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Taste Over Time: The Temporal Dynamics Of User Preferences

Joshua L. Moore, Shuo Chen, Douglas Turnbull, Thorsten Joachims
2013 Zenodo  
The first author is supported by an NSF Graduate Research Fellowship.  ...  We would also like to thank the anonymous reviewers for their feedback, and Brian McFee for helpful discussions and technical advice.  ...  Users and/or songs are allowed to change position over time, which enables the analysis of long-term dynamics of user tastes and artist appeal and style.  ... 
doi:10.5281/zenodo.1416147 fatcat:f7gft7anxraarospzz4vavd6p4

Time-weighted Attentional Session-Aware Recommender System [article]

Mei Wang, Weizhi Li, Yan Yan
2019 arXiv   pre-print
We integrate the time changes in session RNN and add user preferences as model drifting; and (2) a novel triangle parallel attention network that enhances the original RNN model by incorporating time information  ...  And then, our ASARS framework promotes two novel models: (1) an inter-session temporal dynamic model that captures the long-term user interaction for RNN recommender system.  ...  On the other hand, her long-term profile of tastes or preferences would not change much over recent sessions, like her favorite brands, preferred color, fashion style, etc.  ... 
arXiv:1909.05414v1 fatcat:wprgje6jsbh33a4djewf3r5qda

Time-Sensitive Collaborative Filtering through Adaptive Matrix Completion [chapter]

Julien Gaillard, Jean-Michel Renders
2015 Lecture Notes in Computer Science  
Model updates are based on a temporal regularization, ensuring smoothness and consistency over time, while leading to very efficient, easily scalable algebraic computations.  ...  Real-world Recommender Systems are often facing drifts in users' preferences and shifts in items' perception or use.  ...  Introduction The fact that item perception and user tastes and moods vary over time is well known.  ... 
doi:10.1007/978-3-319-16354-3_35 fatcat:fg456dagqberdc42nds3yregd4

Location, time, and preference aware restaurant recommendation method

Md. Ahsan Habib, Md. Abdur Rakib, Muhammad Abul Hasan
2016 2016 19th International Conference on Computer and Information Technology (ICCIT)  
He has offered us help to understand the nec-  ...  Although, in terms of implementing a Food recommendation system, such measurement could cause a problem. As because, both the taste and experience of a user can change over the time.  ...  Hence, the popularity of a restaurant depends on the number of user's visits it gets over a time period.  ... 
doi:10.1109/iccitechn.2016.7860216 fatcat:5ztnfni7bjgk7o5zzmr6n7au7u

TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation

Jianqing Zhang, Dongjing Wang, Dongjin Yu
2021 Neurocomputing  
Firstly, TLSAN models "personalized time-aggregation" and learn user-specific temporal taste via trainable personalized time position embeddings with category-aware correlations in long-term behaviors.  ...  taste that is related to the "time-aggregation" phenomenon ("personalized time-aggregation"), and 3) users' short-term interests play an important role in the next item prediction/recommendation.  ...  Besides, such methods cannot effectively model the dynamic changes of original context over time.  ... 
doi:10.1016/j.neucom.2021.02.015 fatcat:53ktosol6bgsdk34y7tycr76qu

Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation [article]

Chao Chen, Haoyu Geng, Nianzu Yang, Junchi Yan, Daiyue Xue, Jianping Yu, Xiaokang Yang
2022 arXiv   pre-print
User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data.  ...  In this paper, we propose a novel attention network, named self-modulating attention, that models the complex and non-linearly evolving dynamic user preferences.  ...  Often, users' preferences are not static and evolve over time due to a variety of reasons.  ... 
arXiv:2204.06517v1 fatcat:7kwki6cnrzeilcxgrpsziw7jxy

Towards real-time collaborative filtering for big fast data

Ernesto Diaz-Aviles, Wolfgang Nejdl, Lucas Drumond, Lars Schmidt-Thieme
2013 Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion  
The Web of people is highly dynamic and the life experiences between our on-line and "real-world" interactions are increasingly interconnected.  ...  However, given the deluge of data items, it is a challenge for individuals to find relevant and appropriately ranked information at the right time.  ...  We have presented several potential research directions, which we believe could lead us to better support users to conduct reliable assessments of dynamics topics on the Web, such as: views on political  ... 
doi:10.1145/2487788.2488044 dblp:conf/www/Diaz-AvilesNDS13 fatcat:tqvscm56afhwteilaeuso5djey

Recurrent Coevolutionary Latent Feature Processes for Continuous-Time Recommendation

