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Temporal Modeling of User Preferences in Recommender System
2020
International Conference on Information Control Systems & Technologies
The adaptation of the model is performed when describing a temporal pattern of the evolution of user requirements by choosing a time interval within which the initial temporal rules are formed. ...
The model is distinct by the use of temporal rules for building a temporal pattern of periodic changes in user preferences or a description of user requirements' evolution. ...
The metric 3 dsim allows highlighting a group of users with similar behaviour who have shown interest in the subject i x . Therefore, this metric is focused on building item-based recommendations. ...
dblp:conf/icst2/ChalyiL20
fatcat:pde7jtqzw5gwbaksipluhicjei
Dynamic collaborative filtering based on user preference drift and topic evolution
2020
IEEE Access
We propose a model to capture the user preference dynamics in the rating matrix by using a joint decomposition method to extract user latent transition patterns and combine latent factors together with ...
Moreover, existing collaborative filtering models mainly rely on solving data sparsity by adding side information to improve performance. ...
Another interesting extension to our model would be the ability to capture evolving, emerging and fading topic interests of users to improve the prediction performance of the dynamic recommendation system ...
doi:10.1109/access.2020.2993289
fatcat:3ugvw7vqybeztjhvjlwzlc75be
Collaborative Filtering Recommendation on Users' Interest Sequences
2016
PLoS ONE
With these updated similarities, transition characteristics and dynamic evolution patterns of users' preferences are considered. ...
Due to the users' unique behavior evolution patterns and personalized interest transitions among items, users' similarity in sequential dimension should be introduced to further distinguish users' preferences ...
To depict users' dynamic interest evolution patterns, we define the term "interest sequences" and other related concepts in on-line recommendation systems, which are inspired by work in location-based ...
doi:10.1371/journal.pone.0155739
pmid:27195787
pmcid:PMC4873175
fatcat:oss7ozg2cbghzfd6unwnqgdlry
Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution
[article]
2020
arXiv
pre-print
DTS continuously models the evolution of interests using a neural network, and thus is able to tackle the challenge of dynamically representing users' interests based on their historical behaviors. ...
Experiments on public dataset as well as real industry dataset with billions of samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with existing methods ...
) DIEN uses GRU with attentional update gate to model the user interest pattern. ...
arXiv:2001.03025v1
fatcat:e4sg36fbi5affixyd475bqd32u
Deep Time-Stream Framework for Click-through Rate Prediction by Tracking Interest Evolution
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
DTS continuously models the evolution of interests using a neural network, and thus is able to tackle the challenge of dynamically representing users' interests based on their historical behaviors. ...
Experiments on public dataset as well as real industry dataset with billions of samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with existing methods ...
Although improving the performance compared to non-sequential approaches, these RNN based methods still are no enough to represent the user interest evolution without considering time-stream information ...
doi:10.1609/aaai.v34i04.6028
fatcat:jl7dtsxea5hp5gamweqbl5kkkm
Incorporating Memory-Based Preferences and Point-of-Interest Stickiness into Recommendations in Location-Based Social Networks
2021
ISPRS International Journal of Geo-Information
Lastly, we incorporated the influence of both memory-based preferences and POI stickiness into a user-based collaborative filtering framework to improve the performance of POI recommendations. ...
First, we used the memory-based preference-attenuation mechanism to emphasize personal psychological effects and memory-based preference evolution in human mobility patterns. ...
Conflicts of Interest: The authors do not declare any conflict of interest. ...
doi:10.3390/ijgi10010036
fatcat:igtax6vlpnegret5bmega26gfi
Enhancing Mobile App User Understanding and Marketing with Heterogeneous Crowdsourced Data: A Review
2019
IEEE Access
To enhance mobile app development and marketing, it is important to study the key research challenges such as app user profiling, usage pattern understanding, popularity prediction, requirement and feedback ...
We first characterize the opportunities of the CrowdApp, and then present the key research challenges and state-of-the-art techniques to deal with these challenges. ...
group to improve the evolution of each app. ...
doi:10.1109/access.2019.2918325
fatcat:de763kc4qbdy5ijo55jxyhzgt4
Sequential Recommendation with Graph Neural Networks
[article]
2021
arXiv
pre-print
In this work, we propose a graph neural network model called SURGE (short for SeqUential Recommendation with Graph neural nEtworks) to address these two issues. ...
