Content-Based Video Recommendation System Based on Stylistic Visual Features

Deldjoo, Yashar; Elahi, Mehdi; Cremonesi, Paolo; Garzotto, Franca; Piazzolla, Pietro; Quadrana, Massimo

doi:10.1007/s13740-016-0060-9

Cited by 197 publications

(120 citation statements)

References 49 publications

(65 reference statements)

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“…Content-based approaches exploit explicit features of items (e.g., metadata) or implicit ones (derived from the interpretation of non-structured data, e. g. video files [31,32]. CBF suggests items that have characteristics similar to the ones the user has liked in previous experiences with the application, or has shown to like in the ongoing interaction with the application.…”

Section: Related Work 21 Recommender Systemmentioning

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: Related Work 21 Recommender Systemmentioning

confidence: 99%

User interface patterns in recommendation-empowered content intensive multimedia applications

Cremonesi

Elahi

Garzotto

2016

Multimed Tools Appl

Self Cite

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“…Deldjoo et al [52] have presented a technique that can perform extraction of visual features in order to perform video recommendation based on its contents. Nomiya et al [53] have presented a mechanism that can extract emotion-based information from the video.…”

Section: Existing Techniques Of Vamentioning

confidence: 99%

Insights to Video Analytic Modelling Approach with Future Line of Research

Chandra¹,

Reddy²

2016

IJCA

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The area of Video analytics has made some significant improvement due to advancement in image processing and datamining techniques. However, the inclination is still towards image contents and less to mined contents owing to many unsolved issues. Although concept of mining is more than 2 decade old, but mining approaches are yet to be standardized in the area of video surveillance system. With evolution of newer set of challenges in video capturing, existing mining models finds itself less applicable due to unstructured format of dynamic frames. Hence, this paper discusses about video analytics and presents a brief discussion of frequently used mining approaches in video as well as discussed some recent studies in this direction in order to scale the degree of effectiveness in existing system. The paper also presents research gap and provided solution as future line of research as a possible way to overcome the research gap.

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“…eir work in [9,10] discusses video recommendation based on low-level features and visual features. And their work in [11,12] discusses visual features in movie recommendation.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Image Visual Features in Content-Based Recommender System

Deng

Ren

Qin

et al. 2018

Scientific Programming

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Content-based (CB) and collaborative filtering (CF) recommendation algorithms are widely used in modern e-commerce recommender systems (RSs) to improve user experience of personalized services. Item content features and user-item rating data are primarily used to train the recommendation model. However, sparse data would lead such systems unreliable. To solve the data sparsity problem, we consider that more latent information would be imported to catch users' potential preferences. erefore, hybrid features which include all kinds of item features are used to excavate users' interests. In particular, we find that the image visual features can catch more potential preferences of users. In this paper, we leverage the combination of user-item rating data and item hybrid features to propose a novel CB recommendation model, which is suitable for rating-based recommender scenarios. e experimental results show that the proposed model has better recommendation performance in sparse data scenarios than conventional approaches. Besides, training offline and recommendation online make the model has higher efficiency on large datasets.

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Content-Based Video Recommendation System Based on Stylistic Visual Features

Cited by 197 publications

References 49 publications

User interface patterns in recommendation-empowered content intensive multimedia applications

User interface patterns in recommendation-empowered content intensive multimedia applications

Insights to Video Analytic Modelling Approach with Future Line of Research

Leveraging Image Visual Features in Content-Based Recommender System

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