DCRS: a deep contrast reciprocal recommender system to simultaneously capture user interest and attractiveness for online dating

2022 ◽  
Author(s):  
Linhao Luo ◽  
Xiaofeng Zhang ◽  
Xiaoyun Chen ◽  
Kai Liu ◽  
Dan Peng ◽  
...  
Keyword(s):  
Recommender System ◽  
Online Dating ◽  
User Interest

scholarly journals A Categorized Item Recommender System Coping with User Interest Changes

2014 ◽  
Vol 4 (5) ◽  
pp. 399-404 ◽  
Author(s):  
Su Sande Ko Ko ◽  
Rachsuda Jiamthapthaksin
Keyword(s):  
Recommender System ◽  
User Interest

A Dynamic Recommender System with Fused Time and Location Factors

2019 ◽  
Vol 23 (1) ◽  
pp. 25-33
Author(s):  
Xinhua Wang ◽  
Peng Yin ◽  
Yukai Gao ◽  
Lei Guo ◽  
◽  
...  
Keyword(s):  
Recommender System ◽  
User Behavior ◽  
Geographical Location ◽  
User Preferences ◽  
Dynamic Features ◽  
Fusion Model ◽  
User Interest ◽  
Location Factors ◽  
User Behavior Analysis ◽  
Real World Datasets

A recommender system is an important tool to help users obtain content and overcome information overload. It can predict users’ interests and offer recommendations by analyzing their history behaviors. However, traditional recommender systems focus primarily on static user behavior analysis. Recently, with the promotion of the Netflix recommendation prize and the open dataset with location and time information, many researchers have focused on the dynamic characteristics of the recommender system (including the changes in the dynamic model of user interest), and begun to offer recommendations based on these dynamic features. Intuitively, these dynamic user features provide us with an effective method to learn user interests deeply. Based on the observations above, we present a dynamic fusion model by integrating geographical location, user preferences, and the time factor based on the Gibbs sampling process to provide better recommendations. To evaluate the performance of our proposed method, we conducted experiments on real-world datasets. The experimental results indicate that our proposed dynamic recommender system with fused time and location factors not only performs well in traditional scenarios, but also in sparsity situations where users appear at the first time.

Reciprocal recommender system for online dating

2010 ◽  
Author(s):  
Luiz Pizzato ◽  
Tomek Rej ◽  
Thomas Chung ◽  
Irena Koprinska ◽  
Kalina Yacef ◽  
...  
Keyword(s):  
Recommender System ◽  
Online Dating

COLLABORATIVE WEB SEARCH BASED ON USER INTEREST SIMILARITY

2008 ◽  
Vol 17 (04) ◽  
pp. 495-521 ◽  
Author(s):  
DANIELA GODOY ◽  
ANALÍA AMANDI
Keyword(s):  
Recommender System ◽  
Web Search ◽  
Personal Information ◽  
Empirical Evaluation ◽  
User Interest ◽  
Agent Based ◽  
Information Agents ◽  
Personal Agents ◽  
Interest Similarity ◽  
The Web

The motivation behind personal information agents resides in the enormous amount of information available on the Web, which has created a pressing need for effective personalized techniques. In order to assists Web search these agents rely on user profiles modeling information preferences, interests and habits that help to contextualize user queries. In communities of people with similar interests, collaboration among agents fosters knowledge sharing and, consequently, potentially improves the results of individual agents by taking advantage of the knowledge acquired by other agents. In this paper, we propose an agent-based recommender system for supporting collaborative Web search in groups of users with partial similarity of interests. Empirical evaluation showed that the interaction among personal agents increases the performance of the overall recommender system, demonstrating the potential of the approach to reduce the burden of finding information on the Web.

User interest-based recommender system for image-sharing social media

2020 ◽  
Author(s):  
Kunyoung Kim ◽  
Jongmo Kim ◽  
Minhwan Kim ◽  
Mye Sohn
Keyword(s):  
Social Media ◽  
Recommender System ◽  
User Interest ◽  
Image Sharing

scholarly journals A Data-Driven Personalized Lighting Recommender System

2021 ◽  
Vol 4 ◽  
Author(s):  
Atousa Zarindast ◽  
Jonathan Wood
Keyword(s):  
Recommender System ◽  
Geographical Location ◽  
User Preferences ◽  
Geographical Information ◽  
User Interest ◽  
Smart Lighting ◽  
Home Based ◽  
Product Service ◽  
Color Scheme ◽  
Do So

Recommender systems attempt to identify and recommend the most preferable item (product-service) to individual users. These systems predict user interest in items based on related items, users, and the interactions between items and users. We aim to build an auto-routine and color scheme recommender system for home-based smart lighting that leverages a wealth of historical data and machine learning methods. We utilize an unsupervised method to recommend a routine for smart lighting. Moreover, by analyzing users’ daily logs, geographical location, temporal and usage information, we understand user preferences and predict their preferred light colors. To do so, users are clustered based on their geographical information and usage distribution. We then build and train a predictive model within each cluster and aggregate the results. Results indicate that models based on similar users increases the prediction accuracy, with and without prior knowledge about user preferences.

Building a Fuzzy Logic-Based Artificial Neural Network to Uplift Recommendation Accuracy

2019 ◽  
Vol 63 (11) ◽  
pp. 1624-1632
Author(s):  
Bam Bahadur Sinha ◽  
R Dhanalakshmi
Keyword(s):  
Neural Network ◽  
Recommender System ◽  
State Of The Art ◽  
Absolute Error ◽  
Negative Effects ◽  
User Interest ◽  
Proposed Model ◽  
Recommendation Accuracy ◽  
Membership Value ◽  
Personalized Recommender System

Abstract With the advent of the internet, the recommender system escorts the users in a customized way to nominate items from a massive set of possible alternatives. The emergence of overspecification in recommender system has emphasized negative effects on the context of prediction. The drift of user interest over time is one of the challenging affairs in present personalized recommender system. In this paper, we present a neural network model to improve the recommendation performance along with usage of fuzzy-based clustering to decide membership value of users and matching imputation to cutback sparsity to some extent. We evaluate our model on the MovieLens dataset and show that our model not only elevates accuracy, but also considers the order in which recommendation should be given. We compare the proposed model with a number of state-of-the-art personalization methods and show the dominance of our model using accuracy metrics such as root-mean-square error and mean absolute error.

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