Kiite Cafe: A Web Service for Getting Together Virtually to Listen to Music (...Kosetsu Tsukuda
謝辞:Kiiteおよびニコニコ動画のユーザ、VOCALOID楽曲のクリエータ、VOCALOID楽曲の日毎・週毎の人気度ランキングの作成者、そしてVOCALOID文化とそれに関連した文化を築き、支援し、楽しんでいる全ての人々に感謝します。また、Kiite を共同開発したクリプトン・フューチャー・メディア株式会社、我々の研究活動を初期は暗黙的に(後に明示的に)応援してきたニコニコ動画に感謝します。
本ポスターは2021年11月7日~12日に開催された「22nd International Society for Music Information Retrieval Conference (ISMIR 2021)」の発表資料です。
発表した論文のPDFは以下のURLから閲覧できます。
http://ktsukuda.me/wp-content/uploads/ISMIR2021_KiiteCafe_tsukuda.pdf
Toward an Understanding of Lyrics-viewing Behavior While Listening to Music o...Kosetsu Tsukuda
本ポスターは2021年11月7日~12日に開催された「22nd International Society for Music Information Retrieval Conference (ISMIR 2021)」の発表資料です。
発表した論文のPDFは以下のURLから閲覧できます。
http://ktsukuda.me/wp-content/uploads/ISMIR2021_Lyrics_tsukuda.pdf
Explainable Recommendation for Repeat Consumption (RecSys 2020)Kosetsu Tsukuda
The document summarizes a study that generated explanations for recommending previously consumed music items using three factors: personal, social, and item factors. It involved an online survey of 622 participants. The study found that explanations using personal and social factors were more persuasive than those using only item factors. It also found preferences increased for explanations using higher values on the factors. The researchers conclude music platforms could improve recommendations by using more persuasive explanation styles and setting factor values with higher user preferences.
Explainable Recommendation for Repeat Consumption (RecSys 2020)Kosetsu Tsukuda
The document discusses explainable recommendations for repeat consumption of items a user has already consumed. It proposes generating explanations for recommending previously consumed items using personal, social, and item factors, such as the time since the user last listened to a song or the number of listeners of a song. It evaluated the persuasiveness of nine explanation types through an online survey with over 600 participants.
DualDiv: Diversifying Items and Explanation Styles in Explainable Hybrid Reco...Kosetsu Tsukuda
This document proposes a method called DualDiv to diversify both recommended items and explanation styles in a hybrid recommender system. Experimental results show that DualDiv improves diversification metrics like AILD and EILD without decreasing recall, and selects dissimilar explanation styles especially for similar artists, outperforming alternative methods. DualDiv uses a greedy algorithm to generate a diversified ranked list of explanation styles for each recommended item.
Lyric Jumper: A Lyrics-Based Music Exploratory Web Service by Modeling Lyrics...Kosetsu Tsukuda
1) Proposed a topic model that assumes each artist has a distribution over topics and each song is assigned one topic, extending LDA.
2) Applied the model to 147,990 lyrics to implement the Lyric Jumper web service, allowing users to explore lyrics based on estimated topics.
3) Lyric Jumper analysis of over 30 days of logs from over 1,200 PC and 11,000 smartphone users found phrase recommendation was the most frequently used function.
Listener Anonymizer: Camouflaging Play Logs to Preserve User’s Demographic An...Kosetsu Tsukuda
1) The document describes a technique called Listener Anonymizer that aims to camouflage users' music play logs to preserve their demographic anonymity from being predicted.
2) Listener Anonymizer works by computing the effectiveness of different songs at anonymizing a user's nationality, gender, or age based on their play log, and recommending songs to play that will anonymize their demographics.
3) By playing the recommended songs, it biases the prediction of a user's demographics from their play log, preserving their anonymity while still allowing them to use music recommendation services.
Listener Anonymizer: Camouflaging Play Logs to Preserve User’s Demographic An...Kosetsu Tsukuda
1. The document proposes a technique called Listener Anonymizer that aims to preserve users' demographic anonymity when using online music services while still providing music recommendations.
2. Listener Anonymizer works by camouflaging users' play logs with a small number of recommended songs in order to anonymize predictions of users' demographic attributes like age, gender, and nationality.
3. The technique was evaluated on a dataset from Last.fm and showed improvements over baseline methods in anonymizing users' demographics with only a small number of additional songs.
