scholarly journals Pairwise Preference Regression on Movie Recommendation System

2019 ◽  
Vol 4 (1) ◽  
pp. 57
Author(s):  
Rita Rismala ◽  
Rudy Prabowo ◽  
Agung Toto Wibowo
Keyword(s):  
Standard Deviation ◽  
Recommendation System ◽  
Cold Start ◽  
Pairwise Preference ◽  
Movie Recommendation ◽  
New Item ◽  
Cold Start Problem

Recommendation System is able to help users to choose items, including movies, that match their interests. One of the problems faced by recommendation system is cold-start problem. Cold start problem can be categorized into three types, they are: recommending existed item for new user, recommending new item for existed user, and recommending new item for new user. Pairwise preference regression is a method that directly deals with cold-start problem. This method can suggest a recommendation, not only for users who have no historical rating, but also for those who only have less demographic info. From the experiment result, the best score of Normalized Discounted Cumulative Gain (nDGC) from the system is 0.8484. The standard deviation of rating resulted by the recommendation system is 1.24, the average is 3.82. Consequently, the distribution of recommendation result is around rating 5 to 3. Those results mean that this recommendation system is good to solving cold-start problem in movie recommendation system.

scholarly journals Movie genome: alleviating new item cold start in movie recommendation

2019 ◽  
Vol 29 (2) ◽  
pp. 291-343 ◽  
Author(s):  
Yashar Deldjoo ◽  
Maurizio Ferrari Dacrema ◽  
Mihai Gabriel Constantin ◽  
Hamid Eghbal-zadeh ◽  
Stefano Cereda ◽  
...  
Keyword(s):  
Cold Start ◽  
Movie Recommendation ◽  
New Item

scholarly journals Clustering Method based on Genre Interest for Cold-Start Problem in Movie Recommendation

2013 ◽  
Vol 19 (1) ◽  
pp. 57-77 ◽  
Author(s):  
Tithrottanak You ◽  
Ahmad Nurzid Rosli ◽  
Inay Ha ◽  
Geun-Sik Jo
Keyword(s):  
Cold Start ◽  
Clustering Method ◽  
Movie Recommendation ◽  
Cold Start Problem

scholarly journals Personalized Suggestion For Music Based On Collaborative Filtering

2020 ◽  
Vol 9 (05) ◽  
pp. 25047-25051
Author(s):  
Aniket Salunke ◽  
Ruchika Kukreja ◽  
Jayesh Kharche ◽  
Amit Nerurkar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cold Start ◽  
The Internet ◽  
Music Recommendation ◽  
User Similarity ◽  
Content Based Filtering ◽  
Music Recommendation System ◽  
Cold Start Problem

With the advancement of technology there are millions of songs available on the internet and this creates problem for a person to choose from this vast pool of songs. So, there should be some middleman who must do this task on behalf of user and present most relevant songs that perfectly fits the user’s taste. This task is done by recommendation system. Music recommendation system predicts the user liking towards a particular song based on the listening history and profile. Most of the music recommendation system available today will give most recently played song or songs which have overall highest rating as suggestions to users but these suggestions are not personalized. The paper purposes how the recommendation systems can be used to give personalized suggestions to each and every user with the help of collaborative filtering which uses user similarity to give suggestions. The paper aims at implementing this idea and solving the cold start problem using content based filtering at the start.

The Comparison of Efficiency between the Recommendation Algorithm Based on Multi-Attribute Rating Matrix and the Algorithm Based on UIARM

2012 ◽  
Vol 457-458 ◽  
pp. 1544-1549
Author(s):  
Hang Yin ◽  
Gui Ran Chang ◽  
Xing Wei Wang
Keyword(s):  
Computation Time ◽  
Cold Start ◽  
Poor Quality ◽  
Recommendation Algorithm ◽  
Relevant Item ◽  
Item Attribute ◽  
Low Efficiency ◽  
Item Attributes ◽  
New Item ◽  
Cold Start Problem

this recommendation algorithm based on User-Item Attribute Rating Matrix (UIARM) can solve the cold-start problem, but the recommended low efficiency, poor quality. The use of Multi-Attribute Rating Matrix (MARM) can solve this problem; it can reduce the computation time and improve the recommendation quality effectively. The user information is analyzed to create their attribute-tables. The user's ratings are mapped to the relevant item attributes and the user's attributes respectively to generate a User Attribute-Item Attribute Rating Matrix. After UAIARM is simplified, MARM will be created. When a new item/user enters into this system, the attributes of new item/user and MARM are matched to find the N users/item with the highest match degrees as the target of the new items or the recommended items. Experiment results validate the cold-start recommendation algorithm based on MARM is efficient.

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