Alessandro Micarelli

User profiles for personalized information access

The amount of information available online is increasing exponentially. While this information is a valuable resource, its sheer volume limits its value. Many research projects and companies are exploring the use of personalized applications that manage this deluge by tailoring the information presented to individual users. These applications all need to gather, and exploit, some information about individuals in order to be effective. This area is broadly called user profiling. This chapter surveys some of the most popular techniques for collecting information about users, representing, and building user profiles. In particular, explicit information techniques are contrasted with implicitly collected user information using browser caches, proxy servers, browser agents, desktop agents, and search logs. We discuss in detail user profiles represented as weighted keywords, semantic networks, and weighted concepts. We review how each of these profiles is constructed and give examples of projects that employ each of these techniques. Finally, a brief discussion of the importance of privacy protection in profiling is presented.

Personalized search on the world wide web

With the exponential growth of the available information on theWorld Wide Web, a traditional search engine, even if based on sophisticated document indexing algorithms, has difficulty meeting efficiency and effectiveness performance demanded by users searching for relevant information. Users surfing the Web in search of resources to satisfy their information needs have less and less time and patience to formulate queries, wait for the results and sift through them. Consequently, it is vital in many applications - for example in an e-commerce Web site or in a scientific one - for the search system to find the right information very quickly. PersonalizedWeb environments that build models of short-term and long-term user needs based on user actions, browsed documents or past queries are playing an increasingly crucial role: they form a winning combination, able to satisfy the user better than unpersonalized search engines based on traditional Information Retrieval (IR) techniques. Several important user personalization approaches and techniques developed for the Web search domain are illustrated in this chapter, along with examples of real systems currently being used on the Internet.

Adaptive course generation through learning styles representation

This paper presents an approach to automatic course generation and student modeling. The method has been developed during the European funded projects Diogene and Intraserv, focused on the construction of an adaptive e-learning platform. The aim of the platform is the automatic generation and personalization of courses, taking into account pedagogical knowledge on the didactic domain as well as statistic information on both the student’s knowledge degree and learning preferences. Pedagogical information is described by means of an innovative methodology suitable for effective and efficient course generation and personalization. Moreover, statistic information can be collected and exploited by the system in order to better describe the student’s preferences and learning performances. Learning material is chosen by the system matching the student’s learning preferences with the learning material type, following a pedagogical approach suggested by Felder and Silverman. The paper discusses how automatic learning material personalization makes it possible to facilitate distance learning access to both able-bodied and disabled people. Results from the Diogene and Intraserv evaluation are reported and discussed.

Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System

A case study in adaptive information filtering systems for the Web is presented. The described system comprises two main modules, named HUMOS and WIFS. HUMOS is a user modeling system based on stereotypes. It builds and maintains long term models of individual Internet users, representing their information needs. The user model is structured as a frame containing informative words, enhanced with semantic networks. The proposed machine learning approach for the user modeling process is based on the use of an artificial neural network for stereotype assignments. WIFS is a content-based information filtering module, capable of selecting html/text documents on computer science collected from the Web according to the interests of the user. It has been created for the very purpose of the structure of the user model utilized by HUMOS. Currently, this system acts as an adaptive interface to the Web search engine ALTA VISTATM. An empirical evaluation of the system has been made in experimental settings. The experiments focused on the evaluation, by means of a non-parametric statistics approach, of the added value in terms of system performance given by the user modeling component; it also focused on the evaluation of the usability and user acceptance of the system. The results of the experiments are satisfactory and support the choice of a user model-based approach to information filtering on the Web.

An approach to social recommendation for context-aware mobile services

Nowadays, several location-based services (LBSs) allow their users to take advantage of information from the Web about points of interest (POIs) such as cultural events or restaurants. To the best of our knowledge, however, none of these provides information taking into account user preferences, or other elements, in addition to location, that contribute to define the context of use. The provided suggestions do not consider, for example, time, day of week, weather, user activity or means of transport. This article describes a social recommender system able to identify user preferences and information needs, thus suggesting personalized recommendations related to POIs in the surroundings of the users current location. The proposed approach achieves the following goals: (i) to supply, unlike the current LBSs, a methodology for identifying user preferences and needs to be used in the information filtering process; (ii) to exploit the ever-growing amount of information from social networking, user reviews, and local search Web sites; (iii) to establish procedures for defining the context of use to be employed in the recommendation of POIs with low effort. The flexibility of the architecture is such that our approach can be easily extended to any category of POI. Experimental tests carried out on real users enabled us to quantify the benefits of the proposed approach in terms of performance improvement.

