Recommendation Systems with Machine Learning

Fanca, Alexandra; Puscasiu, Adela; Gota, Dan-Ioan; Vălean, Honoriu

doi:10.1109/iccc49264.2020.9257290

Cited by 17 publications

(2 citation statements)

References 13 publications

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“…Recall measures the model's ability to correctly identify positive instances, minimizing false negatives. The generalized equations for the measurements are realized as (18), (19), and (20).…”

Section: Measurements 71 Accuracy Precision and Recallmentioning

confidence: 99%

Personalized Tourist Recommender System: A Data-Driven and Machine-Learning Approach

Shrestha,

Wenan,

Shrestha

et al. 2024

Computation

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This study introduces a data-driven and machine-learning approach to design a personalized tourist recommendation system for Nepal. It examines key tourist attributes, such as demographics, behaviors, preferences, and satisfaction, to develop four sub-models for data collection and machine learning. A structured survey is conducted with 2400 international and domestic tourists, featuring 28 major questions and 125 variables. The data are preprocessed, and significant features are extracted to enhance the accuracy and efficiency of the machine-learning models. These models are evaluated using metrics such as accuracy, precision, recall, F-score, ROC, and lift curves. A comprehensive database for Pokhara City, Nepal, is developed from various sources that includes attributes such as location, cost, popularity, rating, ranking, and trend. The machine-learning models provide intermediate categorical recommendations, which are further mapped using a personalized recommender algorithm. This algorithm makes decisions based on weights assigned to each decision attribute to make the final recommendations. The system’s performance is compared with other popular recommender systems implemented by TripAdvisor, Google Maps, the Nepal tourism website, and others. It is found that the proposed system surpasses existing ones, offering more accurate and optimized recommendations to visitors in Pokhara. This study is a pioneering one and holds significant implications for the tourism industry and the governing sector of Nepal in enhancing the overall tourism business.

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Section: Measurements 71 Accuracy Precision and Recallmentioning

confidence: 99%

Personalized Tourist Recommender System: A Data-Driven and Machine-Learning Approach

Shrestha,

Wenan,

Shrestha

et al. 2024

Computation

View full text Add to dashboard Cite

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“…Paper [3] discusses about the study of construction and contrast of many recommendation systems capable of producing item suggestions based on customer, product, and customer-product interaction data, as well as the usage of various machine learning techniques. In addition, several strategies for applying machine learning models to develop recommendation systems, as well as teaching, assessing, and contrasting the various methods in order to produce a broad yet precise ranking prediction answer.…”

Section: II Work In This Areamentioning

confidence: 99%

Movie Recommendation System Using Machine Learning Algorithms

Jadhav¹,

KB²

2022

JMC

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Movies are one kind of enjoyment, however the challenge is locating the specified resources among the lots of movies discharged annually. To search out a show what users are seeking out through this technology is incredibly troublesome. However, recommendation structures return abundant handier in these things. Searching is the first step depending on which the movies are recommended to the user. To search a movie the user may use any of the parameter like name of the actor, director, genres etc. Searching based on one of the parameter and recommending movies based on the parameter is how the current recommendation system works. The movies are recommended with the help of filtering technique i,e collaborative filtering which recommends the movies based on user preference and ratings of the movies. This can be further enhanced by taking multiple parameters (2 in this case) for searching the movies based on which the recommendation can be done. With the help of multiple parameters (2 in this case) the user can be more specific in what he wants to search so that it helps in more accurate recommendations of the movies.

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