TFMAP: optimizing MAP for top-n context-aware recommendation
By Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha A. Larson, Alan Hanjalic, Nuria Oliver. 2012
231 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Information Systems, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
- Harald Steck
- Alexandros Karatzoglou
- Balázs Hidasi
- Gao Cong
- Gert R. G. Lanckriet
- Yong Yu
- Weinan Zhang
- Julian McAuley
In Information Systems
- Xiangnan He
- Julian McAuley
- Alexandros Karatzoglou
- Xiang Wang
- Balázs Hidasi
- Tat-seng Chua
- Weinan Zhang
- Jun Wang
In Management Science and Operations Research
- Julian McAuley
- Xiangnan He
- Alexandros Karatzoglou
- Balázs Hidasi
- Steffen Rendle
- Harald Steck
- Domonkos Tikk
- Yue Shi
Citations in the corpus
- Gaussian Ranking by Matrix Factorization (2015, methodology): “There has been a large body of work on training collaborative filtering approaches as to maximize various ranking metrics, see eg [5, 8, 17, 2, 18, 10, 20, 21, 14, 13, 12].”
- Optimizing top-n collaborative filtering via dynamic negative item sampling (2013, background): “This is essentially different from most ranking-oriented CF algorithms [6, 7, 9] where firstly the target ranking is somehow smoothed strategies.”
- General factorization framework for context-aware recommendations (2014, methodology): “One class of CARS uses latent factor methods (see e.g. Karatzoglou et al. 2010; Rendle and SchmidtThieme 2010; Hidasi and Tikk 2012; Shi et al. 2012; Rendle 2012).”
- Latent Cross: Making Use of Context in Recurrent Recommender Systems (2018, background): “Methods like factorization machines [34] and other contextual recommenders [22, 37, 48] have provided generalizations of these collaborative filtering approaches.”
- Learning to rank for recommender systems (2013, methodology): “Typical examples of those methods are CofiRank [12], CLiMF [10] and TFMAP [9].”
- Top-N Recommendation with Missing Implicit Feedback (2015, background): “However, the Top-N recall is known to be difficult to maximize directly, while it has been shown in several recent works ([10, 7, 4]) that optimizing for AUC may not yield the best results on performance measures such as NDCG or MAP which focus on the top of the ranking.”
- A Generic Coordinate Descent Framework for Learning from Implicit Feedback (2016, background): “Both approaches are considered state-of-the-art and while CD outperforms BPR on certain datasets [8, 25, 15, 22, 26], BPR has been shown to work better on others [4, 17, 16, 8].”
- Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation (2015, background): “It gives equal weights to each item pair [19, 21].”
- Recommender Systems (2016, unknown): “Numerous other methods have been proposed for incorporating context into such methods [549].”