Fast Tensor Factorization for Large-Scale Context-Aware Recommendation from Implicit Feedback

2020 ◽  
Vol 6 (1) ◽  
pp. 201-208 ◽  
Author(s):  
Szu-Yu Chou ◽  
Jyh-Shing Roger Jang ◽  
Yi-Hsuan Yang
Keyword(s):  
Large Scale ◽  
Implicit Feedback ◽  
Context Aware ◽  
Tensor Factorization

TrustTF: A tensor factorization model using user trust and implicit feedback for context-aware recommender systems

2020 ◽  
Vol 209 ◽  
pp. 106434
Author(s):  
Jianli Zhao ◽  
Wei Wang ◽  
Zipei Zhang ◽  
Qiuxia Sun ◽  
Huan Huo ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Implicit Feedback ◽  
Context Aware ◽  
Tensor Factorization ◽  
Factorization Model ◽  
User Trust

scholarly journals Effective neural architectures for context-aware venue recommendation

2019 ◽  
Vol 53 (2) ◽  
pp. 102-103
Author(s):  
Jarana Manotumruksa
Keyword(s):  
Large Scale ◽  
Contextual Information ◽  
Complex Structure ◽  
Geographical Location ◽  
Implicit Feedback ◽  
Context Aware ◽  
Additional Information ◽  
Current Location ◽  
Explicit Feedback

Users in Location-Based Social Networks (LBSNs), such as Yelp and Foursquare, can search for interesting venues such as restaurants and museums to visit, or share their location with their friends by making an implicit feedback (e.g. checking in at venues they have visited). The users can also leave explicit feedback on the venues they have visited by providing ratings and/or comments. Such explicit and implicit feedback by the users provide rich information about both users and venues, and thus can be leveraged to study the users' movement in urban cities, as well as enhance the quality of personalised venue recommendations. Unlike traditional recommendation systems (e.g. book and movie recommendation systems), making effective venue recommendations is more challenging because we need to take into account the users' current context (e.g. time of the day, user's current location as well as his recently visited venues). In this thesis, based upon Matrix Factorisation (MF) and Bayesian Personalised Ranking (BPR) models, we aim to generate effective context-aware venue recommendation that a user may wish to visit based on the user's historical explicit and implicit feedbacks, the user's contextual information (e.g. the user's current location and time of the day) and additional information (e.g. the geographical location of venues and users' social relationships). To achieve this goal, we need to address the following challenges: namely (C1) modelling the users' preferences and the characteristic of venues, (C2) capturing the complex structure of user-venue interactions in a Collaborative Filtering manner, (C3) modelling the users' short-term ( dynamic ) preferences from the sequential order of user's observed feedback as well as the contextual information associated with the successive feedback, (C4) generating accurate top-K venue recommendations based on the users' preferences using a pairwise ranking-based model and (C5) appropriately sampling potential negative instances to train a ranking-based model. First, to address challenge C1 , we leverage the users' explicit feedback (e.g. their ratings and the textual content of the comments) and additional information (e.g. users' social relationships) to effectively model the users' preferences and the characteristics of venues. In particular, we propose a novel regularisation technique [1] and a factorisation-based model [2] that leverages the users' explicit feedback and the additional information to improve the rating prediction accuracy of the traditional MF model. Experiments conducted on a large scale rating dataset on LBSN demonstrate that the textual content of comments plays an important role in enhancing the accuracy of rating prediction. Second, we investigate how to leverage the users' implicit feedback and additional information such as the users' social relationship and the geographical location of venues to improve the quality of top-K venue recommendations. In particular, to address challenges C4 and C5 , we propose a novel pairwise ranking-based framework for top-K venue recommendations [3] that can incorporate multiple sources of additional information (e.g. the users' social relationship and the geographical location of venues) to effectively sample the potential negative instances. Experimental results on three large scale checkin and rating datasets from LBSNs demonstrate that the social correlations and the geographical influences play an important role to the quality of sampled negative instances and hence can improve the quality of top-K venue recommendations. Finally, to address challenges C2 and C3 , we propose a framework for sequential-based venue recommendations [4] that exploits Deep Neural Network (DNN) models to effectively capture the complex structure of user-venue interactions and the users' long-term ( dynamic ) preferences from their sequential order of checkins. Moreover, we propose a novel Recurrent Neural Network (RNN) architecture [5] that can effectively incorporate the contextual information associated with the successive implicit feedback (e.g. the time interval and the geographical distance between two successive checkins) to generate high quality context-aware venue recommendations. Experimental results on three large scale checkin and rating datasets from LBSNs demonstrate the effectiveness and robustness of our proposed framework and architecture for context-aware venue recommendations. Supervisors Dr. Craig Macdonald (University of Glasgow), Prof. Iadh Ounis (University of Glasgow) Available from : http://theses.gla.ac.uk/76735/

