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

On User Modelling for Personalised News Video Recommendation

In this paper, we introduce a novel approach for modelling user interests. Our approach captures users evolving information needs, identifies aspects of their need and recommends relevant news items to the users. We introduce our approach within the context of personalised news video retrieval. A news video data set is used for experimentation. We employ a simulated user evaluation

Semantic user profiling techniques for personalised multimedia recommendation

Due to the explosion of news materials available through broadcast and other channels, there is an increasing need for personalised news video retrieval. In this work, we introduce a semantic-based user modelling technique to capture users’ evolving information needs. Our approach exploits implicit user interaction to capture long-term user interests in a profile. The organised interests are used to retrieve and recommend news stories to the users. In this paper, we exploit the Linked Open Data Cloud to identify similar news stories that match the users’ interest. We evaluate various recommendation parameters by introducing a simulation-based evaluation scheme

Film and Tourism. An Information System for Disclosing the Cinematographic Attractiveness of Destinations (Giulia Lavarone pp.289-291)

The Department of Cultural Heritage of the University of Padova investigated the topic of film-induced tourism in a year-long project, concluded in April 2015, titled Strumenti innovativi per la promozione turistica: film-induced tourism (Novel Tools to Promote Tourism: Film-induced Tourism). The project was financed by the Veneto Region through ESF funds. It brought together the expertise of film scholars, computer scientists and destination management experts, in partnership with public bodies (Provincia di Padova) and ICT companies. The goal of the project was to develop an information system that fosters film-induced tourism combining data about a geographical area and the movies produced in it. The system is designed as a platform to store and convey rich contents, able to address the needs of the tourist but also of stakeholders as DMOs and film commissions. The system was planned as a model for the destinations which could take advantage of a significant cinematographic background, yet are unlikely to spontaneously produce considerable film-induced tourism phenomena. The province of Padova has been used as a case study

Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

The Datafication of Public Service Media Dreams, Dilemmas and Practical Problems:A Case Study of the Implementation of Personalized Recommendations at the Danish Public Service Media ‘DR’

Historically, public service broadcasting had no quantifiable knowledge about audiences, nor a great interest in knowing them. Today, the competitive logic of the media markets encourage public service media (PSM) organizations to increase datafication. In this paper we examine how a PSM organization interprets the classic public service obligations of creating societal cohesion and diversity in the new world of key performance indicators, business rules and algorithmic parameters.The paper presents a case study of the implementation of a personalization system for the video on demand service of the Danish PSM ‘DR’. Our empirical findings, based on longitudinal in-depth interviewing, indicate a long and difficult process of datafication of PSM, shaped by both the organizational path dependencies of broadcasting production and the expectations of public service broadcasting