Recommending Multimedia Objects in Cultural Heritage Applications

Bartolini, Ilaria; Moscato, Vincenzo; Pensa, Ruggero G.; Penta, Antonio; Picariello, Antonio; Sansone, Carlo; Sapino, Maria Luisa

doi:10.1007/978-3-642-41190-8_28

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…The authors of Bartolini et al (2013) propose a multimedia (image-videodocument) recommender platform to address the cultural heritage domain: in particular, a recommender system to provide personalized visiting paths to tourists visiting the Paestum ruins, one of the major Greco-Roman cities in the South of Italy. The proposed system is able to uniformly combine heterogeneous multimedia data and to provide context-aware recommendation techniques.…”

Section: Image Recommendationmentioning

confidence: 99%

Movie genome: alleviating new item cold start in movie recommendation

Deldjoo

Dacrema

Constantin

et al. 2019

User Model User-Adap Inter

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As of today, most movie recommendation services base their recommendations on collaborative filtering (CF) and/or content-based filtering (CBF) models that use metadata (e.g., genre or cast). In most video-on-demand and streaming services, however, new movies and TV series are continuously added. CF models are unable to make predictions in such a scenario, since the newly added videos lack interactions-a problem technically known as new item cold start (CS). Currently, the most common approach to this problem is to switch to a purely CBF method, usually by exploiting textual metadata. This approach is known to have lower accuracy than CF because it ignores useful collaborative information and relies on human-generated textual metadata, which are expensive to collect and often prone to errors. User-generated content, such as tags, can also be rare or absent in CS situations. In this paper, we introduce a new movie recommender system that addresses the new item problem in the movie domain by (i) integrating state-of-the-art audio and visual descriptors, which can be automatically extracted from video content and constitute what we call the movie genome; (ii) exploiting an effective data fusion method named canonical correlation analysis, which was successfully tested in our previous works Deldjoo et alto better exploit complementary information between different modalities; (iii) proposing a two-step hybrid approach which trains a CF model on warm items (items with interactions) and leverages the learned model on the movie genome to recommend cold items (items without interactions). Experimental validation is carried out using a system-centric study on a large-scale, real-world movie recommendation dataset both in an absolute cold start and in a cold to warm transition; and a user-centric online experiment measuring different subjective aspects, such as satisfaction and diversity. Results show the benefits of this approach compared to existing approaches.

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Section: Image Recommendationmentioning

confidence: 99%

Movie genome: alleviating new item cold start in movie recommendation

Deldjoo

Dacrema

Constantin

et al. 2019

User Model User-Adap Inter

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“…In the last years, some approaches for providing recommendations in the CH domain based on semantic data have been presented, especially exploiting domain ontologies and vocabularies, such as Ruotsalo et al (2013), Moreno et al (2013), Bartolini et al (2013), Albanese et al (2011) and Wang et al (2009).…”

Section: Semantic Poi Recommenders In the Cultural Heritage Domainmentioning

confidence: 99%

“…In (Bartolini et al 2013) a recommender engine in the CH domain is described. This system can provide context-aware recommendations of heterogeneous multimedia data (i.e., images, videos/shots, documents) based on low level descriptors and semantic annotations of multimedia resources and a user model expressed as a list of tags.…”

Section: Semantic Poi Recommenders In the Cultural Heritage Domainmentioning

confidence: 99%

Enhancing cultural recommendations through social and linked open data

Sansonetti

Gasparetti

Micarelli

et al. 2019

User Model User-Adap Inter

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In this article, we describe a hybrid recommender system (RS) in the artistic and cultural heritage area, which takes into account the activities on social media performed by the target user and her friends, and takes advantage of linked open data (LOD) sources. Concretely, the proposed RS (1) extracts information from Facebook by analyzing content generated by users and their friends; (2) performs disambiguation tasks through LOD tools; (3) profiles the active user as a social graph; (4) provides her with personalized suggestions of artistic and cultural resources in the surroundings of the user's current location. The last point is performed by integrating collaborative filtering algorithms with semantic technologies in order to leverage LOD sources such as DBpedia and Europeana. Based on the recommended points of cultural interest, the proposed system is also able to suggest to the active user itineraries among them, which meet her preferences and needs and are sensitive to her physical and social contexts as well. Experimental results on real users showed the effectiveness of the different modules of the proposed recommender.

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“…In this way, for example, a developer could compare the performance of different streaming engines before committing to a specific one for her applications. The fields of application of such technologies are innumerable, from the security of citizens [1], to recommender systems [4], and sentiment analysis [5], to cite a few.…”

Section: Introductionmentioning

confidence: 99%

The Metamorphosis (of RAM3S)

Bartolini

Patella

2021

Applied Sciences

Self Cite

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The real-time analysis of Big Data streams is a terrific resource for transforming data into value. For this, Big Data technologies for smart processing of massive data streams are available, but the facilities they offer are often too raw to be effectively exploited by analysts. RAM3S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. RAM3S has been proven helpful in simplifying the deployment of non-parallel techniques to streaming platforms, such as Apache Storm or Apache Flink. In this paper, we show how RAM3S has been updated to incorporate novel stream processing platforms, such as Apache Samza, and to be able to communicate with different message brokers, such as Apache Kafka. Abstracting from the message broker also provides us with the ability to pipeline several RAM3S instances that can, therefore, perform different processing tasks. This represents a richer model for stream analysis with respect to the one already available in the original RAM3S version. The generality of this new RAM3S version is demonstrated through experiments conducted on three different multimedia applications, proving that RAM3S is a formidable asset for enabling efficient and effective Data Mining and Machine Learning on multimedia data streams.

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Recommending Multimedia Objects in Cultural Heritage Applications

Cited by 13 publications

References 14 publications

Movie genome: alleviating new item cold start in movie recommendation

Movie genome: alleviating new item cold start in movie recommendation

Enhancing cultural recommendations through social and linked open data

The Metamorphosis (of RAM3S)

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