A literature review of recommender systems in the television domain

Véras, Douglas; Prota, Thiago Monteiro; Bispo, Alysson; Prudêncio, Ricardo B. C.; Ferraz, Carlos

doi:10.1016/j.eswa.2015.06.052

Cited by 61 publications

(46 citation statements)

References 160 publications

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“…RSs are widely used in what we referred to as Bcontent intensive applications^, characterized by a very large amount of online multimedia information, either made available by the service provider or user generated (think of the 3 billion videos uploaded on YouTube by late 2012 [57] or the 20 million songs on Spotify [66]). In these contexts, the dimension of the multimedia search space and the availability of an enormous set of choices may slow down the user's exploration, reduce the visibility of some potentially interesting items, and increase the complexity of the decision making process [8,28,29,59,71,79]. Complementing (and in some cases even replacing) free navigation and traditional query-based paradigms, recommendations can alleviate the above problems, and reduce the information overload by focusing the search space and orienting the user's decisions [9].…”

Section: Introductionmentioning

confidence: 99%

User interface patterns in recommendation-empowered content intensive multimedia applications

Cremonesi

Elahi

Garzotto

2016

Multimed Tools Appl

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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 Bgood^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 (Brecommendations^) 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, Multimed Tools Appl

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Section: Introductionmentioning

confidence: 99%

User interface patterns in recommendation-empowered content intensive multimedia applications

Cremonesi

Elahi

Garzotto

2016

Multimed Tools Appl

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“…Due to the increased availability of multimedia, research has been focused on improving the users' decision process by reducing large catalogs of content to a few personalized suggestions [1]. Commercial recommender solutions are now considered core to the business of engaging users and thereby preventing abandonment [2].…”

Section: Introductionmentioning

confidence: 99%

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

Kristoffersen

Shepstone

2020

IEEE Trans. Multimedia

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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.

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“…Most state-of-the-art RSs are built using collaborative filtering (CF) [1,2,4], which is based solely on the analysis of user assigned item preferences. This approach is popular because of its high performance and independence from the nature of the items.…”

Section: Introductionmentioning

confidence: 99%

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

Sheridan¹,

Onsjö

Becerra

et al. 2019

Future Internet

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Collaborative filtering based recommender systems have been 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 when items mostly belong to the same category and are mostly alike (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 algorithm. As 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 observed that our proposed method showed 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.

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A literature review of recommender systems in the television domain

Cited by 61 publications

References 160 publications

User interface patterns in recommendation-empowered content intensive multimedia applications

User interface patterns in recommendation-empowered content intensive multimedia applications

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

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

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