An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

The Importance of Context When Recommending TV Content: Dataset and Algorithms

Home entertainment systems feature in a variety of usage scenarios with one or more simultaneous users, for whom the complexity of choosing media to consume has increased rapidly over the last decade. Users' decision processes are complex and highly influenced by contextual settings, but data supporting the development and evaluation of context-aware recommender systems are scarce. In this paper we present a dataset of self-reported TV consumption enriched with contextual information of viewing situations. We show how choice of genre associates with, among others, the number of present users and users' attention levels. Furthermore, we evaluate the performance of predicting chosen genres given different configurations of contextual information, and compare the results to contextless predictions. The results suggest that including contextual features in the prediction cause notable improvements, and both temporal and social context show significant contributions

User interface patterns in recommendation-empowered content intensive multimedia applications

Design Patterns (DPs) are acknowledged as powerful conceptual tools to improve design quality and to reduce time and cost of the development process by effect of the reuse of “good” design solutions. In many fields (e.g., software engineering, web engineering, interface design) patterns are widely used by practitioners and are also investigated from a research perspective. Still, they have been seldom explored in the arena of Recommender Systems (RSs). RSs provide suggestions (“recommendations”) for items that are likely to be appropriate for the user profile, and are increasingly adopted in content-intensive multimedia applications to complement traditional forms of search in large information spaces. This paper explores RSs through the lens of User Interface (UI) Design Patterns. We have performed a systematic analysis of 54 recommendation-empowered content-intensive multimedia applications, in order to: (i) discover the occurrences of existing domain independent UI patterns; (ii) identify frequently adopted UI solutions that are not modelled by existing patterns, and define a set of new UI patterns, some of which are specific of the interfaces for recommendation features while others can be useful also in a broader context. The results of our inspection have been discussed with and evaluated by a team of experts, leading to a consolidated set of 14 new patterns that are reported in the paper. Reusing pattern-based design solutions instead of building new solutions from scratch enables novice and expert designers to build good UIs for Recommendation-empowered content intensive multimedia applications more effectively, and ultimately can improve the UX experience in this class of systems. From a broader perspective, our work can stimulate future research bridging Recommender Systems, Web Engineering and Interface Design by means of Design Patterns, and highlights new research directions also discussed in the paper

Using implicit feedback for recommender systems: characteristics, applications, and challenges

Recommender systems are software tools to tackle the problem of information overload by helping users to find items that are most relevant for them within an often unmanageable set of choices. To create these personalized recommendations for a user, the algorithmic task of a recommender system is usually to quantify the user's interest in each item by predicting a relevance score, e.g., from the user's current situation or personal preferences in the past. Nowadays, recommender systems are used in various domains to recommend items such as products on e-commerce sites, movies and music on media portals, or people in social networks. To assess the user's preferences, recommender systems proposed in past research often utilized explicit feedback, i.e., deliberately given ratings or like/dislike statements for items. In practice, however, in many of today's application domains of recommender systems this kind of information is not existent. Therefore, recommender systems have to rely on implicit feedback that is derived from the users' behavior and interactions with the system. This information can be extracted from navigation or transaction logs. Using implicit feedback leads to new challenges and open questions regarding, for example, the huge amount of signals to process, the ambiguity of the feedback, and the inevitable noise in the data. This thesis by publication explores some of these challenges and questions that have not been covered in previous research. The thesis is divided into two parts. In the first part, the thesis reviews existing works on implicit feedback and recommender systems that exploit these signals, especially in the Social Information Access domain, which utilizes the "community wisdom" of the social web for recommendations. Common application scenarios for implicit feedback are discussed and a categorization scheme that classifies different types of observable user behavior is established. In addition, state-of-the-art algorithmic approaches for implicit feedback are examined that, e.g., interpret implicit signals directly or convert them to explicit ratings to be able to use "classic" recommendation approaches that were designed for explicit feedback. The second part of the thesis comprises some of the author's publications that deal with selected challenges of implicit feedback based recommendations. These contain (i) a specialized learning-to-rank algorithm that can differentiate different levels of interest indicator strength in implicit signals, (ii) contextualized recommendation techniques for the e-commerce domain that adapt product suggestions to customers' current short-term goals as well as their long-term preferences, and (iii) intelligent reminding approaches that aim at the re-discovery of relevant items in a customer's browsing history. Furthermore, the last paper of the thesis provides an in-depth analysis of different biases of various recommendation algorithms. Especially the popularity bias, the tendency to recommend mostly popular items, can be problematic in practical settings and countermeasures to reduce this bias are proposed

Personalized program guides for digital television.