Hanjun Dai, Yichen Wang, Rakshit Trivedi, Le Song
2016 Proceedings of the 1st Workshop on Deep Learning for Recommender Systems - DLRS 2016  
The RNN learns a nonlinear representation of user and item embeddings which take into account mutual influence between user and item features, and the feature evolution over time.  ...  As users interact with different items over time, user and item features can influence each other, evolve and co-evolve over time.  ...  extended the deep semantic structured model to capture multi-granularity temporal preference of users.  ... 
doi:10.1145/2988450.2988451 dblp:conf/recsys/DaiWTS16 fatcat:fig5uun6hreyzhqumvms525qwe

Modeling and Forecasting the Evolution of Preferences over Time: A Hidden Markov Model of Travel Behavior [article]

Feras El Zarwi, Akshay Vij, Joan Walker
2017 arXiv   pre-print
Literature suggests that preferences, as denoted by taste parameters and consideration sets, may evolve over time in response to changes in demographic and situational variables, psychological, sociological  ...  The framework is applied to study the evolution of travel mode preferences, or modality styles, over time, in response to a major change in the public transportation system.  ...  detailed feedback on an earlier draft of the paper.  ... 
arXiv:1707.09133v1 fatcat:khyk4lg2o5ebrg6pitulh4tn6q

Towards Time-aware Contextual Music Recommendation: An Exploration of Temporal Patterns of Music Listening Using Circular Statistics

Zuriñe Resa, Perfecto Herrera
2010 Zenodo  
This way temporal patterns are identified regarding the time of the day or the day of the week (respectively, a period of 24 hours and of 7 days).  ...  So as to detect these temporal patterns or rhythms, a circular statistical analysis is performed over a data-set containing the listening habits of almost a thousand users of an online radio - music recommender  ...  This allowed the author to detect concept drifts and the temporal evolution of preferences, and to improve the recommendation over a long time span.  ... 
doi:10.5281/zenodo.3753001 fatcat:qjccv4j5bjejjnnobylhfbychu

A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations [article]

Krishna Prasad Neupane, Ervine Zheng, Yu Kong, Qi Yu
2022 arXiv   pre-print
Experiments over real-world data help demonstrate the effectiveness of the proposed time-sensitive cold-start recommendation model.  ...  The proposed model leverages historical and current user-item interactions and dynamically factorizes a user's (latent) preference into time-specific and time-evolving representations that jointly affect  ...  The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing any funding agency.  ... 
arXiv:2204.00970v1 fatcat:tykjxev2ojhdjj2lkvt7krm62q

Using Time Clusters for Following Users' Shifts in Rating Practices

Dionisis Margaris, Costas Vassilakis
2017 Complex Systems Informatics and Modeling Quarterly  
In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel  ...  However, a user's rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa.  ...  However, relying on a single mean value presumes that the users' marking practices remain constant over time; in practice though, a user's marking practices may change over time, i.e. a user could start  ... 
doi:10.7250/csimq.2017-13.02 fatcat:qrep4ycxljhyrmrmttlkgc6sua

Large-scale user modeling with recurrent neural networks for music discovery on multiple time scales

Cedric De Boom, Rohan Agrawal, Samantha Hansen, Esh Kumar, Romain Yon, Ching-Wei Chen, Thomas Demeester, Bart Dhoedt
2017 Multimedia tools and applications  
This way we obtain semantically rich user representations, which capture a user's musical taste over time.  ...  Recommender systems are the application of choice to open up the collection to these users.  ...  We greatly thank Nvidia for its donation of a Tesla K40 and Titan X GPU to support the research of the IDLab group at Ghent University.  ... 
doi:10.1007/s11042-017-5121-z fatcat:wdkmzavmmrafde3ktyydaqygda

A Collaborative Kalman Filter for Time-Evolving Dyadic Processes [article]

San Gultekin, John Paisley
2015 arXiv   pre-print
Each observation is a random variable whose distribution is parameterized by the dot product of the relevant Brownian motions at that moment in time.  ...  We present the collaborative Kalman filter (CKF), a dynamic model for collaborative filtering and related factorization models.  ...  We also note that the PMF algorithm is the non-dynamic version of [15] . . An example of a user drift over time as seen through predicted ratings for several movies from the Netflix data set.  ... 
arXiv:1501.05624v1 fatcat:q3v6i5ocbne4tga7dyd3h5a5ly

Visualizing Timed, Hierarchical Code Structures in AscoGraph

Grigore Burloiu, Arshia Cont
2015 2015 19th International Conference on Information Visualisation  
In this paper, we apply an information visualisation perspective to a set of revisions in the timeline-based representation of action items in AscoGraph, the dedicated user interface to Antescofo.  ...  In the latter, we frame the problem of arranging action rectangles in a 2D space as a strip packing problem, with the additional constraint that the (horizontal) time coordinates of each block are fixed  ...  REPRESENTING TIME IN AscoGraph We present a brief overview of temporal concepts in the Antescofo action language.  ... 
doi:10.1109/iv.2015.37 dblp:conf/iv/BurloiuC15 fatcat:z5tll3tcbvaj5phvvzd35su5a4
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