A replication study on implicit feedback recommender systems with application to the data visualization recommendation

2021 ◽  
Author(s):  
Parisa Lak ◽  
Aysun Bozanta ◽  
Can Kavaklioglu ◽  
Mucahit Cevik ◽  
Ayse Basar ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Data Visualization ◽  
Replication Study ◽  
Implicit Feedback

TrustTF: A tensor factorization model using user trust and implicit feedback for context-aware recommender systems

2020 ◽  
Vol 209 ◽  
pp. 106434
Author(s):  
Jianli Zhao ◽  
Wei Wang ◽  
Zipei Zhang ◽  
Qiuxia Sun ◽  
Huan Huo ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Implicit Feedback ◽  
Context Aware ◽  
Tensor Factorization ◽  
Factorization Model ◽  
User Trust

Resolving data sparsity by multi-type auxiliary implicit feedback for recommender systems

2017 ◽  
Vol 138 ◽  
pp. 202-207 ◽  
Author(s):  
Guibing Guo ◽  
Huihuai Qiu ◽  
Zhenhua Tan ◽  
Yuan Liu ◽  
Jing Ma ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Implicit Feedback ◽  
Data Sparsity

scholarly journals Re-Enrichment Learning: Metadata Saliency for the Evolutive Personalization of a Recommender System

2021 ◽  
Vol 11 (4) ◽  
pp. 1733
Author(s):  
Yuseok Ban ◽  
Kyungjae Lee
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Experimental Results ◽  
Implicit Feedback ◽  
Digital Platforms ◽  
Novel Approach ◽  
Domain Transfer ◽  
Explicit Feedback

Many studies have been conducted on recommender systems in both the academic and industrial fields, as they are currently broadly used in various digital platforms to make personalized suggestions. Despite the improvement in the accuracy of recommenders, the diversity of interest areas recommended to a user tends to be reduced, and the sparsity of explicit feedback from users has been an important issue for making progress in recommender systems. In this paper, we introduce a novel approach, namely re-enrichment learning, which effectively leverages the implicit logged feedback from users to enhance user retention in a platform by enriching their interest areas. The approach consists of (i) graph-based domain transfer and (ii) metadata saliency, which (i) find an adaptive and collaborative domain representing the relations among many users’ metadata and (ii) extract attentional features from a user’s implicit logged feedback, respectively. The experimental results show that our proposed approach has a better capacity to enrich the diversity of interests of a user by means of implicit feedback and to help recommender systems achieve more balanced personalization. Our approach, finally, helps recommenders improve user retention, i.e., encouraging users to click more items or dwell longer on the platform.

scholarly journals Learning to Rank for Personalised Fashion Recommender Systems via Implicit Feedback

2014 ◽  
pp. 51-61 ◽  
Author(s):  
Hai Thanh Nguyen ◽  
Thomas Almenningen ◽  
Martin Havig ◽  
Herman Schistad ◽  
Anders Kofod-Petersen ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Learning To Rank ◽  
Implicit Feedback

scholarly journals Towards Comprehensive Recommender Systems: Time-Aware Unified Recommendations Based on Listwise Ranking of Implicit Cross-Network Data

2020 ◽  
Vol 34 (01) ◽  
pp. 189-197
Author(s):  
Dilruk Perera ◽  
Roger Zimmermann
Keyword(s):  
Recommender Systems ◽  
Web Applications ◽  
Auxiliary Information ◽  
Optimization Criterion ◽  
User Preferences ◽  
Implicit Feedback ◽  
Multiple Time ◽  
Ranking Problem ◽  
Target Network ◽  
Time Aware

The abundance of information in web applications make recommendation essential for users as well as applications. Despite the effectiveness of existing recommender systems, we find two major limitations that reduce their overall performance: (1) inability to provide timely recommendations for both new and existing users by considering the dynamic nature of user preferences, and (2) not fully optimized for the ranking task when using implicit feedback. Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users. Furthermore, we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a list of items. We illustrate our cross-network model using Twitter auxiliary information for recommendations on YouTube target network. Extensive comparisons against multiple time aware and cross-network baselines show that the proposed solution is superior in terms of accuracy, novelty and diversity. Furthermore, experiments conducted on the popular MovieLens dataset suggest that the proposed listwise ranking method outperforms existing state-of-the-art ranking techniques.

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