Abstract 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.

Abstract 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.”).

Abstract 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.

Social networks play important roles in the Semantic Web: knowledge management, information retrieval, ubiquitous computing, and so on. We propose a social network extraction system called POLYPHONET, which employs several advanced techniques to extract relations of persons, detect groups of persons, and obtain keywords for a person. Search engines, especially Google, are used to measure co-occurrence of information and obtain Web documents.Several studies have used search engines to extract social networks from the Web, but our research advances the following points: First, we reduce the related methods into simple pseudocodes using Google so that we can build up integrated systems. Second, we develop several new algorithms for social networking mining such as those to classify relations into categories, to make extraction scalable, and to obtain and utilize person-to-word relations. Third, every module is implemented in POLYPHONET, which has been used at four academic conferences, each with more than 500 participants. We overview that system. Finally, a novel architecture called Super Social Network Mining is proposed; it utilizes simple modules using Google and is characterized by scalability and Relate-Identify processes: Identification of each entity and extraction of relations are repeated to obtain a more precise social network.