Music Recommender Systems

Recommendation becomes a mainstream feature in nowadays e-commerce because of its significant contributions in promoting revenue and customer satisfaction. Given hundreds of millions of user activity logs and product items, accurate and efficient recommendation is a challenging computational task. This paper introduces a new soft hierarchical clustering algorithm-Fuzzy Hierarchical Co-clustering (FHCC) algorithm, and applies this algorithm to detect user-product joint groups from users' behavior data for collaborative filtering recommendation. Via FHCC, complex relations among different data sources can be analyzed and understood comprehensively. Besides, FHCC is able to adapt to different types of applications according to the accessibility of data sources by carefully adjust the weights of different data sources. Experimental evaluations are performed on a benchmark rating dataset to extract user-product co-clusters. The results show that our proposed approach provide more meaningful recommendation results, and outperforms existing item-based and user-based collaborative filtering recommendations in terms of accuracy and ranked position.

Music recommender systems (MRSs) 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 MRSs 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 toward 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.

Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems, has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation and classification of that research. Because of this, we reviewed articles on recommender structures, and then classified those based on sentiment analysis. The articles are categorized into three techniques of recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to find out the research papers related to sentimental analysis based recommender system. To classify research done by authors in this field, we have shown different approaches of recommender system based on sentimental analysis with the help of tables. Our studies give statistics, approximately trends in recommender structures research, and gives practitioners and researchers with perception and destiny route on the recommender system using sentimental analysis. We hope that this paper enables all and sundry who is interested in recommender systems research with insight for destiny.

Recommender system is a helpful tool for helping the user in cutting the time needs to find personalized products, documents, friends, places and services. In addition, the recommender system handles the century web problem: information overload. In the same time, many environments or technologies (i.e. cloud, mobile, social network) become popular today and facing the problem of large amount of information. Therefore, the researchers recognize that the recommender system is a suitable solution to this problem in those environments. This paper, reviews the recent research papers that were applied the recommender system in mobile, social network, or cloud environment. We classify these recommender systems into four groups (i.e. mobile recommender system, social recommender system, cloud recommender system and traditional (PC) recommender system) depending on technology or environment that the RS is applied in. This survey presents some compression, advantages and challenges of these types of recommender systems. Also, it will directly support researchers and professionals in their understanding of those types of recommender systems.

A prerequisite for implementing collaborative filtering recommender systems is the availability of users' preferences data. This data, typically in the form of ratings, is exploited to learn the tastes of the users and to serve them with personalized recommendations. However, there may be a lack of preference data, especially at the initial stage of the operations of a recommender system, i.e., in the Cold Start phase. In particular, when a new user has not yet rated any item, the system would be incapable of generating relevant recommendations for this user. Or, when a new item is added to the system catalogue and no user has rated it, the system cannot recommend this item to any user. This chapter discusses the cold start problem and provides a comprehensive description of techniques that have been proposed to address this problem. It surveys algorithmic solutions and provides a summary of their performance comparison. Moreover, it lists publicly available resources (e.g., libraries and datasets) and offers a set of practical guidelines that can be adopted by researchers and practitioners.

Information overload is a new challenge in e-commerce sites. The problem refers to the fast growing of information that lead following the information flow in real world be impossible. Recommender systems, as the most successful application of information filtering, help users to find items of their interest from huge datasets. Collaborative filtering, as the most successful technique for recommendation, utilises social behaviours of users to detect their interests. Traditional challenges of Collaborative filtering, such as cold start, sparcity problem, accuracy and malicious attacks, derived researchers to use new metadata to improve accuracy of recommenders and solve the traditional problems. Trust based recommender systems focus on trustworthy value on relation among users to make more reliable and accurate recommends. In this paper our focus is on trust based approach and discuss about the process of making recommendation in these method. Furthermore, we review different proposed trust metrics, as the most important step in this process.

Abstract. We describe a new recommender system for the Russian interactive radio network FMhost. The new recommender model combines collaborative and user-based approaches. The system extracts information from tags of listened tracks for matching user and radio station profiles and follows an adaptive online learning strategy based on user history. We also provide some basic examples and describe the quality of service evaluation methodology.

This paper shows how image content can be used to realize a shopping recommender system for intuitively supporting mobile users in decision making. A mobile user equipped with a camera enabled smart phone combined with Global Positioning System (GPS) capabilities would benefit in using a recommender system for mobile users. This recommender system is queried by image sent by a smart phone together with the smart phone's GPS coordinates then the system returns a recommended retail shop together with its GPS coordinates, the image similar to the query image and other items on special offer. This recommender system shows a drastic reduction if not elimination of usage of text by mobile users using mobile devices when accessing the system. This paper presents the proposed recommender system and the simulated results of the recommender system. In summary the main contribution of this paper is to show how image retrieval, image content and camera enabled smart mobile device with GPS capabilities can be used to realize a location-based shopping recommender system for mobile users. K EY W ORDS Recommender systems, image content, GPS, smart phone, e-commerce, image retrieval

The gigantic growth of information on the Internet makes discovery information challenging and time consuming. We are encircled by a plethora of data in the form of blogs, papers, reviews, and comments on different websites. Recommender systems endow a solution to this situation by automatically capturing user interests and recommending respective information the user may also find relevant. The purpose of developing recommender systems is to detract information overload by retrieving the most pertinent knowledge and services from an enormous amount of data, thereby providing personalized services. The most vital feature of a recommender system is its proficiency to "supposition" a user's preferences and interests by examining the behavior of this user and/or the behavior of other users to originate personalized recommendations. So several research works have been done in this area, but nothing consolidated has been appraised. In this paper, we are going to discuss a brief summary of imperfection in the available recommender system. We are also trying to figure out these shortcomings of the available recommender system to generate a new method that improves these shortcomings.

his paper provides an outline of the algorithms submitted for the WSDM Cup 2019 Spotify Sequential Skip Prediction Challenge (team name: mimbres). In the challenge, complete information including acoustic features and user interaction logs for the first half of a listening session is provided. Our goal is to predict whether the individual tracks in the second half of the session will be skipped or not, only given acoustic features. We proposed two different kinds of algorithms that were based on metric learning and sequence learning. The experimental results showed that the sequence learning approach performed significantly better than the metric learning approach. Moreover, we conducted additional experiments to find that significant performance gain can be achieved using complete user log information.