scholarly journals IPTV program recommendation based on combination strategies

2018 ◽  
Vol 169 ◽  
pp. 01003 ◽  
Author(s):  
Hao Li ◽  
Huan Xia ◽  
Yan Kang ◽  
Mohammad Nashir Uddin
Keyword(s):  
Recommendation System ◽  
Cold Start ◽  
Optimal Parameters ◽  
User Interest ◽  
Combination Strategy ◽  
Change Over Time ◽  
Combination Strategies ◽  
Content Based Filtering ◽  
Cold Start Problem ◽  
Over Time

As a new interactive service technology, IPTV has been extensively studying in the field of TV pro-gram recommendation, but the sparse of the user-program rating matrix and the cold-start problem is a bottleneck that the program recommended accurately. In this paper, a flexible combination of two recommendation strategies proposed, which explored the sparse and cold-start problem as well as the issue of user interest change over time. This paper achieved content-based filtering section and collaborative filtering section according to the two combination strategies, which effectively solved the cold-start program and over the sparse problem and the problem of users interest change over time. The experimental results showed that this combinational recommendation system in optimal parameters compared by using any one of two combination strategies or not using any combination strategy at all, and the reducing range of MAE is [2.7%,3%].The increasing range of precision and recall is [13.8%95.5%] and [0,97.8%], respectively. The experiment showed better results when using combinational recommendation system in optimal parameters than using each combination strategies individually or not using any combination strategy.

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 recommendation system for scientific water data

2021 ◽  
Author(s):  
Zhaokun Xue ◽  
Alva Couch
Keyword(s):  
Data Sharing ◽  
Topic Modeling ◽  
Recommendation System ◽  
Latent Dirichlet Allocation ◽  
User Behavior ◽  
Change Over Time ◽  
Task Oriented ◽  
Content Based Filtering ◽  
Over Time

AbstractWe describe a recommendation system for HydroShare, a platform for scientific water data sharing. We discuss similarities, differences and challenges for implementing recommendation systems for scientific water data sharing. We discuss and analyze the behaviors that scientists exhibit in using HydroShare as documented by users’ activity logs. Unlike entertainment system users, users on HydroShare tend to be task-oriented, where the set of tasks of interest can change over time, and older interests are sometimes no longer relevant. By validating recommendation approaches against user behavior as expressed in activity logs, we conclude that a combination of content-based filtering and a latent Dirichlet allocation (LDA) topic modeling of user behavior—rather than and instead of LDA classification of dataset topics—provides a workable solution for HydroShare and compares this approach to existing recommendation methods.

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 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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