they stand with respect to competition. This paper presents an algorithm which not only analyzes the overall sentiment of a document/review, but also identifies the semantic orientation of specific components of the review that lead to a particular sentiment. The algorithm is integrated in an opinion search engine which presents results to a query along with their overall tone and a summary of sentiments of the most important features.

Query Recommendations for Interactive Database Exploration

Relational database systems are becoming increasingly popular in the scientific community to support the interactive exploration of large volumes of data. In this scenario, users employ a query interface (typically, a web-based client) to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. First-time users, however, may not have the necessary knowledge to know where to start their exploration. Other times, users may simply overlook queries that retrieve important information. To assist users in this context, we draw inspiration from Web recommender systems and propose the use of personalized query recommendations. The idea is to track the querying behavior of each user, identify which parts of the database may be of interest for the corresponding data analysis task, and recommend queries that retrieve relevant data. We discuss the main challenges in this novel application of recommendation systems, and outline a possible solution based on collaborative filtering. Preliminary experimental results on real user traces demonstrate that our framework can generate effective query recommendations.

A Trust-Aware System for Personalized User Recommendations in Social Networks

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networking applications, microblogging, or customer review sites. In this environment, trust is becoming an essential quality among user interactions and the recommendation for useful content and trustful users is crucial for all the members of the network. In this paper, we introduce a framework for handling trust in social networks, which is based on a reputation mechanism that captures the implicit and explicit connections between the network members, analyzes the semantics and dynamics of these connections, and provides personalized user recommendations to the network members.

A Study on Social Network Metrics and Their Application in Trust Networks

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networks, micro logging, or customer review sites. Such media often serve as platforms for information dissemination and product placement or promotion. In this environment, influence and trust are becoming essential qualities among user interactions. In this work, we perform an extensive study of various metrics related to the aforementioned elements, and their effect in the process of information propagation in the virtual world. In order to better understand the properties of links and the dynamics of social networks, we distinguish between permanent and transient links and in the latter case, we consider the link freshness. Moreover, we distinguish between local and global influence and compare suggestions provided by locally or globally trusted users.

QueRIE: Collaborative Database Exploration

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the users querying behavior and finds matching patterns in the systems query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active users session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach.

Mining Frequent Generalized Patterns for Web Personalization in the Presence of Taxonomies

The Web is a continuously evolving environment, since its content is updated on a regular basis. As a result, the traditional usage-based approach to generate recommendations that takes as input the navigation paths recorded on the Web page level, is not as effective. Moreover, most of the content available online is either explicitly or implicitly characterized by a set of categories organized in a taxonomy, allowing the page-level navigation patterns to be generalized to a higher, aggregate level. In this direction, the authors present the Frequent Generalized Pattern FGP algorithm. FGP takes as input the transaction data and a hierarchy of categories and produces generalized association rules that contain transaction items and/or item categories. The results can be used to generate association rules and subsequently recommendations for the users. The algorithm can be applied to the log files of a typical Web site; however, it can be more helpful in a Web 2.0 application, such as a feed aggregator or a digital library mediator, where content is semantically annotated and the taxonomic nature is more complex, requiring us to extend FGP in a version called FGP+. The authors experimentally evaluate both algorithms using Web log data collected from a newspaper Web site.

IKUM: an integrated web personalization platform based on content structures and user behavior

Web personalization is the process of customizing a web site to the needs of each specific user or set of users, taking advantage of the knowledge acquired through the analysis of the users navigational behavior. The objective of the I-KnowUMine project (IKUM) is to develop an integrated platform (referred to in the paper as the “IKUM system”) that uses state of the art technology and research results from different application domains in order to provide the basis for the development of online services in a wide range of application areas, presenting personalized content, services and applications to users in a structure more suited to their needs. The benefits provided by the IKUM system result mainly from the combination and integration of technology advances in areas such as Web Mining, Content Management, Personalization and Portals. As a result of this novel combination of these technologies, users of the IKUM system will benefit from the optimal logical structure of information/content provided by the system, allowing them to efficiently execute their processes and to reach their information targets.

Application of Social Network Metrics to a Trust-Aware Collaborative Model for Generating Personalized User Recommendations

Social network analysis has emerged as a key technique in modern sociology, but has recently gained a lot of interest in Web mining research, because of the advent and the increasing popularity of social media, such as blogs, social networks, micro-blogging, customer review sites etc. Such media often serve as platforms for information dissemination and product placement or promotion. One way to improve the quality of recommendations provided to the members of social networks is to use trustworthy resources. In this environment, community-based reputation can help estimating the trustworthiness of individual users. Consequently, influence and trust are becoming essential qualities among user interactions. In this work, we perform an extensive study of various metrics related to the aforementioned elements, and of their effect in the process of information propagation in social networks. In order to better understand the properties of links and the dynamics of social networks, we distinguish between permanent and transient links and in the latter case, we consider the link freshness. Moreover, we distinguish between the propagation of trust in a local level and the effect of global influence and compare suggestions provided by locally trusted or globally influential users.