Search results for "Recommender systems"
showing 10 items of 23 documents
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 …
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…
The seven layers of complexity of recommender systems for children in educational contexts
2019
Recommender systems (RS) in their majority focus on an average target user: adults. We argue that for non-traditional populations in specific contexts, the task is not as straightforward–we must look beyond existing recommendation algorithms, premises for interface design, and standard evaluation metrics and frameworks. We explore the complexity of RS in an educational context for which young children are the target audience. The aim of this position paper is to spell out, label, and organize the specific layers of complexity observed in this context.
Serendipity in recommender systems
2018
The number of goods and services (such as accommodation or music streaming) offered by e-commerce websites does not allow users to examine all the available options in a reasonable amount of time. Recommender systems are auxiliary systems designed to help users find interesting goods or services (items) on a website when the number of available items is overwhelming. Traditionally, recommender systems have been optimized for accuracy, which indicates how often a user consumed the items recommended by system. To increase accuracy, recommender systems often suggest items that are popular and suitably similar to items these users have consumed in the past. As a result, users often lose interest…
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…
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.
The contribution of AI to enhance understanding of Cultural Heritage.
2013
The Artificial Intelligence & Cultural Heritage (AI & CH) working group was born in 1999 with the aim at promoting various scientific activities to increase a more active collaboration between the sectors of cultural assets and artificial intelligence. The many events (workshops and schools) organized over the years have shown the validity of this group for exchanging ideas and gathering researchers and practitioners from different fields. New applications of informatics and artificial intelligence have provided the opportunity to produce innovative tools for documenting, managing and communicating cultural heritage. For this anniversary we intend to show how some of the most important meth…
Investigating serendipity in recommender systems based on real user feedback
2018
Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-addressed question. According to the most common definition, serendipity consists of three components: relevance, novelty and unexpectedness, where each component has multiple variations. In this paper, we looked at eight different definitions of serendipity and asked users how they perceived them in the context of movie recommendations. We surveyed 475 users of the movie recommender system, MovieLens regarding 2146 movies in total and compared tho…
A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion
2015
Collaborative Filtering (CF) is one of the most successful algorithms in recommender systems. However, it suffers from data sparsity and scalability problems. Although many clustering techniques have been incorporated to alleviate these two problems, most of them fail to achieve further significant improvement in recommendation accuracy. First of all, most of them assume each user or item belongs to a single cluster. Since usually users can hold multiple interests and items may belong to multiple categories, it is more reasonable to assume that users and items can join multiple clusters (groups), where each cluster is a subset of like-minded users and items they prefer. Furthermore, most of…
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…