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

Group Recommendations: Survey and Perspectives

The popularity of group recommender systems has increased in the last years. More and more social activity is generated by users over the Web and thus not only domains as TV, music or holidays are used and researched anymore for group recommendation, but also collaborative learning support, digital libraries and other domains seems to be promising for group recommendation. Moreover, principles of group recommenders can be used in order to overcome some single user recommendation shortcomings, such as cold start problem. Numerous group recommenders have been proposed, they differ in application domains which are specific in group characteristics. Today's group recommenders do not include and use the power of social aspects (group structure, social status etc.), which can be extracted and derived from the group. We provide a survey of group recommendation principles for the Web domain and discuss trends and perspectives in this field

A Context-Aware Mobile Recommender System for Places of Interest

In this paper we introduce a novel setting mindful portable recommender framework for spots of intrigue (POIs). Not at all like existing frameworks, which gain clients' inclinations exclusively from their past evaluations, has it considered additionally their identity - utilizing the Five Factor Model. Identity is gained by requesting that clients finish a brief and engaging poll as a major aspect of the enlistment procedure, and is then misused in: (1) a dynamic learning module that effectively obtains evaluations in-setting for POIs that clients are probably going to have encountered, consequently diminishing the anxiety and inconvenience to rate (or skip rating) things that the clients don't have a clue; and (2) in the suggestion display that develops on network factorization and in this manner can be prepared regardless of the possibility that the clients haven't appraised any things yet

Techniques for cold-starting context-aware mobile recommender systems for tourism

Abstract. Novel research works in recommender systems have illustrated the benefits of exploiting contextual information, such as the time and location of a suggested place of interest, in order to better predict the user ratings and produce more relevant recommendations. But, when deploying a context-aware system one must put in place techniques for operating in the cold-start phase, i.e., when no or few ratings are available for the items listed in the system catalogue and it is therefore hard to predict the missing ratings and compose relevant recommendations. This problem has not been directly tackled in previous research. Hence, in order to address it, we have designed and implemented several novel algorithmic components and interface elements in a fully operational points of interest (POI) mobile recommender system (STS). In particular, in this article we illustrate the benefits brought by using the user personality and active learning techniques. We have developed two extended versions of the matrix factorisation algorithm to identify what items the users could and should rate and to compose personalised recommendations. While context-aware recommender systems have been mostly evaluated offline, a testing scenario that suffers from many limitations, in our analysis we evaluate the proposed system in live user studies where the graphical user interface and the full interaction design play a major role. We have measured the system effectiveness in terms of several metrics such as: the quality and quantity of acquired ratings-in-context, the recommendation accuracy (MAE), the system precision, the perceived recommendation quality, the user choice satisfaction, and the system usability. The obtained results confirm that the proposed techniques can effectively overcome the identified cold-start problem

Parsimonious and Adaptive Contextual Information Acquisition in Recommender Systems

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Context-Aware Recommender System (CARS) models are trained on datasets of context-dependent user preferences (ratings and context information). Since the number of context-dependent preferences increases exponentially with the number of contextual factors, and certain contextual in- formation is still hard to acquire automatically (e.g., the user's mood or for whom the user is buying the searched item) it is fundamental to identify and acquire those factors that truly in uence the user preferences and the ratings. In particular, this ensures that (i) the user e ort in specifying contextual information is kept to a minimum, and (ii) the system's performance is not negatively impacted by irrele- vant contextual information. In this paper, we propose a novel method which, unlike existing ones, directly estimates the impact of context on rating predictions and adaptively identi es the contextual factors that are deemed to be useful to be elicited from the users. Our experimental evaluation shows that it compares favourably to various state-of-the-art context selection methods

Investigation of an intelligent personalised service recommendation system in an IMS based cellular mobile network

Success or failure of future information and communication services in general and mobile communications in particular is greatly dependent on the level of personalisations they can offer. While the provision of anytime, anywhere, anyhow services has been the focus of wireless telecommunications in recent years, personalisation however has gained more and more attention as the unique selling point of mobile devices. Smart phones should be intelligent enough to match user’s unique needs and preferences to provide a truly personalised service tailored for the individual user. In the first part of this thesis, the importance and role of personalisation in future mobile networks is studied. This is followed, by an agent based futuristic user scenario that addresses the provision of rich data services independent of location. Scenario analysis identifies the requirements and challenges to be solved for the realisation of a personalised service. An architecture based on IP Multimedia Subsystem is proposed for mobility and to provide service continuity whilst roaming between two different access standards. Another aspect of personalisation, which is user preference modelling, is investigated in the context of service selection in a multi 3rd party service provider environment. A model is proposed for the automatic acquisition of user preferences to assist in service selection decision-making. User preferences are modelled based on a two-level Bayesian Metanetwork. Personal agents incorporating the proposed model provide answers to preference related queries such as cost, QoS and service provider reputation. This allows users to have their preferences considered automatically

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design