Pasquale Lops
Università degli Studi di Bari, Dipartimento di Informatica, Faculty Member
Research Interests:
The vector space model (VSM) emerged for almost three decades as one of the most effective approaches in the area of Information Retrieval (IR), thanks to its good compromise between expressivity, effectiveness and simplicity. Although... more
The vector space model (VSM) emerged for almost three decades as one of the most effective approaches in the area of Information Retrieval (IR), thanks to its good compromise between expressivity, effectiveness and simplicity. Although Information Retrieval and Information Filtering (IF) undoubtedly represent two related research areas, the use of VSM in Information Filtering is much less analyzed, especially for content-based recommender systems.
Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, against the attributes of a content object. The Web 2.0 (r) evolution and the advent of user generated... more
Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, against the attributes of a content object. The Web 2.0 (r) evolution and the advent of user generated content (UGC) have changed the game for personalization, since the role of people has evolved from passive consumers of information to that of active contributors.
The amount of information available on the web, as well as the number of e-businesses and web shoppers, is growing exponentially. Customers have to spend a lot of time to browse the net in order to find relevant information. One way to... more
The amount of information available on the web, as well as the number of e-businesses and web shoppers, is growing exponentially. Customers have to spend a lot of time to browse the net in order to find relevant information. One way to overcome this problem is to use dialoguing agents that exploit user profiles to generate personal recommendations. This paper presents a system, designed according to this approach, that adopts a query refinement mechanism to improve the search process of an Internet commerce web site.
This paper describes OTTHO (On the Tip of my THOught), a system designed for solving a language game, called Guillotine, which demands knowledge covering a broad range of topics, such as movies, politics, literature, history, proverbs,... more
This paper describes OTTHO (On the Tip of my THOught), a system designed for solving a language game, called Guillotine, which demands knowledge covering a broad range of topics, such as movies, politics, literature, history, proverbs, and popular culture. The rule of the game is simple: the player observes five words, generally unrelated to each other, and in one minute she has to provide a sixth word, semantically connected to the others.
This paper describes OTTHO (On the Tip of my THOught), a system designed for solving a language game called Guillotine. The rule of the game is simple: the player observes five words, generally unrelated to each other, and in one minute... more
This paper describes OTTHO (On the Tip of my THOught), a system designed for solving a language game called Guillotine. The rule of the game is simple: the player observes five words, generally unrelated to each other, and in one minute she has to provide a sixth word, semantically connected to the others. The system exploits several knowledge sources, such as a dictionary, a set of proverbs, and Wikipedia to realize a knowledge infusion process.
Abstract Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, against the attributes of a content object. This paper describes a content-based recommender... more
Abstract Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, against the attributes of a content object. This paper describes a content-based recommender system, called FIRSt, that integrates user generated content (UGC) with semantic analysis of content.
Abstract As proved by the continuous growth of the number of web sites which embody recommender systems as a way of personalizing the experience of users with their content, recommender systems represent one of the most popular... more
Abstract As proved by the continuous growth of the number of web sites which embody recommender systems as a way of personalizing the experience of users with their content, recommender systems represent one of the most popular applications of principles and techniques coming from Information Filtering (IF).
Throughout the last decade, the area of Digital Libraries (DL) get more and more interest from both the research and development communities. Likewise, since the release of new platforms enriches them with new features and makes DL more... more
Throughout the last decade, the area of Digital Libraries (DL) get more and more interest from both the research and development communities. Likewise, since the release of new platforms enriches them with new features and makes DL more powerful and effective, the number of web sites integrating these kind of tools is rapidly growing. In this paper we propose an approach for the exploitation of digital libraries for personalization goal in cultural heritage scenario.
