• Corpus ID: 64018541

A Movie Recommender System Based on

@inproceedings{Learning2004AMR,
  title={A Movie Recommender System Based on},
  author={Inductive Learning and Peng Li and Seiji Yamada},
  year={2004},
  url={https://api.semanticscholar.org/CorpusID:64018541}
}
Inductive-learning-based recommendation technology solves the sparsity problem by sharing attributes of each item(the authors call them contents preferences) and the computation cost during inductive learning process is low enough to construct decision tree instantly.
No Paper Link Available
9 Citations
Background Citations
3
Methods Citations
3

9 Citations

Item-Based Collaborative Filtering in Movie Recommendation in Real time

Movie recommender system has been described, which basically uses item-based technique of collaborative filtering to provide the recommendations of items, which is dynamic and will learn from the positive feedback.

Kernel Optimization Based Enhanced Preference Learning for Online Movie Recommendation

The proposed system is a movie recommendation system which related on user preferences or interests produces an accurate movie recommendation to users and reduces nonlinearity in ratings using Monte Carlo uncertainty propagation method.

Stream-based semi-supervised learning for recommender systems

A state-of-the-art matrix factorization algorithm is extended by the ability to add new dimensions to the matrix at runtime and two approaches to semi-supervised learning are implemented: co-training and self-learning.

Framework for Movie Recommendation System using GNN and Textual Data

This paper leverages the growing popularity of Graph Neural Networks in recommendation systems to capitalize on their ability to discern intricate relationships between entities such as movies, users, and actors.

Artificial Intelligence Prediction Technology in Intelligent Recommendation Algorithms for Film and Television

    Jiaqi Li
    Computer Science
    2024 Second International Conference on Data…
  • 2024
By comparing the user satisfaction results of the paper’s algorithm with traditional algorithms, it can be seen that the paper’s algorithm has a much better evaluation score than the traditional 7.42.

Joint learning for image-based handbag recommendation

This paper proposes a method to recommend handbags to each shopper, based on the handbag images the shopper has clicked, by Joint learning of attribute Projection and One-class SVM classification (JPO).

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches

This study aims to employ information retrieval techniques for the automated identification of suggestions using the SemEval2019 dataset to extract suggestions from the textual content of online user reviews, and suggests that information retrieval-based approaches exhibit promising potential for this specific task.

Tca4rec: contrastive learning with popularity-aware asymmetric augmentation for robust sequential recommendation

This paper proposes TCA4Rec, a robust sequential recommendation framework that addresses challenges via a novel two-stage contrastive learning approach, and incorporates an additional memory module to aggregate sequence embeddings, thereby providing flexible and generalized representations of user preferences.

TrustDL: Use of trust-based dictionary learning to facilitate recommendation in social networks

8 References

Item-based collaborative filtering recommendation algorithms

This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.

Recommender Systems

The main purpose of this paper is to spot the research trend in RS, types of RS, problems in RS and future scope of RS, and envisions the future of Recommender System which may open up new research directions in this domain.

Beyond Algorithms: An HCI Perspective on Recommender Systems

From a user’s perspective, an effective recommender system inspires trust in the system; has system logic that is at least somewhat transparent; points users towards new, not-yet-experienced items; provides details about recommended items, including pictures and community ratings; and finally, provides ways to refine recommendations by including or excluding particular genres.

Empirical Analysis of Predictive Algorithms for Collaborative Filtering

Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.

Fab: content-based, collaborative recommendation

It is explained how a hybrid system can incorporate the advantages of both methods while inheriting the disadvantages of neither, and how the particular design of the Fab architecture brings two additional benefits.

C4.5: Programs for Machine Learning

A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.

Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach

This work describes and evaluates a new method called personality diagnosis (PD), which compute the probability that a user is of the same "personality type" as other users, and, in turn, the likelihood that he or she will like new items.

Query Learning Strategies Using Boosting and Bagging