A review on recommender systems

Mansur, Farhin; Patel, Vibha; Patel, Mihir R.

doi:10.1109/iciiecs.2017.8276182

Cited by 32 publications

(9 citation statements)

References 3 publications

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“…Scalability: Collaborative Filtering use massive amount of data to make reliable better which require more number of resources. As information grows exponentially processing becomes expensive and inaccurate result from this Big data challenge [14]. Rest sections of this paper are organized as follows: In section 1, types of filtering techniques and their subtypes are discussed with detail survey and comparison.…”

Section: Challengesmentioning

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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Section: Challengesmentioning

confidence: 99%

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

Sunilkumar

2020

ELCVIA

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“…Los SRs se clasifican en diferentes categorías de acuerdo al tipo de información que se utiliza para recomendar productos y/o servicios a los usuarios [18]. En la literatura se puede encontrar una diversidad de técnicas para implementar los SRs por ejemplo en la Fig.…”

Section: Trabajos Relacionadosunclassified

“…Los principales enfoques que se emplean es el Filtro Colaborativo y el Filtro Basado en Contenido, pero ambos tienen algunas limitaciones y problemas. Mansur y Patel [18] en su trabajo de investigación dan un panorama general de los Sistemas de Recomendación, donde describieron los principales enfoques, problemas y limitaciones existentes. Así que en la mayoría de las aplicaciones que ocupan un SR se utiliza un enfoque híbrido, que combina diferentes técnicas para mejorar el funcionamiento del sistema, donde su idea principal es generar recomendaciones con una mejor exactitud y eficiencia; en cambio cuando se aplica un solo algoritmo es posible obtener resultados inexactos y poco confiables.…”

Section: Trabajos Relacionadosunclassified

Análsis, diseño y desarrollo de un sistema de recomendación basado en datos restauranteros de TripAdvisor y Foursquare

Perez¹,

Cuecuecha²,

Ramírez³

et al. 2018

RCS

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“…In practice, collaborative filtering tends to perform better than content filtering because of the richness of the rating history, which allows to discover latent features in the data and to go beyond what most user and item features can offer [7]. However, this dependency on the rating history often leads to the cold start problem [8]: recommendations to new users (or of new items) may not be good due to the lack of sufficient ratings, thus holding back new users from joining the platform. On the contrary, for users that have rated items for a long time, predictions might not be accurate anymore because they are based on old ratings, which leads to the problem called concept drift [9,10].…”

Section: Introductionmentioning

confidence: 99%

Modurec: Recommender Systems with Feature and Time Modulation

Maroto

Vignac

Frossard

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data. These algorithms unfortunately do not make effective use of other features, which can help solve two well identified problems of collaborative filtering: cold start (not enough data is available for new users or products) and concept shift (the distribution of ratings changes over time). To address these problems, we propose Modurec: an autoencoder-based method that combines all available information using the feature-wise modulation mechanism, which has demonstrated its effectiveness in several fields. While time information helps mitigate the effects of concept shift, the combination of user and item features improve prediction performance when little data is available. We show on Movielens datasets that these modifications produce state-of-the-art results in most evaluated settings compared with standard autoencoder-based methods and other collaborative filtering approaches.

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A review on recommender systems

Cited by 32 publications

References 3 publications

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

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

Análsis, diseño y desarrollo de un sistema de recomendación basado en datos restauranteros de TripAdvisor y Foursquare

Modurec: Recommender Systems with Feature and Time Modulation

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