Recommender Systems try to assist users to access complex information spaces regarding their long term needs and preferences. Various recommendation techniques have been investigated and each one has its own strengths and weaknesses.... more
Recommender Systems try to assist users to access complex information spaces regarding their long term needs and preferences. Various recommendation techniques have been investigated and each one has its own strengths and weaknesses. Especially, content-based techniques suffer of overspecialization problem. We propose to inject diversity in the recommendation task by exploiting the content-based user profile to spot potential surprising suggestions.
The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users... more
The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems.
Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user [1]. They exploit adaptive and intelligent systems technologies and have already proved to be valuable for coping with the... more
Recommender Systems (RSs) are software tools and techniques providing suggestions for items to be of use to a user [1]. They exploit adaptive and intelligent systems technologies and have already proved to be valuable for coping with the information overload problem in several application domains.
Personalized electronic program guides help users overcome information overload in the TV and video domain by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or... more
Personalized electronic program guides help users overcome information overload in the TV and video domain by exploiting recommender systems that automatically compile lists of novel and diverse video assets, based on implicitly or explicitly defined user preferences. In this context, we assume that user preferences can be specified by program genres (documentary, sports,…) and that an asset can be labeled by one or more program genres, thus allowing an initial and coarse preselection of potentially interesting assets.
Abstract Museums have recognized the need for supporting visitors in fulfilling a personalized experience when visiting artwork collections, and they have started to adopt recommender systems as a way to meet this requirement.... more
Abstract Museums have recognized the need for supporting visitors in fulfilling a personalized experience when visiting artwork collections, and they have started to adopt recommender systems as a way to meet this requirement. Content-based recommender systems analyze features of artworks previously rated by a visitor and build a visitor model or profile, in which preferences and interests are stored, based on those features.
The amount of information available on the web, as well as the number of e-businesses and web shoppers is growing exponentially. Customers spend a lot of time to browse the net in order to find relevant product information. One way to... more
The amount of information available on the web, as well as the number of e-businesses and web shoppers is growing exponentially. Customers spend a lot of time to browse the net in order to find relevant product information. One way to overcome this problem is to use dialoguing agents that exploit the knowledge stored in user profiles in order to generate personal recommendations. This paper presents a general framework designed according to this idea in order to develop intelligent e-business applications.
This paper presents MyMusic, a system that exploits social media sources for generating personalized music playlists. This work is based on the idea that information extracted from social networks, such as Facebook and Last. fm, might be... more
This paper presents MyMusic, a system that exploits social media sources for generating personalized music playlists. This work is based on the idea that information extracted from social networks, such as Facebook and Last. fm, might be effectively exploited for personalization tasks. Indeed, information related to music preferences of users can be easily gathered from social platforms and used to define a model of user interests.
This paper presents IDL (Intelligent Digital Library), a prototypical digital library. It integrates machine learning tools and techniques in order to make effective, efficient and economically feasible the process of capturing the... more
This paper presents IDL (Intelligent Digital Library), a prototypical digital library. It integrates machine learning tools and techniques in order to make effective, efficient and economically feasible the process of capturing the information that should be stored and indexed by content in the digital library.
The COGITO 1 project aims at improving consumer-supplier relationships in future e-commerce interfaces featuring agents which can converse with users in written natural language (chatterbots) and extending their capabilities. In this... more
The COGITO 1 project aims at improving consumer-supplier relationships in future e-commerce interfaces featuring agents which can converse with users in written natural language (chatterbots) and extending their capabilities. In this paper we present the personalization component, developed in the COGITO system, that allows for the classification of users accessing an e-commerce web site through Machine Learning techniques.
The rapid evolution of Internet services has led to a constantly increasing number of Web sites and to an increase in the available information. The main challenge is to support Web users in order to facilitate navigation through Web... more
The rapid evolution of Internet services has led to a constantly increasing number of Web sites and to an increase in the available information. The main challenge is to support Web users in order to facilitate navigation through Web sites and to improve searching among the extremely large Web repository, such as digital libraries, online product catalogues, or other generic information sources. The complexity of today's services could be lowered by means of proactive support or advice from the system.
