81 research outputs found

    Personalization in cultural heritage: the road travelled and the one ahead

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    Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed

    The User and the Digital Library

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    Presentation discusses the designing of Digital library as par the user expectations. Presentation discusses issues like Query formation, Document Matching, Ranking of search results and presentability

    Studying, developing, and experimenting contextual advertising systems

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    The World Wide Web has grown so fast in the last decade and it is today a vital daily part of people. The Internet is used for many purposes by an ever growing number of users, mostly for daily activities, tasks, and services. To face the needs of users, an efficient and effective access to information is required. To deal with this task, the adoption of Information Retrieval and Information Filtering techniques is continuously growing. Information Re-trieval (IR) is the field concerned with searching for documents, information within documents, and metadata about documents, as well as searching for structured storage, relational databases, and the World Wide Web. Infor- mation Filtering deals with the problem of selecting relevant information for a given user, according to her/his preferences and interest. Nowadays, Web advertising is one of the major sources of income for a large number of websites. Its main goal is to suggest products and services to the still ever growing population of Internet users. Web advertising is aimed at suggesting products and services to the users. A significant part of Web ad-vertising consists of textual ads, the ubiquitous short text messages usually marked as sponsored links. There are two primary channels for distributing ads: Sponsored Search (or Paid Search Advertising) and Contextual Ad-vertising (or Content Match). Sponsored Search advertising is the task of displaying ads on the page returned from a Web search engine following a query. Contextual Advertising (CA) displays ads within the content of a generic, third party, webpage. In this thesis I study, develop, and evaluated novel solutions in the field of Contextual Advertising. In particular, I studied and developed novel text summarization techniques, I adopted a novel semantic approach, I studied and adopted collaborative approaches, I started a conjunct study of Contex-tual Advertising and Geo-Localization, and I study the task of advertising in the field of Multi-Modal Aggregation. The thesis is organized as follows. In Chapter 1, we briefly describe the main aspects of Information Retrieval. Following, the Chapter 2 shows the problem of Contextual Advertising and describes the main contributes of the literature. Chapter 3 sketches a typical adopted approach and the eval-uation metrics of a Contextual Advertising system. Chapter 4 is related to the syntactic aspects, and its focus is on text summarization. In Chapter 5 the semantic aspects are taken into account, and a novel approach based on ConceptNet is proposed. Chapter 6 proposes a novel view of CA by the adoption of a collaborative filtering approach. Chapter 7 shows a prelim-inary study of Geo Location, performed in collaboration with the Yahoo! Research center in Barcelona. The target is to study several techniques of suggesting localized advertising in the field of mobile applications and search engines. In Chapter 8 is shown a joint work with the RAI Centre for Research and Technological Innovation. The main goal is to study and propose a system of advertising for Multimodal Aggregation data. Chapter 9 ends this work with conclusions and future directions

    Temporal rating habits: A valuable tool for rating discrimination

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    This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in CAMRa '11 Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, http://dx.doi.org/10.1145/2096112.2096118.In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) to tackle the Identifying Ratings (track 2) task of the CAMRa 2011 Challenge. The experiments performed include time-frequency probabilistic strategies, heuristic collaborative filtering (CF) and a model-based CF approach. Results show that probabilistic classifiers based on temporal behavior of users have better performance than traditional recommendation-based strategies, thus reflecting that temporal information is a valuable source for the identification or discrimination of user ratings.This work is supported by the Spanish Government (TIN 2008-06566-C04-02) and by the Comunidad de Madrid and Universidad Aut´onoma de Madrid (CCG10-UAM/TIC-5877). The authors acknowledge support from CCC at UAM

    WebTailor: Internet Service for Salient and Automatic User Interest Profiles

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    Website personalization systems seek to give users unique, tailored content and experiences on the Internet. A key feature of these systems is a user profile that represents each user in a way that distinguishes them from others. In current personalization systems, the data used to create these profiles is extremely limited, which leads to a host of problems and ineffectual personalization. The main goal of this thesis is to improve these personalization systems by addressing their lack of data and its poor quality, breadth, and depth. This is accomplished by analyzing and classifying the content of each user\u27s Internet browsing activity, rather than just their activity on a single website, to autonomously build persistent, ontology-based user profiles. Furthermore, these profiles are built and stored on a remote server, which allows them to be easily made available to approved websites in the interest of providing the data to enable accurate, relevant, and up-to-date personalization

