Enhanced Semantic TV-Show Representation for Personalized Electronic Program Guides

Musto, Cataldo; Narducci, Fedelucio; Lops, Pasquale; Semeraro, Giovanni; Gemmis, Marco de; Barbieri, M.; Korst, Jan; Pronk, Verus; Clout, Ramon

doi:10.1007/978-3-642-31454-4_16

Cited by 22 publications

(8 citation statements)

References 18 publications

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“…As the solution proposed in this paper is based on the extraction of EPG content, which in some cases may contain reduced information [10], a possible future work is to investigate ways to obtain data from different sources to enrich the EPG information, and improve the textual representation of TV programs, for example, using information from Wikipedia 6 [10]. 5 Given a category, how many of the top 4 recommended books are of that same category?…”

Section: Discussionmentioning

confidence: 99%

Recommendation in the Digital TV Domain: an Architecture based on Textual Description Analysis

Ramos

Costa

Soares

et al. 2015

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Recommendation systems have been used in several application domains, most recently for TV (Digital TV, Smart TV, etc.). Several approaches can be used to recommend items, tags, etc., mainly based on user feedback. However, in the Digital TV domain, user feedback has to be done generally by using the remote control, which should be avoided to improve user experience, since assigning explicit feedback to items is restricted by the characteristics of this domain (difficulties when typing with the remote control, etc.). Moreover, in the Smart TV environment several types of items can be recommended (movies, musics, books, etc.). Thus, the recommendation should be generic enough to suit to different content. To solve the problem of acquiring explicit feedback and still generate personalized recommendations to be used by different Smart TV applications, this work proposes a recommendation architecture based on the extraction and classification of terms by analyzing the textual descriptions of TV programs present on electronic programming guides. In order to validate the proposed solution, a prototype using a real dataset has been developed, showing that using the recommended terms it is possible to generate final recommendations for different Smart TV applications.

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Section: Discussionmentioning

confidence: 99%

Recommendation in the Digital TV Domain: an Architecture based on Textual Description Analysis

Ramos

Costa

Soares

et al. 2015

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…Different types of content attributes used by recommender systems could vary from traditional semantic attributes to novel visual features. The former type is more high level, and it is obtained from traditional sources, ranging from databases, or ontologies, to review websites, or social media (Ahn et al, 2007; Billsus & Pazzani, 2000; Cantador, Szomszor, Alani, Fernández, & Castells, 2008; Middleton, Shadbolt, & De Roure, 2004; Mooney & Roy, 2000; Musto et al, 2012). The latter type, on the other hand, is more low level, and it is obtained by directly analysing the video files (Deldjoo, Elahi, Quadrana, & Cremonesi, 2015; Deldjoo, Elahi, Quadrana, Cremonesi, & Garzotto, 2015).…”

Section: Related Workmentioning

confidence: 99%

Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines

Hazrati

Elahi

2020

Expert Systems

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Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.

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“…Multimedia recommender systems typically exploit highlevel or intermediate-level features in order to generate movie recommendation [6,24]. This type of features express semantic and syntactic properties of media content that are obtained from structured sources of meta-information such as databases, lexicons and ontologies, or from less structured data such as reviews, news articles, item descriptions and social tags.…”

Section: Multimedia Recommender Systemsmentioning

confidence: 99%

Using visual features based on MPEG-7 and deep learning for movie recommendation

Deldjoo

Elahi

Quadrana

et al. 2018

Int J Multimed Info Retr

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Item features play an important role in movie recommender systems, where recommendations can be generated by using explicit or implicit preferences of users on traditional features (attributes) such as tag, genre, and cast. Typically, movie features are human-generated, either editorially (e.g., genre and cast) or by leveraging the wisdom of the crowd (e.g., tag), and as such, they are prone to noise and are expensive to collect. Moreover, these features are often rare or absent for new items, making it difficult or even impossible to provide good quality recommendations.In this paper, we show that user's preferences on movies can be better described in terms of the Mise-en-Scène features, i.e., the visual aspects of a movie that characterize design, aesthetics and style (e.g., colors, textures). We use both MPEG-7 visual descriptors and Deep Learning hidden layers as example of mise-en-scène features that can visually describe movies. Interestingly, mise-en-scène features can be computed automatically from video files or even from trailers, offering more flexibility in handling new items, avoiding the need for costly and error-prone human-based tagging, and providing good scalability.We have conducted a set of experiments on a large catalogue of 4K movies. Results show that recommendations based on mise-en-scène features consistently provide the best performance with respect to richer sets of more traditional features, such as genre and tag.

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Enhanced Semantic TV-Show Representation for Personalized Electronic Program Guides

Cited by 22 publications

References 18 publications

Recommendation in the Digital TV Domain: an Architecture based on Textual Description Analysis

Recommendation in the Digital TV Domain: an Architecture based on Textual Description Analysis

Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines

Using visual features based on MPEG-7 and deep learning for movie recommendation

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