Leveraging multi-dimensional user models for personalized next-track music recommendation

Jannach, Dietmar; Kamehkhosh, Iman; Lerche, Lukas

doi:10.1145/3019612.3019756

Cited by 27 publications

(20 citation statements)

References 21 publications

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“…An example of a more recent work is that by Kleinerman et al (2018) who leveraged such a network to generate explanations in the context of reciprocal recommendation scenarios like online dating. Generally, with the increasing number of knowledge sources that become available, HIN-based approaches will continue to gain importance, e.g., to create multi-dimensional user profiles (Jannach et al 2017).…”

Section: Heterogeneous Information Networkmentioning

confidence: 99%

Trends in content-based recommendation

Lops

Jannach

Musto

et al. 2019

User Model User-Adap Inter

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Section: Heterogeneous Information Networkmentioning

confidence: 99%

Trends in content-based recommendation

Lops

Jannach

Musto

et al. 2019

User Model User-Adap Inter

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156

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“…Such techniques are called session-aware according to the terminology of [Quadrana et al 2018]. Examples of such works include [Baeza-Yates et al 2015;Billsus et al 2000;Hariri et al 2012;Jannach et al 2017aJannach et al , 2015aQuadrana et al 2017], and session-aware approaches were applied for various application domains like e-commerce, music, news, or nextapp recommendation. Considering longer-term user preferences in these papers shows to be helpful to improve the recommendations in the current, ongoing session.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of session-based recommendation algorithms

Ludewig

Jannach

2018

User Model User-Adap Inter

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278

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Recommender systems help users find relevant items of interest, for example on e-commerce or media streaming sites. Most academic research is concerned with approaches that personalize the recommendations according to long-term user profiles. In many real-world applications, however, such long-term profiles often do not exist and recommendations therefore have to be made solely based on the observed behavior of a user during an ongoing session. Given the high practical relevance of the problem, an increased interest in this problem can be observed in recent years, leading to a number of proposals for session-based recommendation algorithms that typically aim to predict the user's immediate next actions.In this work, we present the results of an in-depth performance comparison of a number of such algorithms, using a variety of datasets and evaluation measures. Our comparison includes the most recent approaches based on recurrent neural networks like gru4rec, factorized Markov model approaches such as fism or fossil, as well as simpler methods based, e.g., on nearest neighbor schemes. Our experiments reveal that algorithms of this latter class, despite their sometimes almost trivial nature, often perform equally well or significantly better than today's more complex approaches based on deep neural networks. Our results therefore suggest that there is substantial room for improvement regarding the development of more sophisticated session-based recommendation algorithms. 1

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“…We rely on 2 sets of listening sessions. The former, "30Music" [25], composed of listening and playlists data retrieved from Internet radio stations, is open and commonly used for recommendation [2,4,11,26]. The latter is a dataset of listening sessions from Deezer, a French on-demand music streaming service.…”

Section: Datasetsmentioning

confidence: 99%

Word2vec applied to recommendation

Caselles-Dupré

Lesaint²,

Royo-Letelier³

2018

Proceedings of the 12th ACM Conference on Recommender Systems

118

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Skip-gram with negative sampling, a popular variant of Word2vec originally designed and tuned to create word embeddings for Natural Language Processing, has been used to create item embeddings with successful applications in recommendation. While these fields do not share the same type of data, neither evaluate on the same tasks, recommendation applications tend to use the same already tuned hyperparameters values, even if optimal hyperparameters values are often known to be data and task dependent. We thus investigate the marginal importance of each hyperparameter in a recommendation setting through large hyperparameter grid searches on various datasets. Results reveal that optimizing neglected hyperparameters, namely negative sampling distribution, number of epochs, subsampling parameter and window-size, significantly improves performance on a recommendation task, and can increase it by an order of magnitude. Importantly, we find that optimal hyperparameters configurations for Natural Language Processing tasks and Recommendation tasks are noticeably different.

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Leveraging multi-dimensional user models for personalized next-track music recommendation

Cited by 27 publications

References 21 publications

Trends in content-based recommendation

Trends in content-based recommendation

Evaluation of session-based recommendation algorithms

Word2vec applied to recommendation

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