Local implicit feedback mining for music recommendation

Yang, Diyi; Chen, Tianqi; Zhang, Weinan; Lu, Qiuxia; Yu, Yong

doi:10.1145/2365952.2365973

Cited by 35 publications

(28 citation statements)

References 28 publications

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“…ey move the underlying model from a matrix completion task toward a Markov Decision Process (MDP). Businesses encounter session-based recommender systems in domains including music [38], products [18], and news [23]. Deep learning architectures have been applied to session-based recommendation (cf.…”

Section: Session-based Recommendationmentioning

confidence: 99%

Recommending Personalized News in Short User Sessions

Epure

Kille

Ingvaldsen

et al. 2017

Proceedings of the Eleventh ACM Conference on Recommender Systems

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News organizations employ personalized recommenders to target news articles to speci c readers and thus foster engagement. Existing approaches rely on extensive user pro les. However frequently possible, readers rarely authenticate themselves on news publishers' websites. is paper proposes an approach for such cases. It provides a basic degree of personalization while complying with the key characteristics of news recommendation including news popularity, recency, and the dynamics of reading behavior. We extend existing research on the dynamics of news reading behavior by focusing both on the progress of reading interests over time and their relations. Reading interests are considered in three levels: short-, medium-, and long-term. Combinations of these are evaluated in terms of added value to the recommendation's performance and ensured news variety. Experiments with 17-month worth of logs from a German news publisher show that most frequent relations between news reading interests are constant in time but their probabilities change. Recommendations based on combined shortterm and long-term interests result in increased accuracy while recommendations based on combined short-term and medium-term interests yield higher news variety.

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Section: Session-based Recommendationmentioning

confidence: 99%

Recommending Personalized News in Short User Sessions

Epure

Kille

Ingvaldsen

et al. 2017

Proceedings of the Eleventh ACM Conference on Recommender Systems

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“…Compared with traditional SVD++ and Original AutoSVD++, our e cient training algorithm achieves a signi cant reduction in time complexity. Generally, the optimized AutoSVD++ performs R times be er than original AutoSVD++, whereR denotes the average number of items rated by users [13]. Meanwhile, compared with biased SVD model, the incorporated items Cae(C i ) and o set ϵ i does not drag down the training e ciency.…”

Section: Overall Comparisonmentioning

confidence: 99%

AutoSVD++

Zhang

Yao

2017

Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

110

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Collaborative ltering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such as combining with content based ltering and leveraging side information of users and items has been extensively studied to enhance performance. However, most of these approaches depend on hand-cra ed feature engineering, which is usually noise-prone and biased by di erent feature extraction and selection schemes. In this paper, we propose a new hybrid model by generalizing contractive auto-encoder paradigm into matrix factorization framework with good scalability and computational e ciency, which jointly models content information as representations of e ectiveness and compactness, and leverage implicit user feedback to make accurate recommendations. Extensive experiments conducted over three large-scale real datasets indicate the proposed approach outperforms the compared methods for item recommendation.

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“…al. [15] suggested to use implicit feedback collected from a user during a short time period to extract her local preference, which is to represent her taste during the next few minutes. It has been shown that implicit feedback from users can be used for news recommendation [7], and Parra et al [16] showed a strong relation between users' implicit feedback and explicit ratings.…”

Section: Related Workmentioning

confidence: 99%

Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback

Nguyễn

Almenningen

Havig

et al. 2014

Lecture Notes in Computer Science

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Abstract. Fashion e-commerce is a fast growing area in online shopping. The fashion domain has several interesting properties, which make personalised recommendations more difficult than in more traditional domains. To avoid potential bias when using explicit user ratings, which are also expensive to obtain, this work approaches fashion recommendations by analysing implicit feedback from users in an app. A user's actual behaviour, such as Clicks, Wants and Purchases, is used to infer her implicit preference score of an item she has interacted with. This score is then blended with information about the item's price and popularity as well as the recentness of the user's action wrt. the item. Based on these implicit preference scores, we infer the user's ranking of other fashion items by applying different recommendation algorithms. Experimental results show that the proposed method outperforms the most popular baseline approach, thus demonstrating its effectiveness and viability.

show abstract

Local implicit feedback mining for music recommendation

Cited by 35 publications

References 28 publications

Recommending Personalized News in Short User Sessions

Recommending Personalized News in Short User Sessions

AutoSVD++

Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback

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