Kiite Cafe: A Web Service for Getting Together Virtually to Listen to Music (...Kosetsu Tsukuda
謝辞:Kiiteおよびニコニコ動画のユーザ、VOCALOID楽曲のクリエータ、VOCALOID楽曲の日毎・週毎の人気度ランキングの作成者、そしてVOCALOID文化とそれに関連した文化を築き、支援し、楽しんでいる全ての人々に感謝します。また、Kiite を共同開発したクリプトン・フューチャー・メディア株式会社、我々の研究活動を初期は暗黙的に(後に明示的に)応援してきたニコニコ動画に感謝します。
本ポスターは2021年11月7日~12日に開催された「22nd International Society for Music Information Retrieval Conference (ISMIR 2021)」の発表資料です。
発表した論文のPDFは以下のURLから閲覧できます。
http://ktsukuda.me/wp-content/uploads/ISMIR2021_KiiteCafe_tsukuda.pdf
Toward an Understanding of Lyrics-viewing Behavior While Listening to Music o...Kosetsu Tsukuda
本ポスターは2021年11月7日~12日に開催された「22nd International Society for Music Information Retrieval Conference (ISMIR 2021)」の発表資料です。
発表した論文のPDFは以下のURLから閲覧できます。
http://ktsukuda.me/wp-content/uploads/ISMIR2021_Lyrics_tsukuda.pdf
Explainable Recommendation for Repeat Consumption (RecSys 2020)Kosetsu Tsukuda
The document summarizes a study that generated explanations for recommending previously consumed music items using three factors: personal, social, and item factors. It involved an online survey of 622 participants. The study found that explanations using personal and social factors were more persuasive than those using only item factors. It also found preferences increased for explanations using higher values on the factors. The researchers conclude music platforms could improve recommendations by using more persuasive explanation styles and setting factor values with higher user preferences.
Explainable Recommendation for Repeat Consumption (RecSys 2020)Kosetsu Tsukuda
The document discusses explainable recommendations for repeat consumption of items a user has already consumed. It proposes generating explanations for recommending previously consumed items using personal, social, and item factors, such as the time since the user last listened to a song or the number of listeners of a song. It evaluated the persuasiveness of nine explanation types through an online survey with over 600 participants.
DualDiv: Diversifying Items and Explanation Styles in Explainable Hybrid Reco...Kosetsu Tsukuda
This document proposes a method called DualDiv to diversify both recommended items and explanation styles in a hybrid recommender system. Experimental results show that DualDiv improves diversification metrics like AILD and EILD without decreasing recall, and selects dissimilar explanation styles especially for similar artists, outperforming alternative methods. DualDiv uses a greedy algorithm to generate a diversified ranked list of explanation styles for each recommended item.
Lyric Jumper: A Lyrics-Based Music Exploratory Web Service by Modeling Lyrics...Kosetsu Tsukuda
1) Proposed a topic model that assumes each artist has a distribution over topics and each song is assigned one topic, extending LDA.
2) Applied the model to 147,990 lyrics to implement the Lyric Jumper web service, allowing users to explore lyrics based on estimated topics.
3) Lyric Jumper analysis of over 30 days of logs from over 1,200 PC and 11,000 smartphone users found phrase recommendation was the most frequently used function.
Listener Anonymizer: Camouflaging Play Logs to Preserve User’s Demographic An...Kosetsu Tsukuda
1) The document describes a technique called Listener Anonymizer that aims to camouflage users' music play logs to preserve their demographic anonymity from being predicted.
2) Listener Anonymizer works by computing the effectiveness of different songs at anonymizing a user's nationality, gender, or age based on their play log, and recommending songs to play that will anonymize their demographics.
3) By playing the recommended songs, it biases the prediction of a user's demographics from their play log, preserving their anonymity while still allowing them to use music recommendation services.
Listener Anonymizer: Camouflaging Play Logs to Preserve User’s Demographic An...Kosetsu Tsukuda
1. The document proposes a technique called Listener Anonymizer that aims to preserve users' demographic anonymity when using online music services while still providing music recommendations.
2. Listener Anonymizer works by camouflaging users' play logs with a small number of recommended songs in order to anonymize predictions of users' demographic attributes like age, gender, and nationality.
3. The technique was evaluated on a dataset from Last.fm and showed improvements over baseline methods in anonymizing users' demographics with only a small number of additional songs.
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