In this study, we address two emerging yet challenging problems in social media: (1) scoring the text tags in terms of the influence to the numbers of views, comments, and favorite ratings of images and videos on content sharing services, and (2) recommending additional tags to increase such popularity-related numbers. For these purposes, we present the FolkPopularityRank algorithm, which can score text tags based on their ability to influence the popularity-related numbers. The FolkPopularityRank algorithm is inspired by the PageRank and FolkRank algorithms but the scores of the tags are calculated not only by the co-occurrence of the tags but also by considering the popularity-related numbers of the content. To the best of our knowledge, this is the first attempt to recommending tags that can enhance popularity attributes of social media. We conducted extensive experiments with about 1,000 images. We uploaded the photos with the recommended tags along with the original tags to Flickr as a real test, and obtained very promising results.

In this demo, we address two emerging yet challenging problems in social media: (1) scoring the text tags in terms of the influence to the numbers of views, comments, and favorite ratings of images and videos on content sharing services, and (2) recommending additional tags to increase such popularity-related numbers. For these purposes, we present a demo using our FolkPopularityRank (FP-Rank) algorithm, which can score and recommend text tags based on their ability to influence the popularity-related numbers. Our experiments using 1,000 photos showed that we can achieve 1.6 times more views than the original tag sets in Flickr just by adding tags recommended by FP-Rank.

In order to support users in the tagging process and recommendation, we had proposed two tag ranking algorithms, Document Frequency-Weights from regression and Folk Popularity Rank, which can extract tags greatly influencing popularity. We have developed a tag recommendation system using the algorithm we proposed. The recommended tags are not only for appropriate annotations but also for popularity boosting.

Intelligent assistants (IAs) such as Siri and Cortana conversationally interact with users and execute a wide range of actions (e.g., searching the Web, setting alarms, and chatting).IAs can support these actions through the combination of various components such as automatic speech recognition, natural language understanding, and language generation.However, the complexity of these components hinders developers from determining which component causes an error.To remove this hindrance, we focus on reformulation, which is a useful signal of user dissatisfaction, and propose a method to predict the reformulation causes.We evaluate the method using the user logs of a commercial IA.The experimental results have demonstrated that features designed to detect the error of a specific component improve the performance of reformulation cause detection.

Intelligent assistants on mobile devices, such as Siri, have recently gained considerable attention as novel applications of dialogue technologies.A tremendous amount of real users of intelligent assistants provide us with an opportunity to explore a novel task of predicting whether users will continually use their intelligent assistants in the future.We developed prediction models of prospective user engagement by using large-scale user logs obtained from a commercial intelligent assistant.Experiments demonstrated that our models can predict prospective user engagement reasonably well, and outperforms a strong baseline that makes prediction based past utterance frequency.

This paper presents a degree-of-loop assessment method for microvideo clips. Loop video is one of the popular features in microvideo, but there are so many non-loop video tagged with “loop” on microvideo services. This is because upload-ers or spammers also know that loop video is popular and they want to draw attention from viewers. In this paper, we statistically analyze the scene dynamics of the video by using color, optical flow, saliency maps, and evaluate the degree-of-loop. We have collected more than 1,000 video clips from Vine and subjectively evaluated their degree-of-loop. Experimental results show that our proposed algorithm can classify loop/non-loop video with 85.7% accuracy and categorize them into five degree-of-loop categories with 61.5% accuracy.