Sequence-Aware Recommender Systems

Quadrana, Massimo; Cremonesi, Paolo; Jannach, Dietmar

doi:10.1145/3190616

Cited by 379 publications

(224 citation statements)

References 104 publications

(228 reference statements)

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“…Most research on the topic targets either next track recommendation (also known as next song recommendation) or, more generally, automatic playlist continuation (APC), cf. [42,43]. Both tasks constitute of learning a model from existing playlists or listening sessions, but they differ in terms of output.…”

Section: Sequence-aware Music Recommendationmentioning

confidence: 99%

Deep Learning in Music Recommendation Systems

Schedl

2019

Front. Appl. Math. Stat.

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Like in many other research areas, deep learning (DL) is increasingly adopted in music recommendation systems (MRS). Deep neural networks are used in this domain particularly for extracting latent factors of music items from audio signals or metadata and for learning sequential patterns of music items (tracks or artists) from music playlists or listening sessions. Latent item factors are commonly integrated into content-based filtering and hybrid MRS, whereas sequence models of music items are used for sequential music recommendation, e.g., automatic playlist continuation. This review article explains particularities of the music domain in RS research. It gives an overview of the state of the art that employs deep learning for music recommendation. The discussion is structured according to the dimensions of neural network type, input data, recommendation approach (content-based filtering, collaborative filtering, or both), and task (standard or sequential music recommendation). In addition, we discuss major challenges faced in MRS, in particular in the context of the current research on deep learning.

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Section: Sequence-aware Music Recommendationmentioning

confidence: 99%

Deep Learning in Music Recommendation Systems

Schedl

2019

Front. Appl. Math. Stat.

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“…Recently, recurrent neural network (RNN) [17] and its variants (e.g., long-short term memory (LSTM) [18] and gated recurrent unit (GRU) [19,20]) have been successfully applied to sequential recommender systems [21]. The hidden states of RNN methods have both characteristics in nature and are adequate for modeling sequential correlations and temporal dynamics in POI recommender systems [8,22], and they can better capture the long-term dependency.…”

Section: Introductionmentioning

confidence: 99%

“…An excellent choice is to apply RNN and incorporate additional spatiotemporal contextual information, such as continuous geographical distance and time interval. Most RNN methods rely on the last hidden layer activation vector when calculating the output of the network, and this limits the ability to understand and learn the main intention of user check-in behavior from the hidden states [21]. In other words, the historical check-in behaviors of a user are not equally important for predicting the next behavior, and we need to focus on the main information.…”

Section: Introductionmentioning

confidence: 99%

An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

Liu

Wang

et al. 2019

IJGI

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Point-of-interest (POI) recommendation is one of the fundamental tasks for location-based social networks (LBSNs). Some existing methods are mostly based on collaborative filtering (CF), Markov chain (MC) and recurrent neural network (RNN). However, it is difficult to capture dynamic user's preferences using CF based methods. MC based methods suffer from strong independence assumptions. RNN based methods are still in the early stage of incorporating spatiotemporal context information, and the user's main behavioral intention in the current sequence is not emphasized. To solve these problems, we proposed an attention-based spatiotemporal gated recurrent unit (ATST-GRU) network model for POI recommendation in this paper. We first designed a novel variant of GRU, which acquired the user's sequential preference and spatiotemporal preference by feeding the continuous geographical distance and time interval information into the GRU network in each time step. Then, we integrated an attention model into our network, which is a personalized process and can capture the user's main behavioral intention in the user's check-in history. Moreover, we conducted an extensive performance evaluation on two real-world datasets: Foursquare and Gowalla. The experimental results demonstrated that the proposed ATST-GRU network outperforms the existing state-of-the-art POI recommendation methods significantly regarding two commonly-used evaluation metrics.

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“…Fashion item attributes can be created by manual tagging or by automated tagging extracted from images. Customer preference is expressed in text or in images and customer context also constitutes a part of attribute data [9,[11][12][13][14].On the other hand, BBRs, such as various Collaborative Filtering Recommenders (CFRs) [15,16], use user-item interactions when creating recommendation models. CFRs are categorized into Item-Based CFRs (IBCFRs) and User-Based CFRs (UBCFRs).…”

mentioning

confidence: 99%

“…On the other hand, BBRs, such as various Collaborative Filtering Recommenders (CFRs) [15,16], use user-item interactions when creating recommendation models. CFRs are categorized into Item-Based CFRs (IBCFRs) and User-Based CFRs (UBCFRs).…”

mentioning

confidence: 99%

Session-Based Recommender System for Sustainable Digital Marketing

Hwangbo

Kim

2019

Sustainability

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Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.

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Sequence-Aware Recommender Systems

Cited by 379 publications

References 104 publications

Deep Learning in Music Recommendation Systems

Deep Learning in Music Recommendation Systems

An Attention-Based Spatiotemporal Gated Recurrent Unit Network for Point-of-Interest Recommendation

Session-Based Recommender System for Sustainable Digital Marketing

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