Emotion-based music recommendation by affinity discovery from film music

Shan, Man-Kwan; Kuo, Fang-Fei; Chiang, Meng-Fen; Lee, Suh-Yin

doi:10.1016/j.eswa.2008.09.042

Cited by 57 publications

(42 citation statements)

References 12 publications

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“…A summary of the recent relevant literature on this topic is given in Table I. There have been also studies using unnimodal or multimodal approaches for detecting the behavioral or emotional responses to multimedia [21], [23], [24], [20], [6]. There is currently a research trend towards estimating emotions from multimedia content automatically [13], [15], [14]. The emotion recognition has been also used in applications such as detecting topical relevance, or summarizing videos [11], [21], [9].…”

Section: State Of the Artmentioning

confidence: 99%

“…Shan et al [14] used affective characterization using content analysis to improve film music recommendation. Tkalčič et al showed how affective information can improve image recommendation [15].…”

Section: State Of the Artmentioning

confidence: 99%

“…Implicit tagging, dealing directly with users' reactions, can be used directly to find affective tags for a given content. These tags can, in return, help recommendation and retrieval systems to improve their performance [14], [15], [3]. Challenges and difficulties in using self reported emotions [16] make implicit tagging a suitable alternative for recognizing emotional tags.…”

Section: Introductionmentioning

confidence: 99%

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Human-centered implicit tagging: Overview and perspectives

Soleymani

Pantić

2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Abstract-Tags are an effective form of metadata which help users to locate and browse multimedia content of interest. Tags can be generated by users (user-generated explicit tags), automatically from the content (content-based tags), or assigned automatically based on non-verbal behavioral reactions of users to multimedia content (implicit human-centered tags). This paper discusses the definition and applications of implicit humancentered tagging. Implicit tagging is an effortless process by which content is tagged based on users' spontaneous reactions. It is a novel but growing research topic which is attracting more attention with the growing availability of built-in sensors. This paper discusses the state of the art in this novel field of research and provides an overview of publicly available relevant databases and annotation tools. We finally discuss in detail challenges and opportunities in the field.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Human-centered implicit tagging: Overview and perspectives

Soleymani

Pantić

2012

2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…Soleymani et al [2009] built a collaborative filtering system that retrieves video clips based on affective queries. Similarly, but for music content, Shan et al [2009] have developed a system that performs emotion based queries. Arapakis et al [2009] built a complete video recommender system that detects the users' affective state and provides recommended content.…”

Section: Related Workmentioning

confidence: 99%

Impact of Implicit and Explicit Affective Labeling on a Recommender System’s Performance

Tkalčič

Odi

et al. 2012

Lecture Notes in Computer Science

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Abstract. Affective labeling of multimedia content can be useful in recommender systems. In this paper we compare the effect of implicit and explicit affective labeling in an image recommender system. The implicit affective labeling method is based on an emotion detection technique that takes as input the video sequences of the users' facial expressions. It extracts Gabor low level features from the video frames and employs a kNN machine learning technique to generate affective labels in the valence-arousal-dominance space. We performed a comparative study of the performance of a content-based recommender (CBR) system for images that uses three types of metadata to model the users and the items: (i) generic metadata, (ii) explicitly acquired affective labels and (iii) implicitly acquired affective labels with the proposed methodology. The results showed that the CBR performs best when explicit labels are used. However, implicitly acquired labels yield a significantly better performance of the CBR than generic metadata while being an unobtrusive feedback tool.

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“…For example, an emotional facial expression can be detected from the eyes and lips of a human face [3]. Other researchers try to recognise emotions from several ways such as, colour [17][18][19][20], haptic device [9], acoustic [21], music [22] and dance movement [23].…”

Section: Introductionmentioning

confidence: 99%