Leveraging pointwise prediction with learning to rank for top-N recommendation
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Leveraging pointwise prediction with learning to rank for top-N recommendation Nengjun Zhu1 · Jian Cao1 · Xinjiang Lu2 · Qi Gu1 Received: 7 March 2020 / Revised: 12 July 2020 / Accepted: 21 September 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Pointwise prediction and Learning to Rank (L2R) are two hot strategies to model user preference in recommender systems. Currently, these two types of approaches are often considered independently, and most existing efforts utilize them separately. Unfortunately, pointwise prediction tends to cause the problem of overfitting, while L2R is more prone to higher variance. On the other hand, the advantages of multi-task learning and ensemble learning inspire us to utilize multiple approaches jointly so that methods can promote together synergistically. Therefore, we propose a new framework called CPL, where pointwise prediction and L2R are inherently combined to improve the performance of top-N recommendations. To verify the effectiveness of CPL, an instantiation of CPL, which is named CPLmg, is introduced. CPLmg is based on two components, i.e., Factorized SLIM (Sparse LInear Method) and GAPfm (Graded Average Precision factor model), to perform pointwise prediction and L2R, respectively. Different from the original version of SLIM, FSLIM reconstructs a denser representation both for users and items. Moreover, the lowrank users’ and item’s latent factor matrices act as a bridge between FSLIM and GAPfm. Extensive experiments on four real-world datasets show that CPLmg significantly outperforms the compared methods. To explore other possible combinations for CPL further, we
This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering 2019 Guest Editors: Reynold Cheng, Nikos Mamoulis, and Xin Huang Jian Cao
[email protected] Nengjun Zhu zhu [email protected] Xinjiang Lu [email protected] Qi Gu [email protected] 1
Shanghai Institute for Advanced Communication and Data Science, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
2
Baidu Business Intelligence Lab, Baidu Research, Beijing, China
World Wide Web
selected another pointwise method, i.e., FunkSVD, and L2R approach, i.e., BPR, to implement CPLdb. The experimental results demonstrate the superiority of CPL again as it can help improve the performance of its pointwise prediction and L2R components. Keywords Implicit feedback · Personalized recommendation · Collaborative filtering · Learning to Rank · Metrics optimization · Similarity method
1 Introduction Recommender systems have been widely adopted by many online services, since they are able to solve the problem of information overload as well as facilitate interactions between users and systems. Most recommender systems infer users’ interests through users’ historical behaviors, either represented in explicit form or implicit form. Explicit feedbacks, such as ratings, are given by users and they can indicate a users’ interest in a particula
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