Context-aware Adaptive Surgery

2021 ◽  
Vol 5 (3) ◽  
pp. 1-22
Author(s):  
Hongli Wang ◽  
Bin Guo ◽  
Jiaqi Liu ◽  
Sicong Liu ◽  
Yungang Wu ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Resource Constraints ◽  
Search Algorithm ◽  
Search Time ◽  
State Graph ◽  
Context Aware ◽  
Optimal Partition ◽  
Research Attention ◽  
Neighbor Effect

Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.

Context-aware personalized path inference from large-scale GPS snippets

2018 ◽  
Vol 91 ◽  
pp. 78-88 ◽  
Author(s):  
Hongtao Wang ◽  
Hongmei Wang ◽  
Feng Yi ◽  
Hui Wen ◽  
Gang Li ◽  
...  
Keyword(s):  
Large Scale ◽  
Context Aware

scholarly journals Context-aware Music Recommender System Based on Implicit Feedback

2021 ◽  
Vol 36 (1) ◽  
pp. WI2-D_1-10
Author(s):  
Yasufumi Takama ◽  
Jing-cheng Zhang ◽  
Hiroki Shibata
Keyword(s):  
Recommender System ◽  
Implicit Feedback ◽  
Context Aware

Personalized and Context-Aware TV Program Recommendations Based on Implicit Feedback

2015 ◽  
pp. 57-68 ◽  
Author(s):  
Paolo Cremonesi ◽  
Primo Modica ◽  
Roberto Pagano ◽  
Emanuele Rabosio ◽  
Letizia Tanca
Keyword(s):  
Implicit Feedback ◽  
Context Aware ◽  
Tv Program

Context-aware Distance Measures for Dynamic Networks

2022 ◽  
Vol 16 (1) ◽  
pp. 1-34
Author(s):  
Yiji Zhao ◽  
Youfang Lin ◽  
Zhihao Wu ◽  
Yang Wang ◽  
Haomin Wen
Keyword(s):  
Large Scale ◽  
Dynamic Networks ◽  
Distance Measures ◽  
General Definition ◽  
Mathematical Representation ◽  
Network Clustering ◽  
Context Aware ◽  
Network Distance ◽  
Practical Applications ◽  
Spectral Distance

Dynamic networks are widely used in the social, physical, and biological sciences as a concise mathematical representation of the evolving interactions in dynamic complex systems. Measuring distances between network snapshots is important for analyzing and understanding evolution processes of dynamic systems. To the best of our knowledge, however, existing network distance measures are designed for static networks. Therefore, when measuring the distance between any two snapshots in dynamic networks, valuable context structure information existing in other snapshots is ignored. To guide the construction of context-aware distance measures, we propose a context-aware distance paradigm, which introduces context information to enrich the connotation of the general definition of network distance measures. A Context-aware Spectral Distance (CSD) is then given as an instance of the paradigm by constructing a context-aware spectral representation to replace the core component of traditional Spectral Distance (SD). In a node-aligned dynamic network, the context effectively helps CSD gain mainly advantages over SD as follows: (1) CSD is not affected by isospectral problems; (2) CSD satisfies all the requirements of a metric, while SD cannot; and (3) CSD is computationally efficient. In order to process large-scale networks, we develop a kCSD that computes top- k eigenvalues to further reduce the computational complexity of CSD. Although kCSD is a pseudo-metric, it retains most of the advantages of CSD. Experimental results in two practical applications, i.e., event detection and network clustering in dynamic networks, show that our context-aware spectral distance performs better than traditional spectral distance in terms of accuracy, stability, and computational efficiency. In addition, context-aware spectral distance outperforms other baseline methods.

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