In recommending songs to users, various types of relationships can be considered, such as songs liked by users with similar preferences or songs that are acoustically similar to those the target user already likes. Providing explanations for recommendations based on such relationships improves transparency and trust, but users currently have no control over which relationships are emphasized. To solve this problem, we extend an existing recommendation method based on a graph convolutional network (GCN) by representing each relationship as a separate graph layer with adjustable weights. By applying this method, we implemented a song recommender system with three types of relationships (user preference similarity, acoustic similarity, and creator commonality) on a music web service called “Kiite.” On the service, four types of recommendation results are displayed, depending on which relationships are emphasized and to what degree. The recommender system offers both transparency and controllability in that users can freely switch between the four recommendation result types. An analysis of over two years of usage logs demonstrates the effectiveness of combining transparency and controllability in music recommendation.

Numerous systems have been proposed for placing songs on a map to enable music exploration, but existing systems assume that users explore alone and thus lack social interactions, which has been identified as a significant issue for these systems. In this paper, we describe "Kiite World," a web service that enables social-aware music exploration. Kiite World has over 440,000 songs placed on a map and lets users perform the following social interactions while moving their avatars: (1) Users can publish "My Kiite World," where songs from their created playlists are displayed on the map, and they can visit each other's "My Kiite Worlds" to explore songs on the map. (2) The activities of all users exploring songs on Kiite World are visualized in real time, enabling users to synchronize with interested users and explore songs while listening to music together. (3) Any user can easily host music events where she listens to her favorite songs together with other users while they synchronize with her. Analysis of user behavior logs over seven months revealed several reusable insights on the usefulness of incorporating social aspects into map-based music exploration (e.g., users often like songs that are farther from their original interests as a result of exploring songs in other users' "My Kiite Worlds.").

In this paper, we elicit implicit information needs that arise during the process of deciding which products to purchase on e-commerce (EC) sites. We designed product purchase tasks to capture implicit information needs, and we conducted a user study to collect utterance data using a think-aloud method. By analyzing the utterances of participants during the tasks, we developed a taxonomy comprising five categories where people express preferences for products and 11 categories where people want to understand products. Our taxonomy includes implicit information needs that have not been captured in existing EC-related taxonomies (e.g., Preference for Subjective Attributes and Understanding Product Differences). We revealed the characteristics of each category of information need in terms of timing during the tasks: e.g., the information need of Understanding Product Range occurred very frequently in the early stage of a task. We also revealed the occurrence frequencies for different task types: e.g., the information needs of Preference for Objective Attributes, Understanding Product Range, and Understanding Terminology had a higher occurrence when purchasing products less frequently and at a higher cost than when purchasing products frequently at a relatively low cost. Our taxonomy could be used to further improve users’ purchasing processes on EC sites.

When a user listens to a song for the first time, what musical factors (e.g., melody, tempo, and lyrics) influence the user's decision to like or dislike the song? An answer to this question would enable researchers to more deeply understand how people interact with music. Thus, in this paper, we report the results of an online survey involving 302 participants to investigate the influence of 10 musical factors. We also evaluate how a user's personal characteristics (i.e., personality traits and musical sophistication) relate to the importance of each factor for the user. Moreover, we propose and evaluate three factor-based functions that would enable more effectively browsing songs on a music streaming service. The user survey results provide several reusable insights, including the following: (1) for most participants, the melody and singing voice are considered important factors in judging whether they like a song on first listen; (2) personal characteristics do influence the important factors (e.g., participants who have high openness and are sensitive to beat deviations emphasize melody); and (3) the proposed functions each have a certain level of demand because they enable users to easily find music that fits their tastes. We have released part of the survey results as publicly available data so that other researchers can reproduce the results and analyze the data from their own viewpoints.

This paper explores the effectiveness of a flexible song recommendation function implemented in a music web service. The function allows users to create recommendation models, which we refer to as intent-based recommendation models (IBRMs), according to their intents. For example, a user can develop IBRMs for “cool songs,” “songs for concentrating on work,” and so on, and receive recommendations from each of the IBRMs according to her intents. The key novelty of this work lies in the architecture that enables users to explicitly construct and maintain multiple personalized recommendation models in parallel, each specialized for a particular intent. This user-driven approach contrasts with conventional systems that rely on a single, system-controlled recommendation model per user. To develop an IBRM, the user first initializes it by choosing seed songs and then repeatedly updates it by giving feedback based on whether recommended songs are relevant to the user’s intent. In the case study using the real-world web service “Kiite,” we analyze 1,116 IBRMs created by 417 users and show key characteristics of those IBRMs (e.g., it is meaningful to enable users to create their own IBRMs, because the created IBRMs generate largely different recommendation results from one another). These findings demonstrate the effectiveness and practical value of enabling users to control intent-specific recommendation behavior through the proposed IBRM framework.

