436 research outputs found
SASWeb 2012: Semantic and Adaptive Social Web
No full textSASWeb 2012: Semantic and Adaptive Social Web
organized by Lora Aroyo, Federica Cena, Antonina Dattolo, Pasquale Lops, Julita Vassileva
(1) Building multi-layer social knowledge maps with Google Maps API
MinEr Liang, Julio Guerra, Peter Brusilovsky
(2) Learning from a network of peers via peer-driven adjustment of a corpus
John Champaign, Robin Cohen
****Invited Talks
(4) Culture in User Modeling 3.0
Jacqueline Bourdeau
(5) Leveraging social and semantic components in adaptive environments
Cristina Gena
(6) Meaning is its use: towards the use of distributional semantics for content-based recommender systems
Cataldo Musto
(7) Exploring folksonomies for adaptive query expansion
Fabio Gasparett
Leveraging Social Media Sources to Generate Personalized Music Playlists
No full textThis 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. The use of social media is a very cheap and effective way to overcome the classical cold start problem of recommender systems.
In this work we enriched social media-based playlists with new artists related to those the user already likes. Specically, we compare two different enrichment techniques: the first leverages the knowledge stored on DBpedia, the structured version of Wikipedia, while the second is based on the content-based similarity between descriptions of artists. The final playlist is ranked and finally presented to the user that can listen
to the songs and express her feedbacks. A prototype version of MyMusic was made available online in order to carry out a preliminary user study to evaluate the best enrichment strategy. The preliminary results encouraged keeping on this research
Recensioni e letture
No full textStolova Natalya, Cognitive Linguistics and Lexical Change. Motion Verbs from Latin to Romance (Alfonsina Buoniconto) – Juliana Goschler, Anatol Stefanowitsch (eds.), Variation and Change in the Encoding of Motion Events (Noemi De Pasquale) – Paola Di Gennaro, Wandering through Guilt: the Cain Archetype in Twentieth Century Novel (Marina Lops
E-Commerce and Web Technologies - 14th International Conference, EC-Web 2013, Prague, Czech Republic, August 27-28, 2013
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E-Commerce and Web Technologies, 13th International Conference, EC-Web 2012, Vienna, Austria, September 2012, Proceedings
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Exploiting Big Data for Enhanced Representations in Content-Based Recommender Systems
No full textThe recent explosion of Big Data is offering new chances and challenges to all those platforms that provide personalized access to information sources, such as recommender systems and personalized search
engines. In this context, social networks are gaining more and more interests since they represent a perfect source to trigger personalization tasks.
Indeed, users naturally leave on these platforms a lot of data about their preferences, feelings, and friendships. Hence, those data are really valuable for addressing the cold start problem of recommender systems. On
the other hand, since content shared on social networks is noisy and heterogeneous, information extracted must be hardly processed to build
user profiles that can effectively mirror user interests and needs.
In this paper we investigated the effectiveness of external knowledge derived from Wikipedia in representing both documents and user profiles
in a recommendation scenario. Specifically, we compared a classical keyword-based representation with two techniques that are able to
map unstructured text with Wikipedia pages. The advantage of using this representation is that documents and user profiles become richer, more human-readable, less noisy, and potentially connected to the Linked Open Data (LOD) cloud. The goal of our preliminary experimental evaluation was twofolds: 1) to define the representation that best reflects user preferences; 2) to define the representation that provides the best predictive accuracy.
We implemented a news recommender for a preliminary evaluation of our model. We involved more than 50 Facebook and Twitter users and we demonstrated that the encyclopedic-based representation is an effective way for modeling both user profiles and documents
Power to the patients: The HealthNet social network
No full textHealthNet (HN) is a social network that brings together patients with similar health conditions. HN helps users in finding a solution to their health problems by suggesting doctors and health facilities that best fit the patient profile. Indeed, the core component of HN is a recommender system that suggests patients similar to the target user and supports the choice of the doctor and the hospital for a specific condition. The recommendation algorithm first computes similarities among patients, and then generates a ranked list of doctors and hospitals for a given patient profile by exploiting health data shared by the community. The HN typical user can find the most similar patients, can look how they treated their diseases, and can receive suggestions for solving her condition. In order to facilitate the interaction with the system and improve the recommendation step, the patient can express her health status by a natural-language sentence. The system analyzes the sentence and identifies the most relevant medical area (e.g., orthopedics, neurology, allergology, etc.) for that specific case, and uses this information for the recommendation task. Currently HN is in alpha version and only for Italian users, but in the future we want to extend the platform to other languages. We carried out both an in-vitro experimental evaluation to assess the effectiveness of the module for analyzing natural language descriptions provided by users as well as the recommender system to suggest the right doctors for a specific health problem, and an in-vivo evaluation performed by real doctors. Results are really encouraging
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