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Neural Serendipity Recommendation
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-1-0716-2197-4openalex-snapshot
Recommender Systems Handbook
inferredcites →https://doi.org/10.1007/978-3-319-58524-6_26openalex-snapshot
A New Information Theory-Based Serendipitous Algorithm Design
inferredcites →https://doi.org/10.1016/0893-6080(90)90049-qopenalex-snapshot
Probabilistic neural networks
inferredcites →https://doi.org/10.1016/j.eswa.2016.02.013openalex-snapshot
Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities
inferredcites →https://doi.org/10.1016/j.knosys.2013.03.012openalex-snapshot
Recommender systems survey
inferredcites →https://doi.org/10.1016/j.knosys.2016.08.014openalex-snapshot
A survey of serendipity in recommender systems
inferredcites →https://doi.org/10.1016/j.knosys.2017.03.023openalex-snapshot
Balancing accuracy and diversity in recommendations using matrix completion framework
inferredcites →https://doi.org/10.1016/j.neunet.2017.03.009openalex-snapshot
Representation learning via Dual-Autoencoder for recommendation
inferredcites →https://doi.org/10.1016/j.neunet.2018.12.011openalex-snapshot
NeuO: Exploiting the sentimental bias between ratings and reviews with neural networks
inferredcites →https://doi.org/10.1109/icde.2016.7498253openalex-snapshot
“Told you i didn't like it”: Exploiting uninteresting items for effective collaborative filtering
inferredcites →https://doi.org/10.1109/icdm.2015.71openalex-snapshot
Exploiting Temporal and Social Factors for B2B Marketing Campaign Recommendations
inferredcites →https://doi.org/10.1109/icect.2005.102openalex-snapshot
Using Singular Value Decomposition Approximation for Collaborative Filtering
inferredcites →https://doi.org/10.1109/thms.2015.2446953openalex-snapshot
Personalized Travel Package With Multi-Point-of-Interest Recommendation Based on Crowdsourced User Footprints
inferredcites →https://doi.org/10.1109/tkde.2016.2566622openalex-snapshot
Improving Collaborative Recommendation via User-Item Subgroups
inferredcites →https://doi.org/10.1109/tkde.2016.2615039openalex-snapshot
RaPare: A Generic Strategy for Cold-Start Rating Prediction Problem
inferredcites →https://doi.org/10.1109/tkde.2017.2717984openalex-snapshot
An Approach for Building Efficient and Accurate Social Recommender Systems Using Individual Relationship Networks
inferredcites →https://doi.org/10.1109/tmm.2017.2779043openalex-snapshot
Improving Existing Collaborative Filtering Recommendations via Serendipity-Based Algorithm
inferredcites →https://doi.org/10.1109/tsmc.2018.2854000openalex-snapshot
Social Recommendation With Evolutionary Opinion Dynamics
inferredcites →https://doi.org/10.1145/1060745.1060754openalex-snapshot
Improving recommendation lists through topic diversification
inferredcites →https://doi.org/10.1145/2623330.2623705openalex-snapshot
Mobile app recommendations with security and privacy awareness
inferredcites →https://doi.org/10.1145/2766462.2767818openalex-snapshot
Multi-source Information Fusion for Personalized Restaurant Recommendation
inferredcites →https://doi.org/10.1145/2872427.2882976openalex-snapshot
When do Recommender Systems Work the Best?
