Search results for "recommender systems"
showing 10 items of 23 documents
Semantically-enhanced advertisement recommender systems in social networks
2016
El suministro de recomendaciones en los sistemas sociales lleva ya algún tiempo en el punto de mira tanto de los académicos como de la industria. Los gigantes de las redes sociales como Facebook, LinkedIn, Myspace, etc., están ansiosos por encontrar la bala de plata de la recomendación. Estas aplicaciones permiten a los clientes dar forma a unas determinadas redes sociales a través de sus comunicaciones sociales cooperativas cotidianas. Mientras tanto, la experiencia online actual depende progresivamente de la asociación social. Una de las principales preocupaciones en la red social es establecer un plan de negocio exitoso para obtener más beneficios de la red social. Hacer un negocio en ca…
Ranking-Oriented Collaborative Filtering: A Listwise Approach
2016
Collaborative filtering (CF) is one of the most effective techniques in recommender systems, which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms demonstrated significant performance gains in terms of ranking accuracy, being able to estimate a precise preference ranking of items for each user rather than the absolute ratings (as rating-oriented CF algorithms do). Conventional memory-based ranking-oriented CF can be referred to as pairwise algorithms. They represent each user as a set of preferences on each pair of items for similarity calculations and predictions. In this study, we propose ListCF, a novel listwise CF paradigm that seeks improvement in bot…
Semantic technologies for industry: From knowledge modeling and integration to intelligent applications
2013
Artificial Intelligence technologies are growingly used within several software systems ranging from Web services to mobile applications. It is by no doubt true that the more AI algorithms and methods are used the more they tend to depart from a pure "AI" spirit and end to refer to the sphere of standard software. In a sense, AI seems strongly connected with ideas, methods and tools that are not (yet) used by the general public. On the contrary, a more realistic view of it would be a rich and pervading set of successful paradigms and approaches. Industry is currently perceiving semantic technologies as a key contribution of AI to innovation. In this paper a survey of current industrial expe…
Watch This! The Influence of Recommender Systems and Social Factors on the Content Choices of Streaming Video on Demand Consumers
2021
Streaming Video-on-demand (SVOD) services are getting increasingly popular. Current research, however, lacks knowledge about consumers’ content decision processes and their respective influencing factors. Thus, the work reported on in this paper explores socio-technical interrelations of factors impacting content choices in SVOD, examining the social factors WOM, eWOM and peer mediation, as well as the technological influence of recommender systems. A research model based on the Theory of Reasoned Action and the Technology Acceptance Model was created and tested by an n = 186 study sample. Results show that the quality of a recommender system and not the social mapping functionality is the …
A Context-Aware Mobile Solution for Assisting Tourists in a Smart Environment
2017
A Hybrid Recommender System for Cultural Heritage Promotion
2021
Assisting users during their cultural trips is paramount in promoting the heritage of a territory. Recommender Systems offer the automatic tools to guide users in their decision process, by maximizing the adherence of the proposed contents with the particular preferences of every single user. However, traditional recommendation paradigms suffer from several drawbacks which are exacerbated in Cultural Heritage scenarios, due to the extremely wide range of users behaviors, which may also depend on their different educational backgrounds. In this paper, we propose a Hybrid recommender system which combines the four most common recommendation paradigms, namely collaborative filtering, popularit…
CitySearcher: A City Search Engine For Interests
2017
We introduce CitySearcher, a vertical search engine that searches for cities when queried for an interest. Generally in search engines, utilization of semantics between words is favorable for performance improvement. Even though ambiguous query words have multiple semantic meanings, search engines can return diversified results to satisfy different users' information needs. But for CitySearcher, mismatched semantic relationships can lead to extremely unsatisfactory results. For example, the city Sale would incorrectly rank high for the interest shopping because of semantic interpretations of the words. Thus in our system, the main challenge is to eliminate the mismatched semantic relationsh…
Kolaboratīvā filtrēšana ieteikumu sistēmās
2021
Darbs bija veltīts kolaboratīvai filtrēšanai ieteikumu sistēmās. Tika raksturota kolaboratīvās filtrēšanas metode, apskatīti galvēnie izaicinājumi, piemērām, datu nepietiekamība, mērogojamība u.c.. Sīkāk tika apskatīta uz atmiņu balstītas kolaboratīvās filtrēšanas metodes, uz modeļiem balstītas kolaboratīvās filtrēšanas metodes, hibrīdas kolaboratīvās filtrēšanas metodes un kolaboratīvās filtrēšanas novērtēšanas metrika. Praktiski tika apskatīts datu piemērs ar uz saturu balstītiem ieteikumiem un uz atmiņu balstītam kolaboratīvās filtrēšanas metodēm.
Information indexing and recommendation : toward a precise description if items by an ontological approach based on business domain modeling : applic…
2015
Effective management of large amounts of information has become a challenge increasinglyimportant for information systems. Everyday, new information sources emerge on the web. Someonecan easily find what he wants if (s)he seeks an article, a video or a specific artist. However,it becomes quite difficult, even impossible, to have an exploratory approach to discover newcontent. Recommender systems are software tools that aim to assist humans to deal withinformation overload. The work presented in this Phd thesis proposes an architecture for efficientrecommendation of news. In this document, we propose an architecture for efficient recommendationof news articles. Our ontological approach relie…
AN ONTOLOGY-BASED APPROACH TO PROVIDE PERSONNALIZED RECOMMENDATIONS USING A STOCHASTIC ALGORITHM
2011
International audience; The use of personalized recommender systems to assist users in the selection of products is becoming more and more popular and wide-spread. The purpose of a recommender system is to provide the most suitable items from an knowledge base, according the user knowledge, tastes, interests, ... These items are generally proposed as ordered lists. In this article, we propose to combine works from adaptive hypermedia systems, semantic web and combinatory to create a new kind of recommender systems suggesting combinations of items corresponding to the user.