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TFMAP

Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Alan Hanjalic, Nuria Oliver
2012 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12  
a {y.shi, m.a.larson, a.hanjalic}@tudelft.nl, b {alexk, linas, nuriao}@tid.es ABSTRACT In this paper, we tackle the problem of top-N context-aware recommendation for implicit feedback scenarios.  ...  We propose TFMAP, a model that directly maximizes Mean Average Precision in aim of creating an optimally ranked list of items for individual users under a given context.  ...  ACKNOWLEDGMENTS We are very grateful to Matthias Böhmer for making the Appazzar data available.  ... 
doi:10.1145/2348283.2348308 dblp:conf/sigir/ShiKBLHO12 fatcat:idfktcbd3zbzro5ktfdvqtqgmy

Context Aware Recommender System Algorithms: State of the Art and Focus on Factorization Based Methods

Fatima Zahra Lahlou, Houda Benbrahim, Ismail Kassou
2017 Electronic Journal of Information Technology  
Then we study factorization models used for the Context Aware Recommendation task and suggest some possible research directions for developing more performing contextual modeling CARS algorithms.  ...  Context Aware Recommender Systems (CARS) have become an important research area since its introduction in 2001 by (Herlocker and Konstan, 2001) and (Adomavicius and Tuzhilin, 2001).  ...  top-N recommendation for the case of implicit feedback scenarios.  ... 
doaj:5a26071411994898b1c4c07928e7d0b8 fatcat:ufs7xpmpyfd5topui64ynqwfvu

LambdaFM

Fajie Yuan, Guibing Guo, Joemon M. Jose, Long Chen, Haitao Yu, Weinan Zhang
2016 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16  
the implicit feedback based context-aware recommendation problem (IFCAR).  ...  Hence, instead of directly adopting the original lambda strategy, we create three effective lambda surrogates by conducting a theoretical analysis for lambda from the top-N optimization perspective.  ...  for context-aware recommendations.  ... 
doi:10.1145/2983323.2983758 dblp:conf/cikm/YuanGJCYZ16 fatcat:7a6otupgzvcydgvmnudw5i7ba4

Diversity-Ensured Semantic Movie Recommendation by Applying Linked Open Data

Uma Srinivasan, Chidambaram Mani
2018 International Journal of Intelligent Engineering and Systems  
By extracting the semantic-path based features from the user rating-centric graph, it executes the ranking algorithm for the top-N movie recommendation.  ...  Moreover, the diversity-aware re-ranking tends to maintain the trade-off between the diversity and accuracy in the top-N recommendation.  ...  Context-aware movie recommendation model [14] is based on the DBpedia source, presents a movie recommendation tool for mobile applications.  ... 
doi:10.22266/ijies2018.0430.30 fatcat:u2rrfiupmjesrm75mmeqn5f2xy

Improving Personalized Ranking in Recommender Systems with Topic Hierarchies and Implicit Feedback

Marcelo Garcia Manzato, Marcos Aurelio Domingues, Ricardo Marcondes Marcacini, Solange Oliveira Rezende
2014 2014 22nd International Conference on Pattern Recognition  
The knowledge of semantic information about the content and user's preferences is an important issue to improve recommender systems.  ...  In this paper, we mitigate this problem by proposing a recommendation model based on latent factors and implicit feedback which uses an unsupervised topic hierarchy constructor algorithm to organize and  ...  , Shi et al. propose a new context-aware recommendation approach based on Tensor Factorization for Mean Average Precision maximization (TFMAP).  ... 
doi:10.1109/icpr.2014.635 dblp:conf/icpr/ManzatoDMR14 fatcat:hwoogktb3bhfdo2qyde2zjoi34

BoostFM

Fajie Yuan, Guibing Guo, Joemon M. Jose, Long Chen, Haitao Yu, Weinan Zhang
2017 Proceedings of the 22nd International Conference on Intelligent User Interfaces - IUI '17  
The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation.  ...  However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases) rather than explicit ratings.  ...  ACKNOWLEDGMENTS Fajie thanks the CSC funding for supporting his research. This work was also partially supported by the National Natural Science Foundation of China (No. 61472073).  ... 
doi:10.1145/3025171.3025211 dblp:conf/iui/YuanGJCYZ17 fatcat:n7mh6wxf4bgszjh2fesfsaoi6y

Gaussian process factorization machines for context-aware recommendations

Trung V. Nguyen, Alexandros Karatzoglou, Linas Baltrunas
2014 Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval - SIGIR '14  
To address this limitation, we develop a novel and powerful non-linear probabilistic algorithm for context-aware recommendation using Gaussian processes.  ...  Context-aware recommendation (CAR) can lead to significant improvements in the relevance of the recommended items by modeling the nuanced ways in which context influences preferences.  ...  RBM's models for CF are currently restricted to the user-item problem and have not been extended to context. Context-aware recommendation (CAR).  ... 
doi:10.1145/2600428.2609623 dblp:conf/sigir/NguyenKB14 fatcat:drmev4shgzd2veljcs6gq6k3dm