In addition, users' dynamic preferences often change rapidly over time, and hence it is difficult to capture user patterns in their historical sequences. ...
We study how SURGE improves the recommendation for those users with long behavior records. ...
arXiv:2106.14226v1
fatcat:c6inmah6qrh2vlsybo6viftwci
Cloud Synergetic Recommendation Model for Overseas Chinese Education by Modeling Multi-Source User Metaphor Information
2019
International Journal of Emerging Technologies in Learning (iJET)
Finally, in order to improve the efficiency of resource acquisition, a recommendation model of Chinese education based on cloud computing is presented. ...
Firstly, a user vector space with fine granularity representation is constructed by introducing multi-knowledge source emotional metaphor information. ...
Through the evolution of order parameters, user modes can be identified Map-Reduce model can improve the efficiency of synergetic user recommendation model Some problems related to synergetic recommendation ...
doi:10.3991/ijet.v14i23.11105
fatcat:vnaztdlponefzioi6oh5d4lnxe
Guiding Neuroevolution with Structural Objectives
2019
Evolutionary Computation
The first structural objective guides evolution to align neural networks with a user-recommended decomposition pattern. ...
The second structural objective guides evolution towards a population with a high diversity in decomposition patterns. ...
The experiments were performed on the Abel Cluster, owned by the University of Oslo and the Norwegian metacenter for High Performance Computing (NOTUR), and operated by the Department for Research Computing ...
doi:10.1162/evco_a_00250
pmid:30767665
fatcat:2fuud77ztzbjraobojw7ipa7rm
Solving the stability–accuracy–diversity dilemma of recommender systems
2017
Physica A: Statistical Mechanics and its Applications
To improve the similarity stability and recommendation stability is crucial for the user experience enhancement and the better understanding of user interests. ...
systems, while we study the systems' stability with its own natural evolution. ...
Normally, the system recommends L items with the highest scores to users, and those L items are what the system predicts to have the highest potential to hit the target user's interests. ...
doi:10.1016/j.physa.2016.10.083
fatcat:72um25xa2neklbrff7vnfwwfdm
SPDM in Social Media for the Development of Business
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
Data extracted with SPDM is utilized with KNG technique to identify the highly recommended product among clustered users. ...
Spatial Data Mining(SPDM) are acknowledged as mining of spatial knowledge among attractive pattern from various forms of Spatial Data. ...
Finally execution, Performance of KNG is compared with KNN which help the business people to take improved decision for the improvement of business.
II. ...
doi:10.35940/ijitee.e2863.039520
fatcat:jz2wfqahovcudnvpj2cgwfoari
Management of dynamic knowledge
2005
Journal of Knowledge Management
of the ontology with respect to the users' requirements. ...
users and the changes of available data. ...
With an experiment with the Bibster community we were able to show considerable performance improvements over non-personalized recommendations. ...
doi:10.1108/13673270510622483
fatcat:ens6irvcwjaw3aqoti5ywz63du
Evolutionary Preference Learning via Graph Nested GRU ODE for Session-based Recommendation
[article]
2022
arXiv
pre-print
Recently, there has been an increasing interest in modeling the user preference evolution to capture the fine-grained user interests. ...
Session-based recommendation (SBR) aims to predict the user next action based on the ongoing sessions. ...
Compared with all competitive baselines, the improvements brought by modeling the continuous evolution of user preferences are at most 6.05%, according to the ranking metric on average. ...
arXiv:2206.12779v1
fatcat:n3etwoa5rjhirlpazkv7reawrm
Performance of Recommendation Systems in Dynamic Streaming Environments
[chapter]
2007
Proceedings of the 2007 SIAM International Conference on Data Mining
We propose a systematic validation methodology that allows for simulating various controlled user profile evolution scenarios and validating the studied recommendation strategies. ...
In this paper, we study the behavior of collaborative filtering based recommendations under evolving user profile scenarios. ...
Hence, in the case of mild everyday evolution of user activity, TECNO-Streams Recommender has better performance, in the sense that, when the environment changes, the quality does not deteriorate as much ...
doi:10.1137/1.9781611972771.63
dblp:conf/sdm/NasraouiCRG07
fatcat:poylmnfbvrhdpc75px4evyvrh4
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