Razvoj digitalne televizije je doveo do značajnog porasta broja TV sadržaja dostupnih korisnicima, ali je otežao izbor onog koji je od interesa. Sve do pojave personalizovanih programskih vodiča sposobnih da nauče korisnička interesovanja i preporuče odgovarajuće sadržaje nije postojalo rešenje koje je na adekvatan način razmatralo ovaj problem. Ranija rešenja, kao što su štampani i elektronski vodiči, su pretežno samo pretvarala problem viška informacija u drugi oblik. Napredak tehnologije i društva postavlja sve veće zahteve pred personalizovane programske vodiče za digitalnu televiziju, što zahteva njihovo pažljivo planiranje i projektovanje. Vodiči moraju da budu u mogućnosti da modeliraju različite načine donošenja odluka pojedinačnih korisnika, da rade u realnom vremenu na mobilnim uređajima s ograničenim hardverskim resursima, da vode računa o karakteristikama prikupljenih podataka, da uzimaju u obzir kontekst u kome se pristupa TV sadržaju i da štite privatnost svih korisnika, jer neki od njih nisu svesni mogućih opasnosti. Pažljivim izborom arhitekture i algoritma učenja, lokalno implementiran vodič baziran na neuralnim mrežama može da ispuni sve ove zahteve. S obzirom na to da korisnici znatno češće pružaju informacije o sadržajima koji im se dopadaju nego o onim koji im se ne dopadaju, u ekstremnim slučajevima se dešava to da su prikupljene samo pozitivne interakcije. Da bi se taj problem prevazišao, predložen je sistem s dva režima rada. U prvom režimu sistem uči i pruža preporuke samo na osnovu TV sadržaja koje korisnik voli, dok u drugom izjednačava uticaj sadržaja koje korisnik voli i onih koje ne voli na proces pružanja preporuka. Povećan uticaj pozitivnih interakcija dovodi do degradacije predikcije sadržaja koje posmatrač ne želi da gleda, te će se, usled greške u klasifikaciji, neželjeni sadržaji često pojavljivati u listi preporuka i na taj način smanjiti zadovoljstvo korisnika. Korišćenjem serije simulacija pokazali smo da je postignuto trajanje treniranja neuralne mreže kratko, čak i na uređajima s ograničenim hardverskim resursima. Zaključak je da je predloženi vodič veoma pogodan za implementaciju na mobilnim uređajima od kojih se očekuje da u budućnosti postanu dominantan način pristupa TV sadržajima.The development of digital television significantly increased the quantity of media contents available to the users, but made it difficult to make the right choice. Before the invention of the personalized program guides capable of learning user preferences and recommending adequate contents, there were no means of properly addressing this problem. Former solutions, such as printed or electronic program guides, mostly converted the problem of having to deal with too much information into another form. The advancements in both technology and society put higher demands to the personalized program guides for digital TV, which require careful planning and design processes. Guides must be able to model various individual decision making approaches, work in real-time on mobile devices with limited hardware resources, take into account the characteristics of the collected data, take into consideration the program accessing context and protect the privacy of all users, since some of them are not aware of the possible risks. By carefully choosing the architecture and learning algorithms, a locally implemented guide based on neural networks can fulfil all the aforementioned requirements. Due to the fact that the users provide information about the content they like much more often than about the one they dislike, only positive interactions are collected in extreme cases. In order to overcome that situation, a system having two operating modes is proposed. The first mode enables the system to learn and give recommendations based on preferred TV contents, while the second equalizes the influence of the liked and disliked contents on the recommending process. The increased influence of positive interactions degrades the unwanted content prediction process, resulting in classification error, appearance of unwanted content in the recommendation list and user dissatisfaction. By applying a series of simulations, we showed the accomplished neural network training time to be short, even in cases of devices with limited hardware resources. It can be concluded that the proposed guide is very convenient for implementation on mobile devices which are expected to become a dominant way of accessing media contents in the future

Recomendação personalizada dinâmica de informação sobre serviços públicos e sociais na iTV para seniores : um estudo de caso

