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

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Preface to Proceedings of the 1st Workshop on Recommender Systems in Technology Enhanced Learning (RecSysTEL 2010)

AbstractTechnology enhanced learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of both individuals and organisations. It is an application domain that generally addresses all types of technology research & development aiming to support teaching and learning activities. Information retrieval is a pivotal activity in TEL, and the deployment of recommender systems has attracted increased interest during the past years.Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. There are plenty of resources available on the Web, both in terms of digital learning content and people resources (e.g. other learners, experts, tutors) that can be used to facilitate teaching and learning tasks. The challenge is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices.The 1st Workshop on Recommender Systems for Technology Enhanced Learning (RecSysTEL) builds upon the tradition of a series of workshops on Social Information Retrieval for Technology Enhanced Learning (SIRTEL), Context-Aware Recommendation for Learning and Towards User Modelling and Adaptive Systems for All (TUMAS-A)a. RecSysTEL was organised jointly by the 4th ACM Conference on Recommender Systems (RecSys 2010) and the 5th European Conference on Technology Enhanced Learning (EC-TEL 2010), on 29–30 September 2010 in Barcelona, Spain. Its main goal was to bring together researchers and practitioners who are working on topics related to the design, development and testing of recommender systems in educational settings as well as present the current status of research in this area and create cross-disciplinary liaisons between the RecSys and ECTEL communities. Overall, its contributions outline the rich potential of TEL as an application area for recommender systems and identify the challenges of developing such systems in a TEL context

A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm

As the growing interest of web recommendation systems those are applied to deliver customized data for their users, we started working on this system. Generally the recommendation systems are divided into two major categories such as collaborative recommendation system and content based recommendation system. In case of collaborative recommen-dation systems, these try to seek out users who share same tastes that of given user as well as recommends the websites according to the liking given user. Whereas the content based recommendation systems tries to recommend web sites similar to those web sites the user has liked. In the recent research we found that the efficient technique based on asso-ciation rule mining algorithm is proposed in order to solve the problem of web page recommendation. Major problem of the same is that the web pages are given equal importance. Here the importance of pages changes according to the fre-quency of visiting the web page as well as amount of time user spends on that page. Also recommendation of newly added web pages or the pages those are not yet visited by users are not included in the recommendation set. To over-come this problem, we have used the web usage log in the adaptive association rule based web mining where the asso-ciation rules were applied to personalization. This algorithm was purely based on the Apriori data mining algorithm in order to generate the association rules. However this method also suffers from some unavoidable drawbacks. In this paper we are presenting and investigating the new approach based on weighted Association Rule Mining Algorithm and text mining. This is improved algorithm which adds semantic knowledge to the results, has more efficiency and hence gives better quality and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table