scholarly journals A Movie Recommendation using Common Genre Relation on User-Item Subgroup

2019 ◽  
Vol 9 (1S3) ◽  
pp. 193-199
Keyword(s):  
Recommendation System ◽  
Side Information ◽  
Individual Preference ◽  
Graph Model ◽  
Search Space ◽  
Vital Role ◽  
Personalized Recommendation ◽  
Movie Recommendation ◽  
The Individual ◽  
The Empirical Analysis

Movie recommendation system has played a vital role in retrieving the movies that are of interest to the user. Most of the traditional methods provide a unified recommendation without considering the individual preference of the user. To address this challenge, various recommender methods are currently employing side information like location, time, gender, and genre to provide a personalized recommendation. In this paper, we propose —Common Genre Relations (COGS), which incorporates the information on genre relationships between the movies. Meanwhile, the method reduces the search space for each user and helps to mitigate the sparsity problem. To improve the scalability, the methods are executed on user-item subgroups. Extensive experiments are conducted on a real-world dataset. The empirical analysis shows that the proposed method based on the graph model excels the accuracy at top-k than the state-of-art collaborative filtering methods.

scholarly journals Movie Recommendation System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4778-4781
Author(s):  
Ghanashyam Vibhandik
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cosine Similarity ◽  
Daily Lives ◽  
Movie Content ◽  
Movie Recommendation ◽  
The Many ◽  
The Individual ◽  
Content Based Filtering ◽  
Filtering Approach

Movies are very significant in our lives. It is one of the many forms of entertainment that we encounter in our daily lives. It is up to the individual to decide whatever type of film they choose to see, whether it is a comedy, romantic film, action film, or adventure film. However, the issue is locating acceptable content, as there is a large amount of information created each year. As a result, finding our favourite film is really difficult. The goal of this research is to improve the regular filtering technique's performance and accuracy. A recommendation system can be implemented using a variety of approaches. Content-based filtering and collaborative filtering strategies are employed in this work. The content-based filtering approach analyses the user's history/past behaviour and recommends a list of comparable movies depending on their input. K-NN algorithms and collaborative filtering are also employed in this paper to improve the accuracy of the results. Cosine similarity is utilised in this work to quickly discover comparable information. The correctness of the cosine angle is measured by cosine similarity. People may quickly find their favourite movie content thanks to all of this.

scholarly journals Key Technologies and Discrete Dynamic Modeling Analysis of Online Travel Planning System Based on Big Data Scenario Aware Service

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yange Hao ◽  
Na Song
Keyword(s):  
Recommendation System ◽  
Performance Test ◽  
Scene Perception ◽  
Search Space ◽  
Test Method ◽  
Planning System ◽  
Personalized Recommendation ◽  
Travel Planning ◽  
Key Technologies ◽  
Online Travel

The key technology of online travel recommendation system has been widely concerned by many Internet experts. This paper studies and designs a scenario aware service model in online travel planning system and proposes an online travel planning recommendation model which integrates collaborative filtering and clustering personalized recommendation algorithm. At the same time, the algorithm performance test method and model evaluation index are given. The results show that CTTCF algorithm can find more neighbor users than UCF algorithm, and the smaller the search space is, the more significant the advantage is. The number of neighbors is 5, 10, 15, 20, and 25, respectively, and the corresponding average absolute error values are about 0.815, 0.785, 0.765, 0.758, and 0.755, respectively. The scores of the six emotional travel itinerary recommendation schemes are all higher than 142 points. Only the two schemes have no obvious rendering effect. The proposed online travel itinerary planning scheme has potential value and important significance in the application of follow-up recommendation system. It solves the problem of low scene perception satisfaction in the key technologies of online tourism planning system.

scholarly journals Movie Recommendation System

2021 ◽  
Vol 9 (VI) ◽  
pp. 4919-4924
Author(s):  
Dinesh Shishodia
Keyword(s):  
User Experience ◽  
Recommendation System ◽  
Online Dating ◽  
Vital Role ◽  
Music Videos ◽  
Large Set ◽  
Past Behavior ◽  
System Recommendation ◽  
Movie Recommendation

This paper represents the overview of Approaches and techniques used in Movie Recommendation system. Recommendation system is used by many companies like Netflix, Amazon, Flipkart etc. It makes the user experience better and decrease the user efforts. It plays a very vital role in our day-to-day life. It is used in recommending Movies, Articles, News, Books, Music, Videos, People (Online Dating) etc. It learns from the user past behavior and based on that behavior it recommends item to the user. Likewise, in Movie Recommendation system movie is recommended to the user on the basis of movies watched, liked, rated by the user. In year 2020, approximate 10,000 movie were launched according to IDMB data. It saves a lot of times and efforts of the user by suggesting movies according to user taste and user don’t have to select a movie from a large set of movies.

scholarly journals Movie Recommendation System using Cosine Similarity and KNN

2020 ◽  
Vol 9 (5) ◽  
pp. 556-559
Keyword(s):  
Recommendation System ◽  
Information Overload ◽  
Vital Role ◽  
The Internet ◽  
Learning Approaches ◽  
Meaningful Information ◽  
The Past ◽  
Pros And Cons ◽  
Movie Recommendation ◽  
Insight Into

Over the past years, the internet has broadened the horizon of various domains to interact and share meaningful information. As it is said that everything has its pros and cons therefore, along with the expansion of domain comes information overload and difficulty in extraction of data. To overcome this problem the recommendation system plays a vital role. It is used to enhance the user experience by giving fast and coherent suggestions. This paper describes an approach which offers generalized recommendations to every user, based on movie popularity and/or genre. Content-Based Recommender System is implemented using various deep learning approaches. This paper also gives an insight into problems which are faced in content-based recommendation system and we have made an effort to rectify them.

An interpretable mechanism for personalized recommendation based on cross feature

2021 ◽  
pp. 1-12
Author(s):  
Lv YE ◽  
Yue Yang ◽  
Jian-Xu Zeng
Keyword(s):  
Recommender System ◽  
Online Shopping ◽  
Side Information ◽  
Attention Mechanism ◽  
Personalized Recommendation ◽  
Model Based ◽  
Embedded Model ◽  
Feature Attention ◽  
Match Score

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent.

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