Search results for "Recommender systems"

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 …

Tīmekļa vietņu un interneta veikalu personalizēšanas iespējas, problēmas un risinājumi

2015

Maģistra darbs ir veltīts tīmekļa vietņu un interneta veikalu personalizēšanai, tas ir, satura pielāgošana lietotājiem un produktu rekomendācijas. Darbā ir apskatīta personalizēšanas teorija un rekomendāciju algoritmi, salīdzināti populārākie satura personalizēšanas servisi un veikta produktu rekomendāciju servisu analīze. Autors cenšas atrisināt rekomendācijas algoritma pielietošanas problēmu interneta veikalā un piedāvā iespējamo risinājumu. Beigās tiek salīdzināts piedāvātais risinājums ar pieejamiem servisiem.

Challenges of Serendipity in Recommender Systems

2016

Most recommender systems suggest items similar to a user profile, which results in boring recommendations limited by user preferences indicated in the system. To overcome this problem, recommender systems should suggest serendipitous items, which is a challenging task, as it is unclear what makes items serendipitous to a user and how to measure serendipity. The concept is difficult to investigate, as serendipity includes an emotional dimension and serendipitous encounters are very rare. In this paper, we discuss mentioned challenges, review definitions of serendipity and serendipity-oriented evaluation metrics. The goal of the paper is to guide and inspire future efforts on serendipity in r…

Comparing ranking-based collaborative filtering algorithms to a rating-based alternative in recommender systems context

2017

Suuri sisältövalikoima eri internet palveluissa, kuten verkkokaupoissa, voi aiheuttaa liian suurta informaatiomäärää, mikä heikentää asiakaskokemusta. Suosittelujärjestelmät ovat teknologioita, jotka tukevat asiakkaan päätöksentekoa tarjoamalla ennustettuja suosituksia. On yleistä, että asiakkaalle näytetään lista tuotteista, joista asiakas voisi pitää, esimerkiksi top-10 lista elokuvista. Perinteisesti nämä listat ovat tuotettu käyttäen perinteistä arvosanapohjaista menetelmää, missä tuntemattomille tuotteille ennustetaan arvosana ja järjestetty lista muodostetaan arvosanojen perusteella. Sijoitusperusteinen lähestyminen laskee käyttäjien väliset samankaltaisuudet ja ennustaa järjestetyn l…

Listwise Collaborative Filtering

2015

Recently, ranking-oriented collaborative filtering (CF) algorithms have achieved great success in recommender systems. They obtained state-of-the-art performances by estimating a preference ranking of items for each user rather than estimating the absolute ratings on unrated items (as conventional rating-oriented CF algorithms do). In this paper, we propose a new ranking-oriented CF algorithm, called ListCF. Following the memory-based CF framework, ListCF directly predicts a total order of items for each user based on similar users' probability distributions over permutations of the items, and thus differs from previous ranking-oriented memory-based CF algorithms that focus on predicting th…

Social Collaborative Viewpoint Regression with Explainable Recommendations

2017

A recommendation is called explainable if it not only predicts a numerical rating for an item, but also generates explanations for users' preferences. Most existing methods for explainable recommendation apply topic models to analyze user reviews to provide descriptions along with the recommendations they produce. So far, such methods have neglected user opinions and influences from social relations as a source of information for recommendations, even though these are known to improve the rating prediction. In this paper, we propose a latent variable model, called social collaborative viewpoint regression (sCVR), for predicting item ratings based on user opinions and social relations. To th…

A Context-Aware Mobile Solution for Assisting Tourists in a Smart Environment

2017

Semantically-enhanced advertisement recommender systems in social networks

2017

El objetivo principal de la investigación es estudiar y diseñar un entorno de recomendación publicitaria en las redes sociales que puede ser enriquecido mediante tecnologías semánticas. A pesar de que existen muchas aplicaciones y soluciones para los sistemas de recomendación, en este estudio se diseña un framework robusto con un rendimiento adecuado para poder ser implementado en las redes sociales con el objetivo de ampliar los propósitos de negocio. De este objetivo principal se pueden derivar los siguientes objetivos secundarios: 1. Superar las limitaciones iniciales de los métodos clásicos de recomendación. 2. Aumentar la calidad y precisión de las recomendaciones y el rendimiento del …

Cross-Domain Recommendations with Overlapping Items

2016

In recent years, there has been an increasing interest in cross-domain recommender systems. However, most existing works focus on the situation when only users or users and items overlap in different domains. In this paper, we investigate whether the source domain can boost the recommendation performance in the target domain when only items overlap. Due to the lack of publicly available datasets, we collect a dataset from two domains related to music, involving both the users’ rating scores and the description of the items. We then conduct experiments using collaborative filtering and content-based filtering approaches for validation purpose. According to our experimental results, the sourc…