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

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

scholarly journals A Survey of Collaborative Filtering Techniques

2009 ◽  
Vol 2009 ◽  
pp. 1-19 ◽  
Author(s):  
Xiaoyuan Su ◽  
Taghi M. Khoshgoftaar
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Privacy Protection ◽  
State Of The Art ◽  
Predictive Performance ◽  
The State ◽  
Data Sparsity ◽  
Research And Practice ◽  
Model Based ◽  
Comprehensive Survey

As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we first introduce CF tasks and their main challenges, such as data sparsity, scalability, synonymy, gray sheep, shilling attacks, privacy protection, etc., and their possible solutions. We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address the challenges. From basic techniques to the state-of-the-art, we attempt to present a comprehensive survey for CF techniques, which can be served as a roadmap for research and practice in this area.

Knowledge-Guided Disentangled Representation Learning for Recommender Systems

2022 ◽  
Vol 40 (1) ◽  
pp. 1-26
Author(s):  
Shanlei Mu ◽  
Yaliang Li ◽  
Wayne Xin Zhao ◽  
Siqing Li ◽  
Ji-Rong Wen
Keyword(s):  
Recommender Systems ◽  
Semantic Information ◽  
Representation Learning ◽  
Interaction Data ◽  
Data Sparsity ◽  
Proposed Model ◽  
Information Maximization ◽  
Alignment Strategy ◽  
Real World Datasets ◽  
Mutual Information Maximization

In recommender systems, it is essential to understand the underlying factors that affect user-item interaction. Recently, several studies have utilized disentangled representation learning to discover such hidden factors from user-item interaction data, which shows promising results. However, without any external guidance signal, the learned disentangled representations lack clear meanings, and are easy to suffer from the data sparsity issue. In light of these challenges, we study how to leverage knowledge graph (KG) to guide the disentangled representation learning in recommender systems. The purpose for incorporating KG is twofold, making the disentangled representations interpretable and resolving data sparsity issue. However, it is not straightforward to incorporate KG for improving disentangled representations, because KG has very different data characteristics compared with user-item interactions. We propose a novel K nowledge-guided D isentangled R epresentations approach ( KDR ) to utilizing KG to guide the disentangled representation learning in recommender systems. The basic idea, is to first learn more interpretable disentangled dimensions (explicit disentangled representations) based on structural KG, and then align implicit disentangled representations learned from user-item interaction with the explicit disentangled representations. We design a novel alignment strategy based on mutual information maximization. It enables the KG information to guide the implicit disentangled representation learning, and such learned disentangled representations will correspond to semantic information derived from KG. Finally, the fused disentangled representations are optimized to improve the recommendation performance. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model in terms of both performance and interpretability.

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.

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