Link prediction in recommender systems with confidence measures

2019 ◽  
Vol 29 (8) ◽  
pp. 083133 ◽  
Author(s):  
Zhan Su ◽  
Xiliang Zheng ◽  
Jun Ai ◽  
Lihui Shang ◽  
Yuming Shen
Keyword(s):  
Recommender Systems ◽  
Link Prediction ◽  
Confidence Measures

Link prediction in recommender systems based on vector similarity

2020 ◽  
Vol 560 ◽  
pp. 125154 ◽  
Author(s):  
Zhan Su ◽  
Xiliang Zheng ◽  
Jun Ai ◽  
Yuming Shen ◽  
Xuanxiong Zhang
Keyword(s):  
Recommender Systems ◽  
Link Prediction

scholarly journals A Link Prediction Strategy for Personalized Tweet Recommendation through Doc2Vec Approach

2017 ◽  
Vol 2 (4) ◽  
pp. 63 ◽  
Author(s):  
Mojtaba Zahedi Amiri ◽  
Abdullah Shobi
Keyword(s):  
Recommender Systems ◽  
Link Prediction ◽  
Recommendation System ◽  
Information Overload ◽  
Irrelevant Information ◽  
The Other ◽  
Research Subject ◽  
Huge Amount ◽  
Main Research ◽  
Prediction Strategy

<p><em>Nowadays with growth of using Internet as a principle way of communication, likes different social medias channels (Twitter,</em><em> </em><em>Facebook,</em><em> </em><em>etc</em><em>.</em><em>) and also access to huge amount of information like News, there appear a main research subject to help users to find his/her interests among vast amount of relevant and irrelevant information. Recommender systems are helped to handle information overload problem and in this paper we introduce our Tweet Recommendation System that implement user</em><em>’</em><em>s Twitter information (Tweets, Retweet, Like,...) as a source of user’s information. In this work the semantic of tweets that regard as a User’s Explicit Interests</em><em> </em><em>(e.g.</em><em>,</em><em> person, events, product mentioned in user’s tweets) are identified with the Doc2vec approach and recommend similar tweets through link-prediction strategy. The experiment results show that Doc2Vec approach is a</em><em> </em><em>better approach than the other previous approaches.</em></p>

scholarly journals A Similarity-Inclusive Link Prediction Based Recommender System Approach

2019 ◽  
Vol 25 (6) ◽  
pp. 62-69 ◽  
Author(s):  
Zuhal Kurt ◽  
Kemal Ozkan ◽  
Alper Bilge ◽  
Omer Nezih Gerek
Keyword(s):  
Recommender Systems ◽  
Bipartite Graph ◽  
Recommender System ◽  
Link Prediction ◽  
Prediction Method ◽  
Research Field ◽  
Complex Domain ◽  
Proposed Model ◽  
Prediction Techniques ◽  
Item Item

Despite being a challenging research field with many unresolved problems, recommender systems are getting more popular in recent years. These systems rely on the personal preferences of users on items given in the form of ratings and return the preferable items based on choices of like-minded users. In this study, a graph-based recommender system using link prediction techniques incorporating similarity metrics is proposed. A graph-based recommender system that has ratings of users on items can be represented as a bipartite graph, where vertices correspond to users and items and edges to ratings. Recommendation generation in a bipartite graph is a link prediction problem. In current literature, modified link prediction approaches are used to distinguish between fundamental relational dualities of like vs. dislike and similar vs. dissimilar. However, the similarity relationship between users/items is mostly disregarded in the complex domain. The proposed model utilizes user-user and item-item cosine similarity value with the relational dualities in order to improve coverage and hits rate of the system by carefully incorporating similarities. On the standard MovieLens Hetrec and MovieLens datasets, the proposed similarity-inclusive link prediction method performed empirically well compared to other methods operating in the complex domain. The experimental results show that the proposed recommender system can be a plausible alternative to overcome the deficiencies in recommender systems.

Link prediction in recommender systems based on multi-factor network modeling and community detection

2019 ◽  
Vol 126 (3) ◽  
pp. 38003 ◽  
Author(s):  
Jun Ai ◽  
Yayun Liu ◽  
Zhan Su ◽  
Hui Zhang ◽  
Fengyu Zhao
Keyword(s):  
Recommender Systems ◽  
Community Detection ◽  
Link Prediction ◽  
Network Modeling

scholarly journals Keywords-Driven and Weight-aware Paper Recommendation via Paper Correlation Pattern Mining

2021 ◽  
Author(s):  
Hanwen Liu
Keyword(s):  
Recommender Systems ◽  
Link Prediction ◽  
Pattern Mining ◽  
Correlation Pattern ◽  
Relation Graph ◽  
Prediction Approach ◽  
Tools And Methods

Abstract Nowadays, recommender systems have become one of the main tools and methods for users to search for their interested papers from massive candidates. Considering the above drawbacks, in this paper, we propose a link prediction approach that combines time, keywords and authors information for constructing a new relation graph. Finally, a case study is employed to explain our approach step by step and demonstrate the feasibility of our proposal.

Context-Aware Recommender Systems

2012 ◽  
Vol 23 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Li-Cai WANG ◽  
Xiang-Wu MENG ◽  
Yu-Jie ZHANG
Keyword(s):  
Recommender Systems ◽  
Context Aware
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