A hybrid approach for movie recommendation via tags and ratings

Wei, Shouxian; Zheng, Xiaolin; Chen, Deren; Chen, Chaochao

doi:10.1016/j.elerap.2016.01.003

Cited by 90 publications

(36 citation statements)

References 39 publications

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“…Authors in Ref. 21 also introduced a hybrid approach for solving the problem of¯nding the rating of unrated items in a user-item matrix through a weighted combination of user-based CF and item-based CF. These methods addressed the two major challenges of recommender systems, the accuracy of recommendations and sparsity of data, by simultaneously incorporating the correlation of users and items.…”

Section: Hybrid Recommendation Approachesmentioning

confidence: 99%

Improved Movie Recommendations Based on a Hybrid Feature Combination Method

Alshammari

Kapetanakis

Alshammari

et al. 2019

Vietnam J. Comp. Sci.

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Recommender systems help users find relevant items efficiently based on their interests and historical interactions with other users. They are beneficial to businesses by promoting the sale of products and to user by reducing the search burden. Recommender systems can be developed by employing different approaches, including collaborative filtering (CF), demographic filtering (DF), content-based filtering (CBF) and knowledge-based filtering (KBF). However, large amounts of data can produce recommendations that are limited in accuracy because of diversity and sparsity issues. In this paper, we propose a novel hybrid method that combines user–user CF with the attributes of DF to indicate the nearest users, and compare four classifiers against each other. This method has been developed through an investigation of ways to reduce the errors in rating predictions based on users’ past interactions, which leads to improved prediction accuracy in all four classification algorithms. We applied a feature combination method that improves the prediction accuracy and to test our approach, we ran an offline evaluation using the 1M MovieLens dataset, well-known evaluation metrics and comparisons between methods with the results validating our proposed method.

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Section: Hybrid Recommendation Approachesmentioning

confidence: 99%

Improved Movie Recommendations Based on a Hybrid Feature Combination Method

Alshammari

Kapetanakis

Alshammari

et al. 2019

Vietnam J. Comp. Sci.

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“…Application of tag-based recommendation have been exploited in various domains from personalized social media services [18], e-learning environments [19], personalized location recommendation [20], image search [21], personalized news recommendation [22], personalized music recommendation [23], [24], and many others. A fourth category is a hybrid approach that combines two or more of the previously mentioned categories [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems

Duong-Trung¹,

Nguyen²,

Ha³

et al. 2020

IJACSA

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A movie recommender system has been proven to be a convincing implement on carrying out comprehensive and complicated recommendation which helps users find appropriate movies conveniently. It follows a mechanism that a user can be accurately recommended movies based on other similar interests, e.g. collaborative filtering, and the movies themselves, e.g. contentbased filtering. Therefore, the systems should come with predetermined information either by users or by movies. One interesting research question should be asked: "what if this information is missing or not manually manipulated?" The problem has not been addressed in the literature, especially for the 100K and 1M variations of the MovieLens datasets. This paper exploits the movie recommender system based on movies' genres and actors/actresses themselves as the input tags or tag interpolation. We apply tag-based filtering and collaborative filtering that can effectively predict a list of movies that is similar to the movie that a user has been watched. Due to not depending on users' profiles, our approach has eliminated the effect of the coldstart problem. The experiment results obtained on MovieLens datasets indicate that the proposed model may contribute adequate performance regarding efficiency and reliability, and thus provide better-personalized movie recommendations. A movie recommender system has been deployed to demonstrate our work. The collected datasets have been published on our Github repository to encourage further reproducibility and improvement.

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“…Cosine similarity without considering the user rating scale, as in the case of [1][2][3][4][5] score, a score of more than 3 of users is their love,and for the user B, score above 4 is your love. By subtracting the average score of the user pairs, the modified cosine similarity measure improves the above problems.…”

Section: Algorithm Calculationmentioning

confidence: 99%

“…

Abstract:With the exponential growth of Internet data,the traditional stand-alone computational model has been unable to solve the real-time precise recommendation items in a complex and huge data,and the defect of traditional recommendation algorithm has become more obvious,this paper studies the collaborative filtering algorithm and matrix decomposition method,designs a parallel computing architecture based on spark,and a movie recommendation based on hybrid recommendation algorithm [1],the experimental results show that in a certain extent improves the recommendation accuracy and scalability,and has good acceleration effect.

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mentioning

confidence: 99%