# Recommender Systems and Techniques

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/recommender-systems-and-techniques/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the advancements in recommender system technologies, including collaborative filtering, matrix factorization, deep learning, content-based recommendation, web mining, context-aware recommender systems, neural networks, user modeling, and trust-aware recommender systems. The papers cover various techniques and methodologies for improving recommendation accuracy and addressing challenges such as cold start problems and privacy concerns. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t10203 |
| Works | 72 |

## Topic papers all

Showing 15 of 72.

- [Accurately interpreting clickthrough data as implicit feedback](https://scholariq.org/papers/accurately-interpreting-clickthrough-data-as-implicit-feedback/)
- [Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search](https://scholariq.org/papers/evaluating-the-accuracy-of-implicit-feedback-from-clicks-and-query/)
- [Graph Contextualized Self-Attention Network for Session-based Recommendation](https://scholariq.org/papers/graph-contextualized-self-attention-network-for-session-based-recommendation/)
- [Are Graph Augmentations Necessary?](https://scholariq.org/papers/are-graph-augmentations-necessary/)
- [Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://scholariq.org/papers/self-supervised-multi-channel-hypergraph-convolutional-network-for-social/)
- [Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction](https://scholariq.org/papers/interpretable-convolutional-neural-networks-with-dual-local-and-global-attention/)
- [Time weight collaborative filtering](https://scholariq.org/papers/time-weight-collaborative-filtering/)
- [Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation](https://scholariq.org/papers/where-to-go-next-modeling-long-and-short-term-user-preferences-for-point-of/)
- [Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation](https://scholariq.org/papers/where-to-go-next-a-spatio-temporal-gated-network-for-next-poi-recommendation-2/)
- [Shilling attacks against recommender systems: a comprehensive survey](https://scholariq.org/papers/shilling-attacks-against-recommender-systems-a-comprehensive-survey/)
- [Feature-level Deeper Self-Attention Network for Sequential Recommendation](https://scholariq.org/papers/feature-level-deeper-self-attention-network-for-sequential-recommendation/)
- [Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation](https://scholariq.org/papers/where-to-go-next-a-spatio-temporal-gated-network-for-next-poi-recommendation/)
- [XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation](https://scholariq.org/papers/xsimgcl-towards-extremely-simple-graph-contrastive-learning-for-recommendation/)
- [Are we really making much progress?](https://scholariq.org/papers/are-we-really-making-much-progress/)
- [The influence of task and gender on search and evaluation behavior using Google](https://scholariq.org/papers/the-influence-of-task-and-gender-on-search-and-evaluation-behavior-using-google/)

## Topic primary papers

Showing 15 of 46.

- [Graph Contextualized Self-Attention Network for Session-based Recommendation](https://scholariq.org/papers/graph-contextualized-self-attention-network-for-session-based-recommendation/)
- [Are Graph Augmentations Necessary?](https://scholariq.org/papers/are-graph-augmentations-necessary/)
- [Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://scholariq.org/papers/self-supervised-multi-channel-hypergraph-convolutional-network-for-social/)
- [Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction](https://scholariq.org/papers/interpretable-convolutional-neural-networks-with-dual-local-and-global-attention/)
- [Time weight collaborative filtering](https://scholariq.org/papers/time-weight-collaborative-filtering/)
- [Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation](https://scholariq.org/papers/where-to-go-next-modeling-long-and-short-term-user-preferences-for-point-of/)
- [Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation](https://scholariq.org/papers/where-to-go-next-a-spatio-temporal-gated-network-for-next-poi-recommendation-2/)
- [Shilling attacks against recommender systems: a comprehensive survey](https://scholariq.org/papers/shilling-attacks-against-recommender-systems-a-comprehensive-survey/)
- [Feature-level Deeper Self-Attention Network for Sequential Recommendation](https://scholariq.org/papers/feature-level-deeper-self-attention-network-for-sequential-recommendation/)
- [Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation](https://scholariq.org/papers/where-to-go-next-a-spatio-temporal-gated-network-for-next-poi-recommendation/)
- [XSimGCL: Towards Extremely Simple Graph Contrastive Learning for Recommendation](https://scholariq.org/papers/xsimgcl-towards-extremely-simple-graph-contrastive-learning-for-recommendation/)
- [Socially-Aware Self-Supervised Tri-Training for Recommendation](https://scholariq.org/papers/socially-aware-self-supervised-tri-training-for-recommendation/)
- [Streaming Session-based Recommendation](https://scholariq.org/papers/streaming-session-based-recommendation/)
- [Trust based recommender system for the semantic web](https://scholariq.org/papers/trust-based-recommender-system-for-the-semantic-web/)
- [Personal recommendation using deep recurrent neural networks in NetEase](https://scholariq.org/papers/personal-recommendation-using-deep-recurrent-neural-networks-in-netease/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.