Abstract. The recent evolution of e-commerce has emphasized the need for services to be suitable to the needs of individual users: as a consequence, personalization has become an important strategy to improve access to relevant products.... more
Abstract. The recent evolution of e-commerce has emphasized the need for services to be suitable to the needs of individual users: as a consequence, personalization has become an important strategy to improve access to relevant products. This work presents a personalization process based on a text categorization method, which exploits the textual descriptions of the products in online catalogues, in order to discriminate between interesting and uninteresting items for the customer.
The exponential growth of the Web is the most influential factor that contributes to the increasing importance of text retrieval and filtering systems. Anyway, since information exists in many languages, users could also consider as... more
The exponential growth of the Web is the most influential factor that contributes to the increasing importance of text retrieval and filtering systems. Anyway, since information exists in many languages, users could also consider as relevant documents written in different languages from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems.
Abstract Recommenders systems are used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based ones recommend items similar to those a given user has liked in the past. Indeed, the past... more
Abstract Recommenders systems are used with various purposes, especially dealing with e-commerce and information filtering tools. Content-based ones recommend items similar to those a given user has liked in the past. Indeed, the past behavior is supposed to be a reliable indicator of her future behavior. This assumption, however, causes the overspecialization problem. Our purpose is to mitigate the problem stimulating users and facilitating the serendipitous encounters to happen.
This paper describes the possible use of advanced content-based recommendation methods in the area of Digital Libraries. Content-based recommenders analyze documents previously rated by a target user, and build a profile exploited to... more
This paper describes the possible use of advanced content-based recommendation methods in the area of Digital Libraries. Content-based recommenders analyze documents previously rated by a target user, and build a profile exploited to recommend new interesting documents. One of the main limitations of traditional keyword-based approaches is that they are unable to capture the semantics of the user interests, due to the natural language ambiguity.
Abstract The main contribution of this work is the design of an application framework based on both conversational agents and user profiling technologies for the development of e-commerce services. User profiles are exploited by... more
Abstract The main contribution of this work is the design of an application framework based on both conversational agents and user profiling technologies for the development of e-commerce services. User profiles are exploited by conversational agents to help customers in retrieving potentially interesting products from a catalogue. Three techniques were used for collecting data for a usability test: eye-movement tracking, questionnaire, and recording the user-system dialogue.
Abstract. This paper presents the integration of linguistic knowledge in learning semantic user profiles able to represent user interests in a more effective way with respect to classical keyword-based profiles 1. Semantic profiles are... more
Abstract. This paper presents the integration of linguistic knowledge in learning semantic user profiles able to represent user interests in a more effective way with respect to classical keyword-based profiles 1. Semantic profiles are obtained by integrating a naıve Bayes approach for text categorization with a word sense disambiguation strategy based on the WordNet lexical database (Section 2).
Basic content-based personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object. The Web 2.0 (r) evolution has changed the game for... more
Basic content-based personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, with the attributes of a content object. The Web 2.0 (r) evolution has changed the game for personalization, from 'elitary'Web 1.0, written by few and read by many, to web content generated by everyone (user-generated content-UGC), since the role of people has evolved from passive consumers of information to that of active contributors.
Abstract. This paper focuses on the problem of choosing a representation of documents that can be suitable to induce more advanced semantic user profiles, in which concepts are used instead of keywords to represent user interests. We... more
Abstract. This paper focuses on the problem of choosing a representation of documents that can be suitable to induce more advanced semantic user profiles, in which concepts are used instead of keywords to represent user interests. We propose a method which integrates a word sense disambiguation algorithm based on the WordNet IS-A hierarchy, with two machine learning techniques to induce semantic user profiles, namely a relevance feedback method and a probabilistic one.
Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem. In fact, a breakthrough in this field would have a significant impact on many relevant fields, such as information retrieval and information extraction. This... more
Word Sense Disambiguation (WSD) is traditionally considered an AI-hard problem. In fact, a breakthrough in this field would have a significant impact on many relevant fields, such as information retrieval and information extraction. This paper describes JIGSAW, a knowledge-based WSD algorithm that attemps to disambiguate all words in a text by exploiting WordNet senses. The main assumption is that a Part-Of-Speech (POS)-dependent strategy to WSD can turn out to be more effective than a unique strategy.
Abstract Exploring digital collections to find information relevant to a user's interests is a challenging task. Information preferences vary greatly across users; therefore, filtering systems must be highly personalized to serve the... more
Abstract Exploring digital collections to find information relevant to a user's interests is a challenging task. Information preferences vary greatly across users; therefore, filtering systems must be highly personalized to serve the individual interests of the user. Algorithms designed to solve this problem base their relevance computations on user profiles in which representations of the users' interests are maintained.
The World Wide Web is a vast repository of information, much of which is valuable but very often hidden to the user. Currently, Web personalization is the most promising approach to remedy this problem, and Web usage mining, is considered... more
The World Wide Web is a vast repository of information, much of which is valuable but very often hidden to the user. Currently, Web personalization is the most promising approach to remedy this problem, and Web usage mining, is considered a crucial component of any effective Web personalization system. Web usage mining techniques such as clustering and association rules, which rely on offline pattern discovery from user transactions, can be used to improve searching in the Web.
Abstract. The explosion of collaborative platforms we are recently witnessing, such as social networks, or video and photo sharing sites, radically changed the Web dynamics and the way people use and organize information. The use of tags,... more
Abstract. The explosion of collaborative platforms we are recently witnessing, such as social networks, or video and photo sharing sites, radically changed the Web dynamics and the way people use and organize information. The use of tags, keywords freely chosen by users for annotating resources, offers a new way for organizing and retrieving web resources that closely reflects the users' mental model and also allows the use of evolving vocabularies.
Web 2.0 introduced the remarkable phenomenon of usergenerated content. Large numbers of most popular sites on the Web are currently mainstream Web 2.0 applications with rich user-generated content. Wikipedia, YouTube, Flickr, and del.... more
Web 2.0 introduced the remarkable phenomenon of usergenerated content. Large numbers of most popular sites on the Web are currently mainstream Web 2.0 applications with rich user-generated content. Wikipedia, YouTube, Flickr, and del. icio. us are classical examples of those sites. Web 2.0 allows users to do more than just retrieving information, since it is based upon architecture of participation that reduces the barriers of online collaboration and encourages the generation and distribution of content.
Abstract The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages,... more
Abstract The exponential growth of the Web is the most influential factor that contributes to the increasing importance of cross-lingual text retrieval and filtering systems. Indeed, relevant information exists in different languages, thus users need to find documents in languages different from the one the query is formulated in. In this context, an emerging requirement is to sift through the increasing flood of multilingual text: this poses a renewed challenge for designing effective multilingual Information Filtering systems.
Abstract In the last years, hundreds of social networks sites have been launched with both professional (eg, LinkedIn) and non-professional (eg, MySpace, Facebook) orientations. This resulted in a renewed information overload problem, but... more
Abstract In the last years, hundreds of social networks sites have been launched with both professional (eg, LinkedIn) and non-professional (eg, MySpace, Facebook) orientations. This resulted in a renewed information overload problem, but it also provided a new and unforeseen way of gathering useful, accurate and constantly updated information about user interests and tastes.
Abstract This paper investigates the role of Distributional Semantic Models (DSMs) in Question Answering (QA), and specifically in a QA system called Question Cube. Question Cube is a framework for QA that combines several techniques to... more
Abstract This paper investigates the role of Distributional Semantic Models (DSMs) in Question Answering (QA), and specifically in a QA system called Question Cube. Question Cube is a framework for QA that combines several techniques to retrieve passages containing the exact answers for natural language questions. It exploits Information Retrieval models to seek candidate answers and Natural Language Processing algorithms for the analysis of questions and candidate answers both in English and Italian.