A difusão e o acesso adequado à informação sobre Serviços de Interesse Geral são direitos constitucionais dos cidadãos e integram fatores determinantes na estruturação de uma sociedade mais igualitária e baseada na democratização do conhecimento. No entanto, não obstante a crescente quantidade de informação disponível e a evolução das TIC, verifica-se que o cidadão sénior, muitas vezes caraterizado pelos seus baixos níveis de literacia digital e infoinclusão, tem frequentemente dificuldade em aceder a informações sobre políticas e serviços públicos e sociais dos quais pode beneficiar. Com necessidades informacionais específicas e cada vez mais tempo livre decorrente da reforma, os seniores tendem a utilizar a TV como meio primordial de informação e entretenimento. Deste modo, beneficiando da familiaridade deste público com a TV, muitas soluções tecnológicas inovadoras têm-se baseado neste dispositivo. No entanto, apenas conceber e empregar recursos tecnologicamente avançados não é suficiente. É, sim, preciso elaborar soluções personalizadas, que possam melhor adaptar-se às preferências e limitações deste segmento populacional. Neste caso concreto, tal trata-se de identificar qual a informação mais adequada a ser enviada a cada sénior. Por exemplo, informações sobre campanhas de saúde e descontos em taxas moderadoras devem ser enviadas conforme as preferências e o contexto (e.g. localização) do utilizador. Este trabalho propõe uma estratégia de personalização para a entrega de conteúdos informativos sobre Serviços de Interesse Geral, em um ambiente televisivo, para a população sénior. Para tal, este trabalho tem por objetivo alavancar a exibição de vídeos informativos através da integração de um Sistema de Recomendação Sensível ao Contexto (CARS). A investigação dividiu-se em três etapas distintas, numa abordagem de design participativo, de modo que o CARS seja adequado às especificidades deste segmento populacional, considerando as opiniões e indicações de vários seniores em todas as fases do estudo. Na primeira etapa, são caracterizados os dados do trinómio [Item x Utilizador x Contexto]. Esta etapa decorreu com colaboração de especialistas nas áreas de gerontologia, serviços públicos e TV Interativa, bem como com a colaboração de seniores recrutados no âmbito do projeto +TV4E, a partir da aplicação de entrevistas, focus groups e testes guiados. Na segunda etapa, é proposto o CARS de acordo com o Modelo de dados e o esquema de interação obtidos a partir dos resultados provenientes da etapa anterior. Um algoritmo de recomendação híbrido é proposto para gerar as recomendações. Por fim, na terceira e última etapa, foi desenvolvido um protótipo, integrado no projeto +TV4E, de modo a validar o CARS, em ambiente doméstico, por um período de duas semanas e com o apoio de 21 seniores residentes no distrito de Aveiro. A análise dos resultados, a partir dos registos de utilização do protótipo e de entrevistas, corroboram a utilidade e adequabilidade da estratégia de personalização proposta.The dissemination and adequate access to information about Services of General Interest are constitutional rights of the citizens and play a major role in structuring a more egalitarian society based on the democratization of knowledge. However, despite the increasing amount of information available and the evolution of information and communication technologies (ICT), senior citizens, often characterized by lower levels of digital literacy and info-inclusion, often struggle to access information about policies and services that they can benefit from. With specific informational needs and free time due to retirement, seniors tend to use TV as a primary mean of information and entertainment. In this way, benefiting from the familiarity of these citizens with the TV, many innovative technological solutions have been leveraged this device. However, solely designing and employing technologically advanced features is not enough. It is necessary to develop personalized solutions to better adapt to seniors’ preferences and limitations. In this case, this concerns identifying which information is more appropriate to be provided for each senior. For example, information on health campaigns and social tariffs discounts should be tailored according to the user’s specific preferences and contextual factors (e.g. location and dates). That said, this research proposes a personalization strategy for the delivery of highvalued informative contents about Services of General Interest for the senior population. To this end, this work aims to leverage the informative videos exhibition through the integration of a Context-Aware Recommender System (CARS). The investigation was divided into three distinct phases, in a participatory design approach, so that the CARS is adequate to the specifics of this population segment, considering seniors’ opinions and indications in all phases of the study. In the first phase, data of the trinomial [Item x User x Context] is characterized. In addition, this phase was carried out with the collaboration of specialists in the areas of gerontology, public services, interactive TV and software engineering, as well as the collaboration of seniors recruited under the + TV4E project, through the application of interviews, focus groups and guided tests. In the second phase, the CARS is proposed according to the Data Model and the interaction scheme obtained from the results of the previous phase. A hybrid filtering algorithm is proposed to generate the recommendations. Finally, in the third and last phase, a prototype was developed and integrated in the scope of + TV4E project, in order to validate the CARS, in a domestic environment, for a period of two weeks, and with the support of 21 senior residents in the district of Aveiro. The analysis of the results, based on user interactions and interviews, corroborate the usefulness and appropriateness of the personalization strategy proposed by CARS.Programa Doutoral em Informação e Comunicação em Plataformas Digitai