Search results for "Recommender system"
showing 10 items of 70 documents
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…
A Sentiment Enhanced Deep Collaborative Filtering Recommender System
2021
Recommender systems use advanced analytic and learning techniques to select relevant information from massive data and inform users’ smart decision-making on their daily needs. Numerous works exploiting user’s sentiments on products to enhance recommendations have been introduced. However, there has been relatively less work exploring higher-order user-item features interactions for sentiment enhanced recommender system. In this paper, a novel Sentiment Enhanced Deep Collaborative Filtering Recommender System (SE-DCF) is developed. The architecture is based on a Neural Attention network component aggregated with the output predictions of a Convolution Neural Network (CNN) recommender. Speci…
Exploring learning analytics on YouTube: a tool to support students interactions analysis
2021
YouTube is a free online video-sharing platform that is often used by students for their learning activities. The interactions of the students when using the platform to shape new concepts, are worth to be investigated to better understand and to optimize the learning opportunities that take place in this platform. In this paper, we investigate which types of data are relevant to analyse the interactions of students with content on YouTube, and we introduce a new tool that emulates students’ interactions with the platform in order to provide data to be used in supporting Learning Analytics approaches. Our preliminary study inspects the tool effectiveness in data collection and analyses the …
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…
Web Personalization : The State of the Art and Future Avenues for Research and Practice
2016
Provide a review of current developments in web personalization.Show evolution toward more complex contextualized approaches.Identify research gaps in web personalization and in six specific research streams.Propose web personalization to be complemented with interpolated approaches. Although web personalization has been examined by earlier literature reviews, an updated analysis of recent advances in the field is needed. The authors extend prior reviews of web personalization by discussing current areas of interest, research gaps and future directions. A literature review of the top 20 marketing and information systems journals published during the period of 2005-2015 (May) shows active re…
Emotions and Activity Recognition System Using Wearable Device Sensors
2021
Nowadays machines have become extremely smart, there are a lot of existing services that seemed to be unexpectable and futuristic decades or even a few years ago. However, artificial intelligence is still far from human intelligence, machines do not have feelings, consciousness, and intuition. How can we help machines to learn about human feelings and understand their needs better? People take their devices wherever they go, what can devices tell us about their owners? Personal preferences and needs are dependent on emotional and situational contexts. Therefore, emotional and activity aware gadgets would be more intuitive and provide more appropriate information to users. Contemporary weara…
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…