Heterogeneous Knowledge-Based Attentive Neural Networks for Short-Term Music Recommendations

Lin, Qika; Niu, Yaoqiang; Zhu, Yifan; Lü, Hao; Mushonga, Keith Zvikomborero; Niu, Zhendong

doi:10.1109/access.2018.2874959

Cited by 43 publications

(21 citation statements)

References 29 publications

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“…As part of future work, we plan to integrate our findings into music recommendation algorithms, with particular attention to addressing the low mainstreaminess group, since standard collaborative filtering approaches tend to fail to provide suitable music recommendations for this user group (Schedl and Hauger, 2015). For example, we plan to integrate the preference values we obtain for a specific user and a particular genre via our approach as a context dimension into a matrix factorization-based approach (Mnih and Salakhutdinov, 2008;Koenigstein et al, 2011) or a deep learning-based approach (Lin et al, 2018;Sachdeva et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Modeling Popularity and Temporal Drift of Music Genre Preferences

Lex¹,

Kowald²,

Schedl

2020

Transactions of the International Society for Music Information Retrieval

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In this paper, we address the problem of modeling and predicting the music genre preferences of users. We introduce a novel user modeling approach, BLL u , which takes into account the popularity of music genres as well as temporal drifts of user listening behavior. To model these two factors, BLL u adopts a psychological model that describes how humans access information in their memory. We evaluate our approach on a standard dataset of Last.fm listening histories, which contains fine-grained music genre information. To investigate performance for different types of users, we assign each user a mainstreaminess value that corresponds to the distance between the user's music genre preferences and the music genre preferences of the (Last.fm) mainstream. We adopt BLL u to model the listening habits and to predict the music genre preferences of three user groups: listeners of (i) niche, low-mainstream music, (ii) mainstream music, and (iii) medium-mainstream music that lies in-between. Our results show that BLL u provides the highest accuracy for predicting music genre preferences, compared to five baselines: (i) group-based modeling, (ii) user-based collaborative filtering, (iii) item-based collaborative filtering, (iv) frequency-based modeling, and (v) recencybased modeling. Besides, we achieve the most substantial accuracy improvements for the low-mainstream group. We believe that our findings provide valuable insights into the design of music recommender systems.

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Section: Discussionmentioning

confidence: 99%

Modeling Popularity and Temporal Drift of Music Genre Preferences

Lex¹,

Kowald²,

Schedl

2020

Transactions of the International Society for Music Information Retrieval

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“…Such heterogeneous data make unifying the model difficult. Therefore, generalized, comprehensive and informative data fusion has far‐reaching implications for meteorological knowledge services (Lin et al , ).…”

Section: Discussion: Challenges and Future Of Social Weathermentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

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Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing-driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary.Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing-based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological 62 | ZHU et al.

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“…知识图谱作为一种通用的推荐系统辅助信息, 近年来也开始被应用到序列化推荐任务中 [12,20,64] . 其中文献 [64] 中, 作者使用一种比较直观的应用方法引入知识图谱的语义信息到基于 RNN 的序列化推荐算法中, 作者使用 3.1 小节中介绍的嵌入方法引入知识图谱的语义信息到物品的表征中, 然后作为双向 RNN 的输入, 最后基于网络的输出进行推荐预测.…”

Section: 序列化推荐系统unclassified

A survey on knowledge graph-based recommender systems

et al. 2020

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Figure 1 (Color online) An illustration of explainable knowledge graph-based recommendation 扮演着至关重要的角色, 用来促进商务业务发展以及辅助决策过程, 并且广泛地应用于各大电子商务 (淘宝、Amazon、eBay 等) 和多媒体 (MovieLens、豆瓣) 等网站中. 个性化推荐算法是推荐系统的核心, 其主要可以被分为 3 类, 即基于内容的推荐算法、基于协同过滤的推荐算法和混合推荐算法 [5]. 其中, 协同过滤方法通过利用用户历史的行为偏好数据构建模型, 取得了巨大的成功 [6∼8]. 这种方法的优势在于不需要类似基于内容的推荐算法那样对物品进行复杂的特征提取与建模. 尽管这类协同过滤方法通常是有效且普适的, 但其依旧存在着多种问题. 主要包括用户和物品之间的行为关系数据的稀疏问题和对新用户或者新物品进行推荐时存在的冷启动问题. 为此, 研究者尝试将协同过滤推荐算法和其他辅助信息相结合 (例如, 用户与物品的属性特征、用户社交网络信息等) 搭建混合推荐系统来解决以上问题, 从而提升推荐效果 [9∼11]. 知识图谱作为混合推荐系统中一类有效的辅助信息, 近年来受到了大量研究者的关注 [2, 10, 12∼15] .

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Heterogeneous Knowledge-Based Attentive Neural Networks for Short-Term Music Recommendations

Cited by 43 publications

References 29 publications

Modeling Popularity and Temporal Drift of Music Genre Preferences

Modeling Popularity and Temporal Drift of Music Genre Preferences

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

A survey on knowledge graph-based recommender systems

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