    Towards trust-aware recommendations in social networks

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    Recommender systems have been strongly researched within the last decade. With the emergence and popularization of social networks a new fi eld has been opened for social recommendations. Introducing new concepts such as trust and considering the network topology are some of the new strategies that recommender systems have to take into account in order to adapt their techniques to these new scenarios. In this thesis a simple model for recommendations on twitter is developed to apply some of the known techniques and explore how well the state of the art does in a real scenario. The thesis can serve as a basis for further social recommender system research

    An Intelligent Technique for Extracting Subjects from User Profile Using ODP Ontology-Driven Reasoning

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    Abstract: Nowadays, the amount of available information, especially on the Web, is increasing. In this field, the role of user modeling and personalized information access is obviously vital. The traditional techniques like BOW (Bags of words) limit recommendations to the words which have been stored in the profile. In other words, the news items, which semantically relate to the users interests, can't be recognized and recommended to the users. Besides, BOW technique suffers from the curse of dimensionality, thus computational burden reduction is an essential task to efficiently handle a large number of terms in practical applications. This study 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. In this study, a new approach has been proposed to construct a ranked semantic user profile through extracting the related subjects. The new items can be recommended through collecting information from the user's selections, based on existing domain ontology ODP. The efficiency of the proposed technique has been shown by embedding it into an intelligent aggregator, RSS (RSS is acronym of " Really Simple Syndication) feed reader, which has been trained and evaluated by different and heterogeneous users. The results in experimental session show that the incoming news item which semantically relates to the profile gets highly recommended to the user despite its excluding of common words in the profile

    Recommender systems in industrial contexts

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    This thesis consists of four parts: - An analysis of the core functions and the prerequisites for recommender systems in an industrial context: we identify four core functions for recommendation systems: Help do Decide, Help to Compare, Help to Explore, Help to Discover. The implementation of these functions has implications for the choices at the heart of algorithmic recommender systems. - A state of the art, which deals with the main techniques used in automated recommendation system: the two most commonly used algorithmic methods, the K-Nearest-Neighbor methods (KNN) and the fast factorization methods are detailed. The state of the art presents also purely content-based methods, hybridization techniques, and the classical performance metrics used to evaluate the recommender systems. This state of the art then gives an overview of several systems, both from academia and industry (Amazon, Google ...). - An analysis of the performances and implications of a recommendation system developed during this thesis: this system, Reperio, is a hybrid recommender engine using KNN methods. We study the performance of the KNN methods, including the impact of similarity functions used. Then we study the performance of the KNN method in critical uses cases in cold start situation. - A methodology for analyzing the performance of recommender systems in industrial context: this methodology assesses the added value of algorithmic strategies and recommendation systems according to its core functions.Comment: version 3.30, May 201

    On the equilibrium of query reformulation and document retrieval

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    In this paper, we study jointly query reformulation and document relevance estimation, the two essential aspects of information retrieval (IR). Their interactions are modelled as a two-player strategic game: one player, a query formulator, taking actions to produce the optimal query, is expected to maximize its own utility with respect to the relevance estimation of documents produced by the other player, a retrieval modeler; simultaneously, the retrieval modeler, taking actions to produce the document relevance scores, needs to optimize its likelihood from the training data with respect to the refined query produced by the query formulator. Their equilibrium or equilibria will be reached when both are the best responses to each other. We derive our equilibrium theory of IR using normal-form representations: when a standard relevance feedback algorithm is coupled with a retrieval model, they would share the same objective function and thus form a partnership game; by contrast, pseudo relevance feedback pursues a rather different objective than that of retrieval models, therefore the interaction between them would lead to a general-sum game (though implicitly collaborative). Our game-theoretical analyses not only yield useful insights into the two major aspects of IR, but also offer new practical algorithms for achieving the equilibrium state of retrieval which have been shown to bring consistent performance improvements in both text retrieval and item recommendation
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