Abstract An electronic performance support system (EPSS) introduces challenges on contextualized and personalized information delivery. Recommender systems aim at delivering and suggesting relevant information according to users... more
Abstract An electronic performance support system (EPSS) introduces challenges on contextualized and personalized information delivery. Recommender systems aim at delivering and suggesting relevant information according to users preferences, thus EPSSs could take advantage of the recommendation algorithms that have the effect of guiding users in a large space of possible options. The JUMP project aims at integrating an EPSS with a hybrid recommender system.
Résumé/Abstract In the recent years, the Internet has experienced a rapid shift from information and entertainment to electronic commerce. The amount of information available on the web, as well as the number of e-businesses and web... more
Résumé/Abstract In the recent years, the Internet has experienced a rapid shift from information and entertainment to electronic commerce. The amount of information available on the web, as well as the number of e-businesses and web shoppers, is growing exponentially. Customers have to spend a lot of time to browse the net in order to find the information fitting their needs.
The chapter presents the SWAPTeam participation at the ECML/PKDD 2011-Discovery Challenge for the task on the cold start problem focused on making recommendations for new video lectures. The developed solution uses a content-based... more
The chapter presents the SWAPTeam participation at the ECML/PKDD 2011-Discovery Challenge for the task on the cold start problem focused on making recommendations for new video lectures. The developed solution uses a content-based approach because it is less sensitive to the cold start problem that is commonly associated with pure collaborative filtering recommenders. The Challenge organizers encouraged solutions that can actually affect VideoLecture.
Abstract The rapid growth of the so-called Web 2.0 has changed the surfers' behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing... more
Abstract The rapid growth of the so-called Web 2.0 has changed the surfers' behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing resources, spread as a model for browsing and retrieving web contents.
Abstract—Cultural heritage personalization and Web 2.0 joint research efforts have recently emerged in the attempt to build social and collaborative approaches to solve the problem of filtering content in the context of art museums. One... more
Abstract—Cultural heritage personalization and Web 2.0 joint research efforts have recently emerged in the attempt to build social and collaborative approaches to solve the problem of filtering content in the context of art museums. One way to tackle the problem of recommending artifacts to visitors is to take into account not only the official textual descriptions, but also the user-generated content, namely the tags, which visitors could use to freely annotate relevant works.
The Social Web, or the so called Web 2.0, is growing daily by the number of users and applications. In this way, a significant part of newly generated Web content and traffic is created by the users itself. They create, connect, comment,... more
The Social Web, or the so called Web 2.0, is growing daily by the number of users and applications. In this way, a significant part of newly generated Web content and traffic is created by the users itself. They create, connect, comment, tag, rate, remix, upload, download, new or existing resources in an architecture of participation, where user contribution and interaction adds value.
In the Internet era, huge amounts of data are available to everybody, in every place and at any moment. Searching for relevant information can be overwhelming, thus contributing to the user's sense of information overload. Building... more
In the Internet era, huge amounts of data are available to everybody, in every place and at any moment. Searching for relevant information can be overwhelming, thus contributing to the user's sense of information overload. Building systems for assisting users in this task is often complicated by the difficulty in articulating user interests in a structured form–a profile–to be used for searching. Machine learning methods offer a promising approach to solve this problem.
Abstract. Nowadays, the amount of available information, especially on the Web and in Digital Libraries, is increasing over time. In this context, the role of user modeling and personalized information access is increasing. This paper... more
Abstract. Nowadays, the amount of available information, especially on the Web and in Digital Libraries, is increasing over time. In this context, the role of user modeling and personalized information access is increasing. This paper focuses on the problem of choosing a representation of documents that can be suitable to induce concept-based user profiles as well as to support a content-based retrieval process.