Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation
@article{Chen2022ModelingDU, title={Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation}, author={Chao Chen and Dongsheng Li and Junchi Yan and Xiaokang Yang}, journal={IEEE Transactions on Knowledge and Data Engineering}, year={2022}, volume={34}, pages={5446-5458} }
Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms – including both shallow and deep ones – often model such dynamics independently, i.e., user static and dynamic preferences are not modeled under the same latent space, which makes it difficult to fuse them for recommendation. This paper considers the problem of embedding a user's sequential behavior into the latent…
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References
SHOWING 1-10 OF 70 REFERENCES
Sequential Recommendation with User Memory Networks
- Computer ScienceWSDM
- 2018
A memory-augmented neural network (MANN) integrated with the insights of collaborative filtering for recommendation is designed, which store and update users» historical records explicitly, which enhances the expressiveness of the model.
Sequential Recommender System based on Hierarchical Attention Networks
- Computer ScienceIJCAI
- 2018
A novel two-layer hierarchical attention network is proposed, which takes the above properties into account, to recommend the next item user might be interested and demonstrates the superiority of the method compared with other state-of-the-art ones.
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation
- Computer Science2016 IEEE 16th International Conference on Data Mining (ICDM)
- 2016
Fossil is proposed, a similarity-based method that outperforms alternative algorithms, especially on sparse datasets, and qualitatively that it captures personalized dynamics and is able to make meaningful recommendations.
Learning Hierarchical Representation Model for NextBasket Recommendation
- Computer ScienceSIGIR
- 2015
This paper introduces a novel recommendation approach, namely hierarchical representation model (HRM), which can well capture both sequential behavior and users' general taste by involving transaction and user representations in prediction.
A Dynamic Recurrent Model for Next Basket Recommendation
- Computer ScienceSIGIR
- 2016
This work proposes a novel model, Dynamic REcurrent bAsket Model (DREAM), based on Recurrent Neural Network (RNN), which not only learns a dynamic representation of a user but also captures global sequential features among baskets.
Recurrent Recommender Networks
- Computer ScienceWSDM
- 2017
Recurrent Recommender Networks (RRN) are proposed that are able to predict future behavioral trajectories by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization.
Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models
- Computer ScienceRecSys
- 2015
This paper introduces a hierarchical hidden Markov model for capturing changes in user's preferences using a user's feedback sequence on items and the current context of the user as a hidden variable in this model.
Factorizing personalized Markov chains for next-basket recommendation
- Computer ScienceWWW '10
- 2010
This paper introduces an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data and shows that the FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization.
Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks
- Computer ScienceSIGIR
- 2018
This paper proposes a novel knowledge enhanced sequential recommender that integrates the RNN-based networks with Key-Value Memory Network (KV-MN) and incorporates knowledge base information to enhance the semantic representation of KV- MN.
Modeling relationships at multiple scales to improve accuracy of large recommender systems
- Computer ScienceKDD '07
- 2007
Both the local and the regional approaches, and in particular their combination through a unifying model, compare favorably with other approaches and deliver substantially better results than the commercial Netflix Cinematch recommender system on a large publicly available data set.