User tweets based genre prediction and movie recommendation using LSI and SVD

Bansal, Sakshi; Gupta, Chetan; Arora, Anuja

doi:10.1109/ic3.2016.7880220

Cited by 12 publications

(2 citation statements)

References 3 publications

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“…Matrix decomposition is often used to predict scores in recommendation systems. Bansal et al 34 combined LSI and SVD to recommend personalized videos for users. They analyzed the types of movies visited by users through LSI, made approximate judgment on movies through the scores predicted by SVD, and finally recommended videos similar to those visited by users.…”

Section: Related Workmentioning

confidence: 99%

A hybrid intelligent service recommendation by latent semantics and explicit ratings

Duan

Gao

et al. 2021

Int J of Intelligent Sys

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User rating of a service is the explicit behavior of users expressing their preference for the service. Most exciting recommendation methods focus on predicting userservice ratings according to users' historical rating behaviors. However, the behavior of users invoking services is implicit feedback. By analyzing the services called by users, mining their potential semantic representations can also help model users' hidden interests. To this end, how to integrate the implicit feedback and explicit rating of users to provide users with better recommendation experience is a problem to be addressed for service recommendation. In this paper, we propose a novel latent semantic integrated explicit rating (LSIER) scheme to recommend services to users. The LSIER scheme is designed by integrating the probabilistic matrix factorization (PMF) model and the probabilistic latent semantic index (PLSI) model. consists of the two stages: (1) the PMF model is used to generate a user feature matrix and a service feature matrix, and the two feature matrices are updated to complete the missing service score records of the users, and (2) the PLSI model is used to train users access records, where an expectation maximization algorithm is applied to derive the model parameters to realize unsupervised soft clustering of services. When the user gives explicit or implicit feedback to the service, the LSIER scheme can identify the current interest probability distribution of the user according to the category Beijing Jiaotong University, Grant/Award Number: 2019RC050 to which the called service belongs, and provide the user with a list of service recommendations with scores. The performance of the proposed LSIER scheme is evaluated using the Netflix data set and the Movielens data set.Experiments show that the scheme can achieve better recommendation accuracy and recall rate than existing methods.

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Section: Related Workmentioning

confidence: 99%

A hybrid intelligent service recommendation by latent semantics and explicit ratings

Duan

Gao

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

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“…Cosine similarity [67], [68] is the measure of similarity between two non-zero vectors in the inner product space. It measures the angle between these two vectors.…”

Section: Cosine Similaritymentioning

confidence: 99%

A review of movie recommendation system: Limitations, Survey and Challenges

Sunilkumar

2020

ELCVIA

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Recommendation System is a major area which is very popular and useful for people to take proper automated decisions. It is a method that helps user to find out the information which is beneficial to him/her from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user's activity (Content Based Filtering) which he wants to engage with. To overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have surveyed state-of-the-art methods of Content Based Filtering, Collaborative Filtering, Hybrid Approach and Deep Learning Based Methods for movie recommendation. We have also reviewed different similarity measures. Various companies like facebook which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored.

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Genre based hybrid filtering for movie recommendation engine

Roy

Ludwig

2021

J Intell Inf Syst

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User tweets based genre prediction and movie recommendation using LSI and SVD

Cited by 12 publications

References 3 publications

A hybrid intelligent service recommendation by latent semantics and explicit ratings

A hybrid intelligent service recommendation by latent semantics and explicit ratings

A review of movie recommendation system: Limitations, Survey and Challenges

Genre based hybrid filtering for movie recommendation engine

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