1,720,967 research outputs found
Text Categorization with Modified LSI
No full textAutomatic Text Categorization (TC) is a complex and useful task for many naturallanguage applications, and is usually performed through the use of a set of manuallyclassified documents, a training collection. Term-based representation of documents hasfound widespread use in TC. However, one of the main shortcomings of such methods isthat they largely disregard lexical semantics and, as a consequence, are not sufficientelyrobust with respect to variations in word usage. We shall design, implement, and evaluatea new, text classification algorithm. Our main idea is to find a series of projections ofthe training data by using a new modifided LSI algorithm, project all training instancesto the low-dimensional subspace found in the previous step, induce a binary search onthe projected low-dimensional data. Our conclusion is that, with all its simplicity andefficiency, our approach is comparable (and sometimes superior) to SVM in terms ofaccurac
Social Semantic Query Expansion
No full textWeak semantic techniques rely on the integration of Semantic Web techniques with social annotations, and aim to embrace the strengths of both of them. In this paper, 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 in-depth experimental evaluation performed on both artificial datasets and real users show that our approach outperforms traditional techniques, such as relevance feedback and per- sonalized 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 compu- tational costs, we also provide a complexity analysis of our system, in order to show its capability to operate in real time
Knowledge retrieval and personalization in virtual enterprises
No full textEach business company collects, produces and exploits for its activities and goals large amounts of information. Most of the times this knowledge makes the intellectual capital for creating value and innovation. Knowledge management (KM) systems aim at manipulating knowledge by storing and redistributing corporate information that are acquired from the organization's members. In this context, Virtual Enterprises (VE) plays a crucial role as not permanent alliances of enterprises joined together to share resources and skills in order to better respond to business opportunities. The representation and retrieval of distributed knowledge is an important feature that information systems must provide in order to obtain advantages from this kind of enterprises. KEEN 1 (Knowledge-based Extended Enterprise) is a research project for developing a system able to extract and let different business companies access to collective knowledge required to achieve particular shared goals. In this paper, we report the most important features of this system, especially in the context of distributed knowledge representation and retrieval
Personalization in Virtual Enterprises
No full textEach business company collects, produces and exploits for its activities and goals large amounts of infor-mation. Most of the times this knowledge makes the intellectual capital for creating value and innovation. Knowledge management (KM) systems aim at manipulating knowledge by storing and redistributing corporate information that are acquired from the organizations members. In this context, Virtual Enterprises (VE) plays a crucial role as not permanent alliances of enterprises joined together to share resources and skills in order to better respond to business opportunities. The representation and retrieval of distributed knowledge is an important feature that information systems must provide in order to obtain advantages from this kind of enterprises. PVE (Personalized Virtual Enterprise) is an ongoing research project for developing a system able to extract and let different business companies access to collective knowledge required to achieve particular shared goals. In this paper, we report the most important features of this system,especially in the context of distributed knowledge representation and retrieval
SocialSearch - A Social Platform for Web 2.0 Search
No full textIn the last decade, social bookmarking services have gained popularity as a way of annotating and categoriz- ing a variety of different web resources. The idea behind this work is to exploit such services for enhancing traditional query expansion techniques. Specifically, the system we propose relies on three-dimensional co- occurrence matrices, where the further dimension is introduced to represent categories of terms sharing the same semantic property. Such categories, named semantic classes, are related to the folksonomy mined from social bookmarking services such as Delicious, Digg, and StumbleUpon. The paper illustrates a comparative experimental evaluation on real datasets, such as the one collected by the Open Directory Project and the TREC 2004. We also include the results of a specific disambiguation analysis aimed to evaluate the effective- ness of our approach in comparison with state-of-the-art techniques when satisfying queries characterized by polysemic and ambiguous terms
Wavelet-based Music Recommendation
No full textRecommender Systems provide suggestions for items (e.g., movies or songs) to be of use to a user. They must take into account information to deliver more useful (perceived) recommendations. Current music recommender takes an initial input of a song and plays music with similar characteristics, or music that other users have listened to along with the input song. Listening behaviors in terms of temporal information associated to ratings or playbacks are usually ignored. We propose a recommender that predicts the most rated songs that a given user is likely to play in the future analyzing and comparing user listening habits by means of signal processing techniques
A social semantic approach to adaptive query expansion
No full textClassic query expansion approaches are based on the use of two-dimensional co-occurrence matrices. In this paper, we propose the adoption 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 in-depth experimental evaluation performed on real users show that our approach outperforms traditional techniques, so confirming the validity and usefulness of the categorization of the user needs and preferences in semantic classes
An Approach to Social Recommendation for Context-Aware Mobile Services
No full textNowadays, 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 paper 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 user’s 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
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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