Numerous systems have been proposed for placing songs on a map to enable music exploration, but existing systems assume that users explore alone and thus lack social interactions, which has been identified as a significant issue for these systems. In this paper, we describe "Kiite World," a web service that enables social-aware music exploration. Kiite World has over 440,000 songs placed on a map and lets users perform the following social interactions while moving their avatars: (1) Users can publish "My Kiite World," where songs from their created playlists are displayed on the map, and they can visit each other's "My Kiite Worlds" to explore songs on the map. (2) The activities of all users exploring songs on Kiite World are visualized in real time, enabling users to synchronize with interested users and explore songs while listening to music together. (3) Any user can easily host music events where she listens to her favorite songs together with other users while they synchronize with her. Analysis of user behavior logs over seven months revealed several reusable insights on the usefulness of incorporating social aspects into map-based music exploration (e.g., users often like songs that are farther from their original interests as a result of exploring songs in other users' "My KiiteWorlds.").

Why and how do people view lyrics? Although various lyrics-based music systems have been proposed, this fundamental question remains unexplored. Better understanding of lyrics viewing behavior would be beneficial for both researchers and music streaming platforms to improve their lyrics-based systems. Therefore, in this paper, we investigate why and how people view lyrics, especially when they listen to music on a smartphone. To answer “why,” we conduct a questionnaire-based online user survey involving 206 participants. To answer “how,” we analyze over 23 million lyrics request logs sent from the smartphone application of a music streaming service. Our analysis results suggest several reusable insights, including the following: (1) People have high demand for viewing lyrics to confirm what the artist sings, more deeply understand the lyrics, sing the song, and figure out the structure such as verse and chorus. (2) People like to view lyrics after returning home at night and before going to sleep rather than during the daytime. (3) People usually view the same lyrics repeatedly over time. Applying these insights, we also discuss application examples that could enable people to more actively view lyrics and listen to new songs, which would not only diversify and enrich people's music listening experiences but also be beneficial especially for music streaming platforms.

This paper describes a public web service called Kiite Cafe that lets users get together virtually to listen to music. When users listen to music on Kiite Cafe, their experiences are enhanced by two architectures: (i) visualization of each user's reactions, and (ii) selection of songs from users' favorite songs. These architectures enable users to feel social connection with others and the joy of introducing others to their favorite songs as if they were together listening to music in person. In addition, the architectures provide three user experiences: (1) motivation to react to played songs, (2) the opportunity to listen to a diverse range of songs, and (3) the opportunity to contribute as a curator. By analyzing the behavior logs of 2,399 Kiite Cafe users over a year, we quantitatively show that these user experiences can generate various effects (e.g., users react to a more diverse range of songs on Kiite Cafe than when listening alone). We also discuss how our proposed architectures can enrich music listening experiences with others.

In light of the COVID-19 pandemic making it difficult for people to get together in person, this paper describes a public web service called Kiite Cafe that lets users get together virtually to listen to music. When users listen to music on Kiite Cafe, their experiences are characterized by two architectures: (i) visualization of each user’s reactions, and (ii) selection of songs from users’ favorite songs. These architectures enable users to feel social connection with others and the joy of introducing others to their favorite songs as if they were together in person to listen to music. In addition, the architectures provide three user experiences: (1) motivation to react to played songs, (2) the opportunity to listen to a diverse range of songs, and (3) the opportunity to contribute as curators. By analyzing the behavior logs of 1,760 Kiite Cafe users over about five months, we quantitatively show that these user experiences can generate various effects (e.g., users react to a more diverse range of songs on Kiite Cafe than when listening alone). We also discuss how our proposed architectures can continue to enrich music listening experiences with others even after the pandemic’s resolution.

Why and how do people view lyrics? Although various lyrics-based systems have been proposed in MIR commu-nity, this fundamental question remains unexplored. Better understanding of lyrics viewing behavior would be beneficial for both researchers and music streaming platforms to improve their lyrics-based systems. Therefore, in this paper, we investigate why and how people view lyrics, especially when they listen to music on a smartphone. To answer “why,” we conduct a questionnaire-based online user survey involving 206 participants. To answer “how,” we analyze over 23 million lyrics request logs sent from the smartphone application of a music streaming service. Our analysis results suggest several reusable insights, including the following: (1) People have high demand for viewing lyrics to confirm what the artist sings, more deeply understand the lyrics, sing the song, and figure out the structure such as verse and chorus. (2) People like to view lyrics after returning home at night and before going to sleep rather than during the daytime. (3) People usually view the same lyrics repeatedly over time. Applying these insights, we also discuss application examples that could enable people to more actively view lyrics and listen to new songs, which would not only diversify and enrich people’s music listening experiences but also be beneficial especially for music streaming platforms.

For recommending songs to a user, one effective approach is to represent artists and songs with latent vectors and predict the user's preference toward the songs. Although the latent vectors represent the characteristics of artists and songs well, they have typically been used only for computing the preference score. In this paper, we discuss how we can leverage these vectors for realizing applications that enable users to search for songs from new perspectives. To this end, by embedding song/artist vectors into the same feature space, we first propose two concepts of artist-song relationships: overall similarity and prominent affinity. Overall similarity is the degree to which the characteristics of a song are similar overall to the characteristics of the artist; while prominent affinity is the degree to which a song prominently represents the characteristics of the artist. By using Last.fm play logs for two years, we analyze the characteristics of the concepts. Moreover, based on the analysis results, we propose three applications for song search. Through case studies, we demonstrate that our proposed applications are beneficial for searching for songs according to the users' various search intents.

