Identification Cold start Problem Start in Matrix Factorization Method for Online Web Recommendation Systems

2020 ◽  
Author(s):  
Nitin Zaware
Keyword(s):  
Matrix Factorization ◽  
Recommendation Systems ◽  
Cold Start ◽  
Factorization Method ◽  
Cold Start Problem ◽  
Online Web

scholarly journals Predicting the side effects of drugs using matrix factorization on spontaneous reporting database

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kohei Fukuto ◽  
Tatsuya Takagi ◽  
Yu-Shi Tian
Keyword(s):  
Side Effects ◽  
Matrix Factorization ◽  
Side Effect ◽  
Cold Start ◽  
Factorization Method ◽  
Mapping Method ◽  
Spontaneous Reports ◽  
The Matrix ◽  
History Of ◽  
Cold Start Problem

AbstractThe severe side effects of some drugs can threaten the lives of patients and financially jeopardize pharmaceutical companies. Computational methods utilizing chemical, biological, and phenotypic features have been used to address this problem by predicting the side effects. Among these methods, the matrix factorization method, which utilizes the side-effect history of different drugs, has yielded promising results. However, approaches that encapsulate all the characteristics of side-effect prediction have not been investigated to date. To address this gap, we applied the logistic matrix factorization algorithm to a database of spontaneous reports to construct a prediction with higher accuracy. We expressed the distinction in the importance of drug-side effect pairs by a weighting strategy and addressed the cold-start problem via an attribute-to-feature mapping method. Consequently, our proposed model improved the prediction accuracy by 2.5% and efficiently handled the cold-start problem. The proposed methodology is expected to benefit applications such as warning systems in clinical settings.

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 Convolutional Neural Network and Matrix Factorization-Based Travel Location Recommendation Method Using Community-Contributed Geotagged Photos

2020 ◽  
Vol 9 (8) ◽  
pp. 464
Author(s):  
Thaair Ameen ◽  
Ling Chen ◽  
Zhenxing Xu ◽  
Dandan Lyu ◽  
Hongyu Shi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Matrix Factorization ◽  
Factorization Method ◽  
Experimental Results ◽  
Latent Factor ◽  
Location Recommendation ◽  
Weighted Matrix ◽  
Geotagged Photos ◽  
Cold Start Problem

Travel location recommendation methods using community-contributed geotagged photos are based on past check-ins. Therefore, these methods cannot effectively work for new travel locations, i.e., they suffer from the travel location cold start problem. In this study, we propose a convolutional neural network and matrix factorization-based travel location recommendation method to address the problem. Specifically, a weighted matrix factorization method is used to obtain the latent factor representations of travel locations. The latent factor representation for a new travel location is estimated from its photos by using a convolutional neural network. Experimental results on a Flickr dataset demonstrate that the proposed method can provide better recommendations than existing methods.

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.

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.

A Novel Overlapping Method to Alleviate the Cold-Start Problem in Recommendation Systems

2021 ◽  
Vol 31 (09) ◽  
pp. 1277-1297
Author(s):  
Ali M. Ahmed Al-Sabaawi ◽  
Hacer Karacan ◽  
Yusuf Erkan Yenice
Keyword(s):  
Social Relations ◽  
Average Distance ◽  
Recommendation Systems ◽  
Cold Start ◽  
Absolute Error ◽  
Sufficient Information ◽  
Partitioning Around Medoids ◽  
Real World Datasets ◽  
Overlapping Method ◽  
Cold Start Problem

Recommendation systems (RSs) are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The main objective of RSs is to tool up users with desired items that meet their preferences. A major problem in RSs is called: “cold-start”; it is a potential problem called so in computer-based information systems which comprises a degree of automated data modeling. Particularly, it concerns the issue in which the system cannot draw any inferences nor have it yet gathered sufficient information about users or items. Since RSs performance is substantially limited by cold-start users and cold-start items problems; this research study takes the route for a major aim to attenuate users’ cold-start problem. Still in the process of researching, sundry studies have been conducted to tackle this issue by using clustering techniques to group users according to their social relations, their ratings or both. However, a clustering technique disregards a variety of users’ tastes. In this case, the researcher has adopted the overlapping technique as a tool to deal with the clustering technique’s defects. The advantage of the overlapping technique excels over others by allowing users to belong to multi-clusters at the same time according to their behavior in the social network and ratings feedback. On that account, a novel overlapping method is presented and applied. This latter is executed by using the partitioning around medoids (PAM) algorithm to implement the clustering, which is achieved by means of exploiting social relations and confidence values. After acquiring users’ clusters, the average distances are computed in each cluster. Thereafter, a content comparison is made regarding the distances between every user and the computed distances of the clusters. If the comparison result is less than or equal to the average distance of a cluster, a new user is added to this cluster. The singular value decomposition plus (SVD[Formula: see text]) method is then applied to every cluster to compute predictions values. The outcome is calculated by computing the average of mean absolute error (MAE) and root mean square error (RMSE) for every cluster. The model is tested by two real world datasets: Ciao and FilmTrust. Ultimately, findings have exhibited a great deal of insights on how the proposed model outperformed a number of the state-of-the-art studies in terms of prediction accuracy.

Sign in / Sign up

Export Citation Format

Share Document