scholarly journals Addressing Item-Cold Start Problem in Recommendation Systems Using Model Based Approach and Deep Learning

2017 ◽  
pp. 176-185 ◽  
Author(s):  
Ivica Obadić ◽  
Gjorgji Madjarov ◽  
Ivica Dimitrovski ◽  
Dejan Gjorgjevikj
Keyword(s):  
Deep Learning ◽  
Recommendation Systems ◽  
Cold Start ◽  
Model Based ◽  
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.

scholarly journals A Mapping Approach to Identify Player Types for Game Recommendations

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 379
Author(s):  
Yeonghun Lee ◽  
Yuchul Jung
Keyword(s):  
Diagnostic Test ◽  
Rapid Development ◽  
Recommendation Systems ◽  
Cold Start ◽  
Challenging Problem ◽  
Gaming Industry ◽  
Mapping Model ◽  
Existing Problems ◽  
Android App ◽  
Cold Start Problem

As the size of the domestic and international gaming industry gradually grows, various games are undergoing rapid development cycles to compete in the current market. However, selecting and recommending suitable games for users continues to be a challenging problem. Although game recommendation systems based on the prior gaming experience of users exist, they are limited owing to the cold start problem. Unlike existing approaches, the current study addressed existing problems by identifying the personality of the user through a personality diagnostic test and mapping the personality to the player type. In addition, an Android app-based prototype was developed that recommends games by mapping tag information about the user’s personality and the game. A set of user experiments were conducted to verify the feasibility of the proposed mapping model and the recommendation prototype.

scholarly journals Simple and Efficient Computational Intelligence Strategies for Effective Collaborative Decisions

2019 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Emelia Opoku Aboagye ◽  
Rajesh Kumar
Keyword(s):  
Deep Learning ◽  
Feature Space ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Tensor Factorization ◽  
Scalable Systems ◽  
Collaborative Recommendation ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Cold Start Problem

We approach scalability and cold start problems of collaborative recommendation in this paper. An intelligent hybrid filtering framework that maximizes feature engineering and solves cold start problem for personalized recommendation based on deep learning is proposed in this paper. Present e-commerce sites mainly recommend pertinent items or products to a lot of users through personalized recommendation. Such personalization depends on large extent on scalable systems which strategically responds promptly to the request of the numerous users accessing the site (new users). Tensor Factorization (TF) provides scalable and accurate approach for collaborative filtering in such environments. In this paper, we propose a hybrid-based system to address scalability problems in such environments. We propose to use a multi-task approach which represent multiview data from users, according to their purchasing and rating history. We use a Deep Learning approach to map item and user inter-relationship to a low dimensional feature space where item-user resemblance and their preferred items is maximized. The evaluation results from real world datasets show that, our novel deep learning multitask tensor factorization (NeuralFil) analysis is computationally less expensive, scalable and addresses the cold-start problem through explicit multi-task approach for optimal recommendation decision making.

Eliminating Cold-Start Problem of Music Recommendation through SOM Based Sampling

2013 ◽  
Vol 278-280 ◽  
pp. 1119-1123
Author(s):  
Ning Han Liu ◽  
Cheng Yu Chiang ◽  
Hsiang Ming Hsu
Keyword(s):  
Recommendation Systems ◽  
Cold Start ◽  
Random Selection ◽  
Music Recommendation ◽  
General Systems ◽  
Cold Start Problem

Recommendation systems have a prevalent cold-start problem. The problem is occurred might due to new users or new items (music) are added into the system. In this paper, the meaning of the cold-start is narrowed to that the systems do not understand the new user’s preferences. Therefore, systems can not recommend the music to users. Although many recommendation systems have a solution to reduce the cold-start, e.g., general systems utilize random to select songs. The systems random select some music works to user so that systems will know the user’s preferences after they rated the music works. However, systems may cost much time to collect the necessary information when the new user is interesting in some special types of music. Therefore, if systems select various type of music initially, the user’s preferences will be extracted more quickly. That is the cold-start problem can be reduced when the types of initial recommended music are various. In our approach, we utilize SOM to select some music from clusters. According to experiment, SOM selects type of music more average than k-means and random selection. Therefore, SOM can improve the cold-start problem and increase the precision of recommendation results.

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