Improving Music Recommendation in Session-Based Collaborative Filtering by Using Temporal Context

Dias, Ricardo; Fonseca, Manuel J.

doi:10.1109/ictai.2013.120

Cited by 53 publications

(27 citation statements)

References 16 publications

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“…The application fields of context-aware recommenders include among others: point-of-interest [4], video [33], music [8], and news recommendation [20]. Context-aware recommender approaches can be classified into three main groups: prefiltering, postfiltering, and contextual modeling [3].…”

Section: Related Workmentioning

confidence: 99%

Speeding up ALS learning via approximate methods for context-aware recommendations

Hidasi

Tikk²

2015

Knowl Inf Syst

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Implicit feedback-based recommendation problems, typically set in real-world applications, recently have been receiving more attention in the research community. From the practical point of view, scalability of such methods is crucial. However, factorization-based algorithms efficient in explicit rating data applied directly to implicit data are computationally inefficient; therefore, different techniques are needed to adapt to implicit feedback. For alternating least squares (ALS) learning, several research contributions have proposed efficient adaptation techniques for implicit feedback. These algorithms scale linearly with the number of nonzero data points, but cubically in the number of features, which is a computational bottleneck that prevents the efficient usage of accurate high factor models. Also, map-reduce type big data techniques are not viable with ALS learning, because there is no known technique that solves the high communication overhead required for random access of the feature matrices. To overcome this drawback, here we present two generic approximate variants for fast ALS learning, using conjugate gradient (CG) and coordinate descent (CD). Both CG and CD can be coupled with all methods using ALS learning. We demonstrate the advantages of fast ALS variants on iTALS, a generic context-aware algorithm, which applies ALS learning for tensor factorization on implicit data. In the experiments, we compare the approximate techniques with the base ALS learning in terms of training time, scalability, recommendation accuracy, and convergence. We show that the proposed solutions offer a trade-off between recommendation accuracy and speed of training time; this makes it possible to apply ALS-based methods efficiently even for billions of data points.

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Section: Related Workmentioning

confidence: 99%

Speeding up ALS learning via approximate methods for context-aware recommendations

Hidasi

Tikk²

2015

Knowl Inf Syst

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“…Thirdly, some slight differences in styles and genres of music pieces are also shown by the learned embeddings, which shows that the learned embeddings by MEM can effectively capture the accurate features of the corresponding music pieces. For example, as for the last four music pieces (13)(14)(15)(16), all of which are soundtracks for anime, and they are more similar to each other than the other pieces (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12) in Table 8. In addition, the former two pieces (13)(14) are more similar to each other than the latter two pieces (15)(16) in Table 8.…”

Section: Illustrations Of Selected Music Pieces' Embeddingsmentioning

confidence: 99%

“…Consequently, music recommendation approaches with environment-related parameters perform better than those without considering contextual information. The environment-related contexts include time [10], location [17], weather [26] and hybrid context [40]. Kaminskas and Ricci [17] explored the possibilities of adapting music to the place of interests that the users are visiting.…”

Section: Environment-related Context Based Approachesmentioning

confidence: 99%

“…Kaminskas and Ricci [17] explored the possibilities of adapting music to the place of interests that the users are visiting. In [10], the authors incorporated temporal information in session-based collaborative filtering method to improve the performance of music recommendation. Park et al [26] presented a context-aware music recommender, which utilized several kinds of context information, including noise, light level, weather, and time.…”

Section: Environment-related Context Based Approachesmentioning

confidence: 99%

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Learning to embed music and metadata for context-aware music recommendation

et al. 2017

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Contextual factors greatly influence users' musical preferences, so they are beneficial remarkably to music recommendation and retrieval tasks. However, it still needs to be studied how to obtain and utilize the contextual information. In this paper, we propose a context-aware music recommendation approach, which can recommend music pieces appropriate for users' contextual preferences for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require music pieces to be represented by features ahead, but it can learn the representations from users' historical listening records. Specifically, the proposed approach first learns music pieces' embeddings (feature vectors in low-dimension continuous space) from music listening records and corresponding metadata. Then it infers and models users' global and contextual preferences for music from their listening records with the learned embeddings. Finally, it recommends appropriate music pieces according to the target user's preferences to satisfy her/his real-time requirements. Experimental evaluations on a real-world dataset show that the proposed approach outperforms baseline methods in terms of precision, recall, F1 score, and hitrate. Especially, our approach has better performance on sparse datasets.

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“…Liu et al [34] present a social temporal collaborative ranking model that can simultaneously achieve three objectives: (1) combines both explicit and implicit user feedback, (2) supports time awareness using an expressive sequential matrix factorization model and a temporal smoothness regularization function to tackle overfitting, and (3) supports social network awareness by incorporating a network regularization term. Dias and Fonseca [35] explore the usage of temporal context and session diversity in session-based CF techniques for music recommendation. They compare two techniques to capture the users' listening patterns over time: one explicitly extracts temporal properties and session diversity, to group and compare the similarity of sessions, the other uses a generative topic modeling algorithm, which is able to implicitly model temporal patterns.…”

Section: Related Workmentioning

confidence: 99%

Using Time Clusters for Following Users’ Shifts in Rating Practices

Margaris

Vassilakis

2018

J. Complex Syst. Inform. Model. Q.

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Abstract. Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user's rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users' ratings is inadequate, since it fails to follow such shifts in users' rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in useruser collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users' rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.

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Improving Music Recommendation in Session-Based Collaborative Filtering by Using Temporal Context

Cited by 53 publications

References 16 publications

Speeding up ALS learning via approximate methods for context-aware recommendations

Speeding up ALS learning via approximate methods for context-aware recommendations

Learning to embed music and metadata for context-aware music recommendation

Using Time Clusters for Following Users’ Shifts in Rating Practices

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