Infoweb: An adaptive information filtering system for the cultural heritage domain

This paper presents a system developed for adaptive retrieval and the filtering of documents belonging to digital libraries available on the Web. This system, called InfoWeb, is currently in operation on the ENEA (National Entity for Alternative Energy) digital library Web site reserved to the cultural heritage and environment domain. InfoWeb records the user information needs in a user model, created through a representation, which extends the traditional vector space model and takes the form of a semantic network consisting of co-occurrences between index terms. The initial user model is built on the basis of stereotypes, developed through a clustering of the collection by using specific documents as a starting point. The users query can be expanded in an adaptive way, using the user model formulated by the user himself. The system has been tested on the entire collection comprising about 14,000 documents in HTML/text format. The results of the experiments are satisfactory both in terms of performance and in terms of the systems ability to adapt itself to the users shifting interests.

Social semantic query expansion

Weak semantic techniques rely on the integration of Semantic Web techniques with social annotations and aim to embrace the strengths of both. In this article, we propose a novel weak semantic technique for query expansion. Traditional query expansion techniques are based on the computation of two-dimensional co-occurrence matrices. Our approach proposes the use of three-dimensional matrices, where the added dimension is represented by semantic classes (i.e., categories comprising all the terms that share a semantic property) related to the folksonomy extracted from social bookmarking services, such as delicious and StumbleUpon. The results of an indepth experimental evaluation performed on both artificial datasets and real users show that our approach outperforms traditional techniques, such as relevance feedback and personalized PageRank, so confirming the validity and usefulness of the categorization of the user needs and preferences in semantic classes. We also present the results of a questionnaire aimed to know the users opinion regarding the system. As one drawback of several query expansion techniques is their high computational costs, we also provide a complexity analysis of our system, in order to show its capability of operating in real time.

Social Tagging in Query Expansion: A New Way for Personalized Web Search

Social networks and collaborative tagging systems are rapidly gaining popularity as primary means for sorting and sharing data: users tag their bookmarks in order to simplify information dissemination and later lookup. Social Bookmarking services are useful in two important respects: first, they can allow an individual to remember the visited URLs, and second, tags can be made by the community to guide users towards valuable content. In this paper we focus on the latter use: we present a novel approach for personalized web search using query expansion. We further extend the family of well-known co-occurence matrix technique models by using a new way of exploring social tagging services. Our approach shows its strength particularly in the case of disambiguation of word contexts. We show how to design and implement such a system in practice and conduct several experiments on a real web-dataset collected from Regione Lazio Portal1. To the best of our knowledge this is the first study centered on using social bookmarking and tagging techniques for personalization

Adaptive focused crawling

The large amount of available information on the Web makes it hard for users to locate resources about particular topics of interest. Traditional search tools, e.g., search engines, do not always successfully cope with this problem, that is, helping users to seek the right information. In the personalized search domain, focused crawlers are receiving increasing attention, as a well-founded alternative to search theWeb. Unlike a standard crawler, which traverses theWeb downloading all the documents it comes across, a focused crawler is developed to retrieve documents related to a given topic of interest, reducing the network and computational resources. This chapter presents an overview of the focused crawling domain and, in particular, of the approaches that include a sort of adaptivity. That feature makes it possible to change the system behavior according to the particular environment and its relationships with the given input parameters during the search.

Context-aware movie recommendation based on signal processing and machine learning

Most of the existing recommendation engines do not take into consideration contextual information for suggesting interesting items to users. Features such as time, location, or weather, may affect the user preferences for a particular item. In this paper, we propose two different context-aware approaches for the movie recommendation task. The first is an hybrid recommender that assesses available contextual factors related to time in order to increase the performance of traditional CF approaches. The second approach aims at identifying users in a household that submitted a given rating. This latter approach is based on machine learning techniques, namely, neural networks and majority voting classifiers. The effectiveness of both the approaches has been experimentally validated using several evaluation metrics and a large dataset.