A Comprehensive Survey on Movie Recommendation Systems

Lavanya, R.; Singh, Utkarsh; Tyagi, Vibhor

doi:10.1109/icais50930.2021.9395759

Cited by 8 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al. [10], in order to tackle the information explosion problem, recommendation systems are helpful. Authors mentioned about the problems of data sparsity, cold start problem, scalability, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Movie Recommendation System using Machine Learning

Nandan

2024

IJSREM

View full text Add to dashboard Cite

There is already enough content available on the movie recommendation system. Showing the movie recommendations is essential so that the user need not waste a lot of time searching for the content which he/she might like. Thus, movie recommendation system plays a vital role to get user personalized movie recommendations. After searching a lot on the internet and referring to a lot of research papers, we got to know that the recommendations made using Content-based Filtering are using a single text to vector conversion technique and a single technique to find the similarity between the vectors. In this research work, we have used multiple text to vector conversion techniques and manipulated the results of the multiple algorithms to get the final recommendation list. You can think of it as a hybrid approach using the Content-based Filtering technique only.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Movie Recommendation System using Machine Learning

Nandan

2024

IJSREM

View full text Add to dashboard Cite

show abstract

“…Step 3: Following the acquisition of the mean values for each instance as delineated in the second step, the overall average score for all instances of the context Ct is computed in accordance with Eq. (1).…”

Section: Figure 1 Multidimensional Context Spacementioning

confidence: 99%

“…With the burgeoning of the Internet and advancements in information technology, an exponential surge in data across network platforms has been observed [1]. Such proliferation not only furnishes users with a wealth of intriguing information but paradoxically engenders a quandary of information overload.…”

Section: Introductionmentioning

confidence: 99%

A Novel Context-Aware Deep Learning Algorithm for Enhanced Movie Recommendation Systems

Zhang,

Abisado

2023

MMEP

View full text Add to dashboard Cite

Recommendation systems serve as a pivotal solution to address the increasing issue of information overload. While traditional recommendation algorithms have been grounded primarily on user-item interactions, the significance of a user's contextual information influencing decision-making has often been overlooked. Such neglect becomes more evident in the realm of systems integrating contextual mechanisms, which encounter pronounced data sparsity challenges. Existing studies in contextual recommendation systems tend to treat all contextual features uniformly as influencers of user decisions. Yet, a prevalent dilemma is the frequent absence of contextual data, leading to potential misallocations of contextual features. To mitigate these challenges, a novel deep learning-based recommendation system, termed the CAW-NeuMF Model, has been designed. Accompanying this model, a Context-aware Weighted high-order Tensor Factorization algorithm (CAWTF) has been introduced. This algorithm facilitates the calculation of correlations between user ratings in varied contexts, relying on the said context. Additionally, it ascertains the weight of context features grounded on the user ratings correlation. Such a process aids in isolating the most influential contextual features, thereby amplifying the efficiency of personalized recommendations. Empirical evaluations using the LDOS CoMoDa dataset revealed that the proposed model substantially enhances prediction score accuracy. Comparative analyses against alternative recommendation models further affirmed the superior efficacy of the introduced approach.

show abstract