Displaying appropriate explanations for recommended items is of vital importance for improving the persuasiveness and user satisfaction of recommender systems. Although a user often consumes the same item repeatedly in some domains such as music and restaurants, existing studies have focused on generating explanations for recommending novel items. In this paper, we describe the concept of explainable recommendation for repeatedly consumed items. Because of the high proportion of repeat consumption in music listening, we suggest nine kinds of explanations for song recommendations according to three factors: personal, social, and item factors. From the results of an online survey involving 622 participants, we evaluate the usefulness of these explanations.

For amateur creators, it has been becoming popular to create new content based on existing original work: such new content is called derivative work. We know that derivative creation is popular, but why are individual derivative works created? Although there are several factors that inspire the creation of derivative works, such factors cannot usually be observed on the Web. In this paper, we propose a model for inferring latent factors from sequences of derivative work posting events. We assume a sequence to be a stochastic process incorporating the following three factors: (1) the original work's attractiveness, (2) the original work's popularity, and (3) the derivative work's popularity. To characterize content popularity, we use content ranking data and incorporate rank-biased popularity based on the creators' browsing behaviors. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling derivative creation activity. Second, by using real-world datasets of music-related derivative work creation, we conducted quantitative experiments and showed the effectiveness of adopting all three factors to model derivative creation activity and considering creators' browsing behaviors in terms of the negative logarithm of the likelihood for test data. Third, we carried out qualitative experiments and showed that our model is useful in analyzing following aspects: (1) derivative creation activity in terms of category characteristics, (2) temporal development of factors that trigger derivative work posting events, (3) creator characteristics, (4) N-th order derivative creation process, and (5) original work ranking.

People use web image search with various search intents: from serious demands for work to just passing time by browsing images of a favorite actor. Such a diversity of intents can influence user satisfaction and evaluation metrics, both of which are important factors for providing a better image search environment. In this paper, we investigate this influence by using a publicly available one-month field study dataset. With respect to satisfaction, we take into consideration both query-level and task-level satisfaction provided by search users. Regarding the evaluation metrics, we use grid-based evaluation metrics that incorporate user behavior specific to image search. The results of our analysis indicate that both query/task satisfaction and grid-based evaluation metrics are influenced by the image search intent. Based on the results, we show possibilities to support users' search processes according to their search intents. We also discuss that there is still room for improvement in evaluation metrics through the development of intent-aware evaluation metrics in image search.

In Web services dealing with user-generated content (UGC), a user can have two roles: a role of a consumer and that of a producer. Since most item recommendation models have only considered the role of a user as a consumer, how to leverage the two roles to improve UGC recommendation accuracy has been underexplored. In this paper, based on the state-of-the-art UGC recommendation method called CPRec (consumer and producer based recommendation), we propose ABCPRec (adaptively bridging CPRec). Unlike CPRec, which assumes that the two roles of a user are always related to each other, ABCPRec adaptively bridges the two roles according to the similarity between her nature as a consumer and that as a producer. This enables the model to learn each user's characteristics as both a consumer and a producer and to recommend items to each user more accurately. By using two real-world datasets, we showed that our proposed method significantly outperformed comparative methods in terms of AUC.

In recommender systems, item diversification and explainable recommendations improve users' satisfaction. Unlike traditional explainable recommendations that display a single explanation for each item, explainable hybrid recommendations display multiple explanations for each item and are, therefore, more beneficial for users. When multiple explanations are displayed, one problem is that similar sets of explanation styles (ESs) such as user-based, item-based, and popularity-based may be displayed for similar items. Although item diversification has been studied well, the question of how to diversify the ESs remains underexplored. In this paper, we propose a method for diversifying ESs and a framework, called DualDiv, that recommends items by diversifying both the items and the ESs. Our experimental results show that DualDiv can increase the diversity of the items and the ESs without largely reducing the recommendation accuracy.

In Web services dealing with user-generated content (UGC), a user can have two roles: a role of a consumer and that of a producer. Since most item recommendation models have only considered the role of a user as a consumer, how to leverage the two roles to improve UGC recommendation accuracy has been underexplored. In this paper, based on the state-of-the-art UGC recommendation method called CPRec (consumer and producer based recommendation), we propose ABCPRec (adaptively bridging CPRec). Unlike CPRec, which assumes that the two roles of a user are always related to each other, ABCPRec adaptively bridges the two roles according to the similarity between her nature as a consumer and that as a producer. This enables the model to learn each user's characteristics as both a consumer and a producer and to recommend items to each user more accurately. By using two real-world datasets, we showed that our proposed method significantly outperformed comparative methods in terms of AUC.