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 user's 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.

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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.

Social networks have become very important for networking, communications, and content sharing. Social networking applications generate a huge amount of data on a daily basis and social networks constitute a growing field of research, because of the heterogeneity of data and structures formed in them, and their size and dynamics. When this wealth of data is leveraged by recommender systems, the resulting coupling can help address interesting problems related to social engagement, member recruitment, and friend recommendations. In this work we review the various facets of large-scale social recommender systems, summarizing the challenges and interesting problems and discussing some of the solutions.

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, micro-blogging, 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 work, 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.

Markov models have been widely used for modelling users' navigational behaviour in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users' navigation is used to extract popular web paths and predict current users' next steps. Such purely usage-based probabilistic models, however, present certain shortcomings. Since the prediction of users' navigational behaviour is based solely on the usage data, structural properties of the Web graph are ignored. Thus important -in terms of pagerank authority score -paths may be underrated. In this paper we present a hybrid probabilistic predictive model extending the properties of Markov models by incorporating link analysis methods. More specifically, we propose the use of a PageRank-style algorithm for assigning prior probabilities to the web pages based on their importance in the web site's graph. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches.

This demonstration presents QueRIE, a recommender system that supports interactive database exploration. This system aims at assisting non-expert users of scientific databases by tracking their querying behavior and generating personalized query recommendations. The system is supported by two recommendation engines and the underlying recommendation algorithms. The first identifies potentially "interesting" parts of the database related to the corresponding data analysis task by locating those database parts that were accessed by similar users in the past. The second identifies structurally similar queries to the ones posted by the current user. Both approaches result in a recommendation set of SQL queries that is provided to the user to modify, or directly post to the database. The demonstrated system will enable users to query and get real-time recommendations from the SkyServer database, using user traces collected from the SkyServer query log.

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