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.

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.

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.

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.

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 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.

Many amateur creators now create derivative works and put them on the web. 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 behavior. Our main contributions are three-fold: (1) to the best of our knowledge, this is the first study modeling derivative creation activity, (2) by using a real-world dataset of music-related derivative work creation to evaluate our model, we showed the effectiveness of adopting all three factors to model derivative creation activity and onsidering creators' browsing behavior, and (3) we carried out qualitative experiments and showed that our model is useful in analyzing derivative creation activity in terms of category characteristics, temporal development of factors that trigger derivative work posting events, etc.

Many people search and watch music videos on video sharing websites. Although a vast variety of videos are uploaded, current search systems on video sharing websites allow users to search a limited range of music videos for an input query. Especially when a user does not have enough knowledge of a query, the problem gets worse because the user cannot customize the range by changing the query or adding some keywords to the original query. In this paper, we propose a music video search system, called ExploratoryVideoSearch, that is coordinate term aware and diversity aware. Our system focuses on artist name queries to search videos on YouTube and has two novel functions: (1) given an artist name query, the system shows a search result for the artist as well as those for its coordinate terms, and (2) the system diversifies search results for the query and its coordinate terms, and allows users to interactively change the diversity level. Coordinate terms are obtained by utilizing the Million Song Dataset and Wikipedia, while search results are diversified based on tags attached to YouTube music videos. ExploratoryVideoSearch enables users to search a wide variety of music videos without requiring deep knowledge about a query.