Tensor Methods and Recommender Systems [article]

Evgeny Frolov, Ivan Oseledets
2018 arXiv   pre-print
, situational (e.g. context-aware, criteria-driven) recommendations.  ...  We also provide a high level discussion of the future perspectives and directions for further improvement of tensor-based recommendation systems.  ...  Acknowledgements The authors would like to thank Maxim Rakhuba and Alexander Fonarev for their help for improving the manuscript, and also Michael Thess for insightful conversations.  ... 
arXiv:1603.06038v2 fatcat:yn4ozyphr5hwheignf65j26xsy

A Semi-Supervised Model for Top-N Recommendation

Shulong Chen, Yuxing Peng
2018 Symmetry  
Top-N recommendation is an important recommendation technique that delivers a ranked top-N item list to each user. Data sparsity is a great challenge for top-N recommendation.  ...  Therefore, we select a certain number of items ranked higher in the recommendation list to construct an intermediate set and optimize the metric Area Under the Curve (AUC).  ...  We made an assumption about users' relative preferences among unrated items and put forward an approach to build an intermediate set and optimize the AUC metric. 2.  ... 
doi:10.3390/sym10100492 fatcat:of7ludz2jbf7rogdpocmmpnaqm

Attribute-aware Collaborative Filtering: Survey and Classification [article]

Wen-Hao Chen, Chin-Chi Hsu, Yi-An Lai, Vincent Liu, Mi-Yen Yeh, Shou-De Lin
2018 arXiv   pre-print
We provide the readers not only the high level mathematical interpretation of the existing works in this area but also the mathematical insight for each category of models.  ...  This paper surveys works in the past decade developing attribute-aware CF systems, and discovered that mathematically they can be classified into four different categories.  ...  cial for real-world recommendation since a user pays a ention to the top-N items.  ... 
arXiv:1810.08765v1 fatcat:gkjhxziqengxbog5greaziponq

Attribute-Aware Recommender System Based on Collaborative Filtering: Survey and Classification

Wen-Hao Chen, Chin-Chi Hsu, Yi-An Lai, Vincent Liu, Mi-Yen Yeh, Shou-De Lin
2020 Frontiers in Big Data  
This paper surveys work in the past decade to develop attribute-aware CF systems and finds that they can be classified into four different categories mathematically.  ...  Attribute-aware CF models aim at rating prediction given not only the historical rating given by users to items but also the information associated with users (e.g., age), items (e.g., price), and ratings  ...  AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.  ... 
doi:10.3389/fdata.2019.00049 pmid:33693372 pmcid:PMC7931907 fatcat:yt4l4mp2hfd47do5opvyf73kyy

GAPfm

Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, Martha Larson, Alan Hanjalic
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
If accurate top-N recommendation lists are to be produced for such graded relevance domains, it is critical to generate a ranked list of recommended items directly rather than predicting ratings.  ...  Experimental results show that GAPfm achieves substantial improvements on the top-N recommendation task, compared to several state-ofthe-art approaches.  ...  In a similar spirit, TFMAP [28] directly optimizes Average Precision for context-aware recommendations. All of these methods use binary implicit-feedback data.  ... 
doi:10.1145/2505515.2505653 dblp:conf/cikm/ShiKBLH13 fatcat:4vsr343swzf35mydj2q57mpzui

Exploiting feature extraction techniques on users' reviews for movies recommendation

Rafael M. D'Addio, Marcos A. Domingues, Marcelo G. Manzato
2017 Journal of the Brazilian Computer Society  
We focus on exploiting the impact that different feature extraction techniques, allied with sentiment analysis, cause in an item attribute-aware neighborhood-based recommender algorithm.  ...  Beyond the traditional recommender strategies, there is a growing effort to incorporate users' reviews into the recommendation process, since they provide a rich set of information regarding both items  ...  We evaluated this scenario by selecting the top 100 items with the highest predicted ratings for each user and applied the precision at n (prec@n) and the mean average precision (MAP) measures.  ... 
doi:10.1186/s13173-017-0057-8 fatcat:dvznwzljuzfw5ixztj7juihr4a

Einsatz von Machine-Learning-Methoden zur adaptiven Darstellung von Software-Metriken [article]

Matthias Hermann, Universität Stuttgart, Universität Stuttgart
2017
TFMAP: Optimizing MAP for Top-N Context-aware Recommendation Im Bereich des kollaborativen Filterns und der kontextsensitiven Empfehlungen haben Shi et al.  ...  : Optimizing MAP for Top-N Context-aware Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Zusammenfassung und Handlungsbedarf . . . . . . . . . . . . . . . . . . 29 Bewertungskriterien  ... 
doi:10.18419/opus-9296 fatcat:pgzprr6hwjgurddhcegkabctve