Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

User Curiosity Factor in Determining Serendipity of Recommender System

Recommender rystem (RS) is created to solve the problem by recommending some items among a huge selection of items that will be useful for the e-commerce users. RS prevents the users from being flooded by information that is irrelevant for them.Unlike information retrieval (IR) systems, the RS system's goal is to present information to the users that is accurate and preferably useful to them. Too much focus on accuracy in RS may lead to an overspecialization problem, which will decrease its effectiveness. Therefore, the trend in RS research is focusing beyond accuracy methods, such as serendipity. Serendipity can be described as an unexpected discovery that is useful. Since the concept of a recommendation system is still evolving today, formalizing the definition of serendipity in a recommendation system is very challenging.One known subjective factor of serendipity is curiosity. While some researchers already addressed curiosity factor, it is found that the relationships between various serendipity component as perceived by the users and their curiosity levels is still yet to be researched. In this paper, the method to determine user curiosity model by considering the variation of rated items was presented, then relation to serendipity components using existing user feedback data was validated. The finding showed that the curiosity model was related to some user-perceived values of serendipity, but not all. Moreover, it also had positive effect on broadening the user preference.

Recommender systems and their ethical challenges

This article presents the first, systematic analysis of the ethical challenges posed by recommender systems through a literature review. The article identifies six areas of concern, and maps them onto a proposed taxonomy of different kinds of ethical impact. The analysis uncovers a gap in the literature: currently user-centred approaches do not consider the interests of a variety of other stakeholders—as opposed to just the receivers of a recommendation—in assessing the ethical impacts of a recommender system

Serendipitous News Discovery Increases News Consumption in News Recommender Systems

News recommender system users obtain news via incidental exposure to news and experience serendipity in the incidental news consumption. Serendipitous news discovery, the same as serendipity, refers to discovering unexpected and useful information unintentionally. Researchers suggest building serendipitous news recommender systems and increasing serendipitous news discovery to increase the diversity of the news consumption. However, the impacts of serendipitous news discovery on news consumption are uninvestigated, and rare research provides theoretical guidance to the serendipitous news recommender systems. The thesis investigated the impacts of serendipitous news discovery on news consumption with a serendipityrelated emotion, surprise, as a mediator and need for activation as a moderator. 463 participants recruited from Amazon MTurk completed the online survey-experiment. The findings suggest that surprise mediates the correlations between serendipitous news discovery and news consumption. Users who experience higher serendipitous news discovery indicate more positive attitudes on news consumption in the news recommender systems. The results also indicate the possibility that the lack of constant serendipitous news discovery may lead to the consumption of the news similar to the news that trigger serendipity. The research suggests that serendipitous news discovery increases news consumption, including news selection and reading