Sequential Movie Genre Prediction using Average Transition Probability with Clustering
@article{Kim2021SequentialMG, title={Sequential Movie Genre Prediction using Average Transition Probability with Clustering}, author={Jihye Kim and Jinkyung Kim and Jaeyoung Choi}, journal={ArXiv}, year={2021}, volume={abs/2111.02740} }
In recent movie recommendations, predicting the user’s sequential behavior and suggesting the next movie to watch is one of the most important issues. However, capturing such sequential behavior is not easy because each user’s short-term or long-term behavior must be taken into account. For this reason, many research results show that the performance of recommending a specific movie is not very high in a sequential recommendation. In this paper, we propose a cluster-based method for classifying…
Figures and Tables from this paper
References
SHOWING 1-10 OF 29 REFERENCES
Recommender system design using movie genre similarity and preferred genres in SmartPhone
- Computer ScienceMultimedia Tools and Applications
- 2011
A recommender system using movie genre similarity and preferred genres to recommend new genres to targeted customers is proposed and designed and generated to provide related services in a mobile experimental environment.
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.
Recurrent Collaborative Filtering for Unifying General and Sequential Recommender
- Computer ScienceIJCAI
- 2018
This paper proposes a recommendation model named Recurrent Collaborative Filtering (RCF), which unifies both paradigms within a single model and empirically demonstrates that the model outperforms the state-of-the-art methods across the tasks of both sequential and general recommender.
Translation-based Recommendation: A Scalable Method for Modeling Sequential Behavior
- Computer ScienceIJCAI
- 2018
A unified method is proposed, TransRec, to model such interactions for largescale sequential prediction of users’ personalized sequential behavior, which outperforms the state-of-the-art on a wide spectrum of real-world datasets.
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.
Session-based Recommendation with Graph Neural Networks
- Computer ScienceAAAI
- 2019
In the proposed method, session sequences are modeled as graph-structured data and GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods.
Collaborative filtering with temporal dynamics
- Computer ScienceKDD
- 2009
Two leading collaborative filtering recommendation approaches are revamp and a more sensitive approach is required, which can make better distinctions between transient effects and long term patterns.
An MDP-Based Recommender System
- Computer ScienceJ. Mach. Learn. Res.
- 2002
The use of an n-gram predictive model is suggested for generating the initial MDP, which induces a Markovchain model of user behavior whose predictive accuracy is greater than that of existing predictive models.
Next Item Recommendation with Self-Attentive Metric Learning
- Computer Science
- 2018
A novel sequence-aware recommendation model that utilizes self-attention mechanism to infer the item-item relationship from user’s historical interactions and is finally trained in a metric learning framework, taking both local and global user intentions into consideration.
A Survey on Session-based Recommender Systems
- PsychologyACM Comput. Surv.
- 2022
A systematic and comprehensive review on SBRS is provided and a hierarchical framework is created to categorize the related research issues and methods of SBRS and to reveal its intrinsic challenges and complexities.