Compositional Cbr Via Collaborative Filtering (original) (raw)
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Content-based music recommendation based on user preference examples
1st Workshop On Music …, 2010
Recommending relevant and novel music to a user is one of the central applied problems in music information research. In the present work we propose three content-based approaches to this task. Starting from an explicit set of music tracks provided by the user as evidence of his/her music preferences, we infer high-level semantic descriptors, covering different musical facets, such as genre, culture, moods, instruments, rhythm, and tempo. On this basis, two of the proposed approaches employ a semantic music similarity measure to generate recommendations. The third approach creates a probabilistic model of the user's preference in the semantic domain. We evaluate these approaches against two recommenders using state-of-the-art timbral features, and two contextual baselines, one exploiting simple genre categories, the other using similarity information obtained from collaborative filtering. We conduct a listening experiment to assess familiarity, liking and further listening intentions for the provided recommendations. According to the obtained results, we found our semantic approaches to outperform the low-level timbral baselines together with the genre-based recommender. Though the proposed approaches could not reach a performance comparable to the involved collaborative filtering system, they yielded acceptable results in terms of successful novel recommendations. We conclude that the proposed semantic approaches are suitable for music discovery especially in the long tail.
Effective social content-based collaborative filtering for music recommendation
Recently, music recommender systems have been proposed to help users obtain the interested music. Traditional recommender systems making attempts to discover users' musical preferences by ratings always suffer from problems of rating diversity, rating sparsity and lack of ratings. These problems result in unsatisfactory recommendation results. To deal with traditional problems, in this paper, we propose a novel music recommender system, namely Multi-modal Music Recommender system (MMR), which integrates social and collaborative information to predict users' preferences. In this work, the playcounts are transformed into collaborative information to cope with problem of lack of rating information, while item tags and artist tags are employed as social information to cope with problems of rating diversity and rating sparsity. Through optimizing the integrated social-and-collaborative information, the users' preferences can be inferred more accurately and efficiently. The experimental results reveal that, three problems can be alleviated significantly and our proposed method outperforms other state-of-the-art recommender systems in terms of RMSE (Root Mean Square Error) and NDCG (Normalized Discount Cumulative Gain).
Hybrid Approach to Music Recommender Systems
A large number of internet and consumer services applications involve predicting user responses to choices. Some examples include media recommendation systems implemented by Netflix \cite{Netflix} and Spotify \cite{Spotify}. Traditionally, recommender systems have been broadly classified into two categories: Collaborative Filtering and Content-Based Recommendation. We present a novel approach to recommend songs. The goal of the system is to recommend a song that is similar to the user's previously heard songs and is also rated highly by other similar user. We achieve this by using both community ratings and the metadata of songs, thus combining the traditional collaborative filtering and content-based recommendation approaches. We train a deep neural-network to predict ratings for the song and then make recommendations.
Expert Systems with Applications, 2016
The great quantity of music content available online has increased interest in music recommender systems. However, some important problems must be addressed in order to give reliable recommendations. Many approaches have been proposed to deal with cold-start and first-rater drawbacks; however, the problem of generating recommendations for gray-sheep users has been less studied. Most of the methods that address this problem are content-based, hence they require item information that is not always available. Another significant drawback is the difficulty in obtaining explicit feedback from users, necessary for inducing recommendation models, which causes the well-known sparsity problem. In this work, a recommendation method based on playing coefficients is proposed for addressing the above-mentioned shortcomings of recommender systems when little information is available. The results prove that this proposal outperforms other collaborative filtering methods, including those that make use of user attributes.
Context boosting collaborative recommendations* 1
Knowledge-Based Systems, 2004
This paper describes the operation of and research behind a networked application for the delivery of personalised streams of music at Trinity College Dublin. Smart Radio is a web based client-server application that uses streaming audio technology and recommendation techniques to allow users build, manage and share music programmes. Since good content descriptors are difficult to obtain in the audio domain, we originally used automated collaborative filtering, a 'content less' approach as our recommendation strategy. We describe how we improve the ACF technique by leveraging a light content-based technique that attempts to capture the user's current listening 'context'. This involves a two stage retrieval process where ACF recommendations are ranked according to the user's current interests. Finally, we demonstrate a novel on-line evaluation strategy that pits the ACF strategy against the context-boosted strategy in a real time competition. Figure 1. The Smart Radio recommendation panel. Recommended playlists are presented as an ordered list. By default the first playlist is automatically displayed.
Proceedings of the ACM Recommender Systems Challenge 2018 on - RecSys Challenge '18
In this paper we provide an overview of the approach we used as team Creamy Fireflies for the ACM RecSys Challenge 2018. The competition, organized by Spotify, focuses on the problem of playlist continuation, that is suggesting which tracks the user may add to an existing playlist. The challenge addresses this issue in many use cases, from playlist cold start to playlists already composed by up to a hundred tracks. Our team proposes a solution based on a few well known models both content based and collaborative, whose predictions are aggregated via an ensembling step. Moreover by analyzing the underlying structure of the data, we propose a series of boosts to be applied on top of the final predictions and improve the recommendation quality. The proposed approach leverages well-known algorithms and is able to offer a high recommendation quality while requiring a limited amount of computational resources.
IEEE Transactions on Audio, Speech, and Language Processing, 2008
This paper presents a hybrid music recommender system that ranks musical pieces while efficiently maintaining collaborative and content-based data, i.e., rating scores given by users and acoustic features of audio signals. This hybrid approach overcomes the conventional tradeoff between recommendation accuracy and variety of recommended artists. Collaborative filtering, which is used on e-commerce sites, cannot recommend nonbrated pieces and provides a narrow variety of artists. Content-based filtering does not have satisfactory accuracy because it is based on the heuristics that the user's favorite pieces will have similar musical content despite there being exceptions. To attain a higher recommendation accuracy along with a wider variety of artists, we use a probabilistic generative model that unifies the collaborative and content-based data in a principled way. This model can explain the generative mechanism of the observed data in the probability theory. The probability distribution over users, pieces, and features is decomposed into three conditionally independent ones by introducing latent variables. This decomposition enables us to efficiently and incrementally adapt the model for increasing numbers of users and rating scores. We evaluated our system by using audio signals of commercial CDs and their corresponding rating scores obtained from an e-commerce site. The results revealed that our system accurately recommended pieces including nonrated ones from a wide variety of artists and maintained a high degree of accuracy even when new users and rating scores were added. Index Terms-Aspect model, hybrid collaborative and contentbased recommendation, incremental training, music recommender system, probabilistic generative model. I. INTRODUCTION T HE importance of music recommender systems is increasing because many online services that manage large music collections do not provide users with fully satisfactory access to their collections [1], [2]. Standard retrieval systems Manuscript
Context boosting collaborative recommendations
2004
This paper describes the research underpinning a networked application for the delivery of personalised streams of music over the Internet. The initial system used automated collaborative filtering (ACF), a 'content-less' approach to recommend new music to users. We show how we have improved on this basic technique by leveraging a light content-based technique that attempts to capture the user's current listening 'context'.
Enhancing collaborative filtering in music recommender system by using context based approach
Recommender Systems analyse some user and item interactions to help users by recommending the most relevant, feasible and appropriate items from a wide range and pool of items and resources. We propose to enhance the collaborative filtering methodology in recommender systems by considering the content as well as the context based approach towards recommendation. The rating of items and the contextual information of users are expected to enhance the relevance constraint by taking into account the user's mood and activity implicitly by using respective API's to capture and consider the contextual features of the user. The methodology and technique of reduction based approach and user based rating prediction will be used to accomplish the desired results for the proposed recommender system.