On the real-time web as a source of recommendation knowledge

Esparza, Sandra Garcia; O’Mahony, Michael P.; Smyth, Barry

doi:10.1145/1864708.1864773

Cited by 30 publications

(7 citation statements)

References 11 publications

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“…Ha et al [30] incorporated social network information into their recommendation model, considering both direct and indirect user relationships with weighted degrees representing relationship strengths. Esparza et al [31] analyzed users' Twitter content to make movie recommendations based on their reviews. Pera et al [32] proposed a group movie recommendation approach using content similarity and popularity, employing association factors between words to measure movie content similarity and a merging strategy to infer group interests from individual interests.…”

Section: Video Recommendationsmentioning

confidence: 99%

A Graph based Recommendation Model for Short Video with Attention and Message Passing

Singh,

Rousseau,

Delacroix

2024

Preprint

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The rise of the Internet has revolutionized the way information is accessed and consumed, leading to an era characterized by data abundance and complexity. In this landscape, users are often inundated with numerous choices, creating a challenge known as information overload. To address this challenge, recommendation systems have emerged as indispensable tools across various online platforms. These systems, by analyzing user attributes and behaviors, aim to provide personalized recommendations, thus assisting users in navigating the vast array of available information. However, conventional recommendation algorithms often overlook valuable user-item features embedded in additional data sources such as review texts. Moreover, integrating auxiliary information to enhance recommendation accuracy adds complexity to the process. In light of these considerations, this paper introduces an interest-aware message passing recommendation model. By partitioning the graph into subgraphs based on user and item interactions, the model leverages graph convolution operations to learn node representations, thereby enhancing the expression of users' interests and preferences. Through empirical evaluation on benchmark datasets, our model outperforms existing baselines, demonstrating its efficacy in addressing the challenges of recommendation in the age of data abundance.

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Section: Video Recommendationsmentioning

confidence: 99%

A Graph based Recommendation Model for Short Video with Attention and Message Passing

Singh,

Rousseau,

Delacroix

2024

Preprint

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“…It employed six different degrees to represent the weights of relationships between users. Esparza et al [22] conducted an analysis of users' Twitter content to generate movie recommendations based on their movie reviews. Pera et al [23] introduced a group movie recommendation approach leveraging content similarity and popularity.…”

Section: Video Recommendationsmentioning

confidence: 99%

Multimodal Representation and Cross Modal Enhancement for Short Video Recommendation

Jae-won,

Yoo-jin,

Min-seok

2024

Preprint

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The surge in short video content production on various platforms has marked the emergence of short videos as a new and popular form of media. However, the sheer abundance and complexity of short video data present challenges for effective video recommendation. Short videos encapsulate rich multimodal information across both temporal and spatial dimensions, allowing users to engage with videos in various ways—whether focusing on the content of a particular shot, delving into the storyline, or enjoying the accompanying music. Conventional video recommendation systems typically focus on a singular type of recommended content, providing recommendations for entire videos, which may not fully satisfy the nuanced preferences of users. In multimodal data, each modality contributes specific information to the others, establishing correlations between them. In the context of video data, which combines image, speech, and text data, understanding the relationships among these three media types is crucial for effective multimodal content-based video recommendation. In this paper, we leverage the consistency of multimodal features for understanding multimedia content, aiming to derive a robust representation from the inherent characteristics of short videos. Unlike previous studies that primarily concentrate on a single modality in short video recommendation, our approach capitalizes on the multimodality of short video content and adopts a multimodal recommendation strategy. By extracting and fusing information from multiple modalities, we achieve a more comprehensive short video content analysis, paving the way for our recommendation method.

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“…Therefore, such approaches lack data and they could not be useful for venue recommendations and contextual suggestion systems [38,39]. Other studies used review-based approaches, but also suffer from a lack of reviews available for several venues [37][38][39][40][41][177][178][179].…”

Section: Critical Reviewmentioning

confidence: 99%

“…As the complex subjectivity of the term "context" increases, this should be carefully analyzed in the application to provide consistent suggestions. In most of the reviewed studies, contextual factors are chosen based on past studies lacking validation of such contexts in similar application domains [25,39,42,84,133,152,[177][178][179][180]. Hence, before developing contextual-based suggestion and recommendation systems, exploring relevant contexts in each application domain would be ideal [19].…”

Section: Recommendations For System Developersmentioning

confidence: 99%

“…Hence, before developing contextual-based suggestion and recommendation systems, exploring relevant contexts in each application domain would be ideal [19]. After compiling the context, its relevancy and tourists' preferences are required to assess the likelihood of the suggestion influencing the tourist [40,44,89,141,177,181]. The correlation between context and tourist preferences can be analyzed using techniques such as similarity measures and multi-regression [48,77,182,183].…”

Section: Recommendations For System Developersmentioning

confidence: 99%

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Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

et al. 2021

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Agenda 2030 of Sustainable Development Goals (SDGs) 9 and 11 recognizes tourism as one of the central industries to global development to tackle global challenges. With the transformation of information and communication technologies (ICT), e-tourism has evolved globally to establish commercial relationships using the Internet for offering tourism-related products, including giving personalised suggestions. The contextual suggestion has emerged as a modified recommendation system that is integrated with information-retrieval techniques within large databases to provide tourists with a list of suggestions based on contexts, such as location, time of day, or day of the week (weekdays or weekends). This study surveyed literature in the field of contextual suggestion and recommendation systems with a focus on e-tourism. The concerns linked with approaches used in contextual suggestion and recommendation systems are highlighted in this systematic review, while motivations, recommendations, and practical implications in e-tourism are also discussed in this paper. A query search using the keywords “contextual suggestion system”, “recommendation system”, and “tourism” identified 143 relevant articles published from 2012 to 2020. Four major repositories are considered for searching, namely, (i) Science Direct, (ii) Scopus, (iii) IEEE, and (iv) Web of Science. This review was carried out under the protocols of four phases, namely, (i) query searching in major article repositories, (ii) removal of duplicates, (iii) scan of title and abstract, and (iv) complete reading of articles. To identify the gaps in current research, a taxonomy analysis was exemplified into categories and subcategories. The main categories were highlighted as (i) review articles, (ii) model/framework, and (iii) applications. Critical analysis was carried out on the basis of the available literature on the limitations of approaches used in contextual suggestion and recommendation systems. In conclusion, the approaches used are mainly based on content-based filtering, collaborative filtering, preference-based product ranking, and language modelling. The evaluation measures for the contextual suggestion system include precision, normalized discounted cumulative, and mean reciprocal rank, while test collections comprise Internet resources. Given that the tourism industry contributed to the environmental and social-economic development, contextual suggestion and recommendation systems have presented themselves to be relevant in integrating and achieving SDG 9 and SDG 11 in many ways such as web-based e-services by the government sector and smart gadgets based on reliable and real-time data and information for city planners as well as law enforcement personnel in a sustainable city.

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On the real-time web as a source of recommendation knowledge

Cited by 30 publications

References 11 publications

A Graph based Recommendation Model for Short Video with Attention and Message Passing

A Graph based Recommendation Model for Short Video with Attention and Message Passing

Multimodal Representation and Cross Modal Enhancement for Short Video Recommendation

Systematic Review of Contextual Suggestion and Recommendation Systems for Sustainable e-Tourism

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