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.

Research for Cold-Start Problem in Network-Based Recommendation Algorithm

2013 ◽  
Vol 462-463 ◽  
pp. 861-867
Author(s):  
Li Min Liu ◽  
Chen Yang Zhang ◽  
Zhi Qiang Ma ◽  
Yu Hong Xiao
Keyword(s):  
Cold Start ◽  
Recommendation Algorithm ◽  
New Item ◽  
Cold Start Problem

Network-based recommendation algorithm presents a good recommended result in many aspects. The algorithm is also facing the problem of cold-start. This paper proposes a solution for cold-start problem which makes use of an algorithm based on items similarity to calculate the similarity between the new item and other items in the system, and then link the new item to the user-item matrix. Finally the new items can be recommended to users by the network-based recommendation algorithm what the traditional network-based recommendation algorithm can't do. Therefore, the problem is solved on certain degree.

scholarly journals Addressing Complete New Item Cold-Start Recommendation: A Niche Item-Based Collaborative Filtering via Interrelationship Mining

2019 ◽  
Vol 9 (9) ◽  
pp. 1894 ◽  
Author(s):  
Zhi-Peng Zhang ◽  
Yasuo Kudo ◽  
Tetsuya Murai ◽  
Yong-Gong Ren
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Cold Start ◽  
Binary Relations ◽  
Excellent Performance ◽  
Satisfactory Accuracy ◽  
Available Information ◽  
Item Attributes ◽  
New Item ◽  
Cold Start Problem

Recommender system (RS) can be used to provide personalized recommendations based on the different tastes of users. Item-based collaborative filtering (IBCF) has been successfully applied to modern RSs because of its excellent performance, but it is susceptible to the new item cold-start problem, especially when a new item has no rating records (complete new item cold-start). Motivated by this, we propose a niche approach which applies interrelationship mining into IBCF in this paper. The proposed approach utilizes interrelationship mining to extract new binary relations between each pair of item attributes, and constructs interrelated attributes to rich the available information on a new item. Further, similarity, computed using interrelated attributes, can reflect characteristics between new items and others more accurately. Some significant properties, as well as the usage of interrelated attributes, are provided in detail. Experimental results obtained suggest that the proposed approach can effectively solve the complete new item cold-start problem of IBCF and can be used to provide new item recommendations with satisfactory accuracy and diversity in modern RSs.

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 An item-oriented recommendation algorithm on cold-start problem

2011 ◽  
Vol 95 (5) ◽  
pp. 58003 ◽  
Author(s):  
Tian Qiu ◽  
Guang Chen ◽  
Zi-Ke Zhang ◽  
Tao Zhou
Keyword(s):  
Cold Start ◽  
Recommendation Algorithm ◽  
Cold Start Problem

scholarly journals Dealing with Pure New User Cold-Start Problem in Recommendation System Based on Linked Open Data and Social Network Features

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Usha Yadav ◽  
Neelam Duhan ◽  
Komal Kumar Bhatia
Keyword(s):  
Social Network ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Open Data ◽  
Computation Time ◽  
Cold Start ◽  
Linked Open Data ◽  
Hybrid Recommendation ◽  
Cold Start Problem

Preferring accuracy over computation time or vice versa is very challenging in the context of recommendation systems, which encourages many researchers to opt for hybrid recommendation systems. Currently, researchers are trying hard to produce correct and accurate recommendations by suggesting the use of ontology, but the lack of techniques renders to take its full advantage. One of the major issues in recommender systems bothering many researchers is pure new user cold-start problem which arises due to the absence of information in the system about the new user. Linked Open Data (LOD) initiative sets standards for interoperability among cross domains and has gathered enormous amount of data over the past years, which provides various ways by which recommender system’s performance can be improved by enriching user’s profile with relevant features. This research work focuses on solving pure new user cold-start problem by building user’s profile based on LOD, collaborative features, and social network-based features. Here, a new approach is devised to compute item similarity based on ontology, thus predicting the rating of nonrated item. A modified method to calculate user’s similarity based on collaborative features to deal with other issues such as accuracy and computation time is also proposed. The empirical results and comparative analysis of the proposed hybrid recommendation system dictate its better performance specifically for providing solution to pure new user cold-start problem.

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