inferredcites →https://doi.org/10.1145/2911451.2911489openalex-snapshot
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
inferredcites →https://doi.org/10.1145/2911451.2911502openalex-snapshot
Discrete Collaborative Filtering
inferredcites →https://doi.org/10.1145/2939672.2939773openalex-snapshot
Unified Point-of-Interest Recommendation with Temporal Interval Assessment
inferredcites →https://doi.org/10.1145/3038912.3052569openalex-snapshot
Neural Collaborative Filtering
inferredcites →https://doi.org/10.1145/3038912.3052585openalex-snapshot
Learning to Recommend Accurate and Diverse Items
inferredcites →https://doi.org/10.1145/3097983.3098122openalex-snapshot
A Location-Sentiment-Aware Recommender System for Both Home-Town and Out-of-Town Users
inferredcites →https://doi.org/10.1145/3132847.3132911openalex-snapshot
Learning and Transferring Social and Item Visibilities for Personalized Recommendation
inferredcites →https://doi.org/10.1145/3209978.3209981openalex-snapshot
Adversarial Personalized Ranking for Recommendation
inferredcites →https://doi.org/10.1145/371920.372071openalex-snapshot
Item-based collaborative filtering recommendation algorithms
inferredcites →https://doi.org/10.1145/582415.582418openalex-snapshot
Cumulated gain-based evaluation of IR techniques
inferredcites →https://doi.org/10.1155/2019/6325654openalex-snapshot
Topological Influence‐Aware Recommendation on Social Networks
inferredcites →https://doi.org/10.1561/9781601988157openalex-snapshot
Deep Learning: Methods and Applications
inferredcites →https://doi.org/10.3233/wia-140294openalex-snapshot
Music recommenders based on hybrid techniques and serendipity
inferredcites →https://doi.org/10.48550/arxiv.1207.4676openalex-snapshot
Proceedings of the 29th International Conference on Machine Learning (ICML-12)
inferredcites →https://doi.org/10.5220/0005879802510256openalex-snapshot
Challenges of Serendipity in Recommender Systems
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-23515-3_8openalex-snapshot
Boosting Item Coverage in Session-Based Recommendation
inferred← citeshttps://doi.org/10.1007/978-981-92-0363-5_28openalex-snapshot
LLM-Enhanced Reinforcement Learning for Long-Term User Satisfaction in Interactive Recommendation
inferred← citeshttps://doi.org/10.1007/978-981-96-8298-0_28openalex-snapshot
Large Language Models with Multi-faceted Relation Alignment for User Novel Interest Discovery
inferred← citeshttps://doi.org/10.1007/s11257-023-09385-8openalex-snapshot
What influences users to provide explicit feedback? A case of food delivery recommenders
inferred← citeshttps://doi.org/10.1007/s11518-026-5737-5openalex-snapshot
TIER: A Temporal Information-Enhanced Repeat-Aware Sequential Recommendation Model for On-Demand Television Content
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115660openalex-snapshot
Deep neural network approach for a serendipity-oriented recommendation system
inferred← citeshttps://doi.org/10.1016/j.ipm.2023.103636openalex-snapshot
An adaptive category-aware recommender based on dual knowledge graphs
inferred← citeshttps://doi.org/10.1016/j.knosys.2023.111359openalex-snapshot
A recommendation model for e-commerce platforms oriented to explicit information compensation and hidden information mining
inferred← citeshttps://doi.org/10.1080/10447318.2023.2238369openalex-snapshot
Interactive Feedback Loop with Counterfactual Data Modification for Serendipity in a Recommendation System
inferred← citeshttps://doi.org/10.1108/jd-01-2024-0011openalex-snapshot
“We do not always enjoy surprises”: investigating artificial serendipity in an online marketplace context
inferred← citeshttps://doi.org/10.1109/tkde.2021.3054782openalex-snapshot
A Unified Collaborative Representation Learning for Neural-Network Based Recommender Systems
inferred← citeshttps://doi.org/10.1109/tkde.2023.3243239openalex-snapshot
Zone-Enhanced Spatio-Temporal Representation Learning for Urban POI Recommendation
inferred← citeshttps://doi.org/10.1109/tkde.2023.3290140openalex-snapshot
GS$^{2}$-RS: A Generative Approach for Alleviating Cold start and Filter bubbles in Recommender Systems
inferred← citeshttps://doi.org/10.1145/3539618.3591787openalex-snapshot
Wisdom of Crowds and Fine-Grained Learning for Serendipity Recommendations
inferred← citeshttps://doi.org/10.1145/3555646openalex-snapshot
Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design
inferred← citeshttps://doi.org/10.1145/3605145openalex-snapshot
Deep Learning Models for Serendipity Recommendations: A Survey and New Perspectives
inferred← citeshttps://doi.org/10.1145/3627673.3679533openalex-snapshot
Sparks of Surprise: Multi-objective Recommendations with Hierarchical Decision Transformers for Diversity, Novelty, and Serendipity
inferred← citeshttps://doi.org/10.1145/3664190.3672521openalex-snapshot
The Art of Asking: Prompting Large Language Models for Serendipity Recommendations
inferred← citeshttps://doi.org/10.1145/3690654openalex-snapshot
A Deep Learning Model for Cross-Domain Serendipity Recommendations
inferred← citeshttps://doi.org/10.1145/3758092openalex-snapshot
Uncovering Recommendation Serendipity with Objective Data-driven Factor Investigation
inferred← citeshttps://doi.org/10.1155/2021/1604268openalex-snapshot
Human Origin‐Destination Flow Prediction Based on Large Scale Mobile Signal Data
inferred← citeshttps://doi.org/10.12720/jait.14.4.701-717openalex-snapshot
Towards Ideal and Efficient Recommendation Systems Based on the Five Evaluation Concepts Promoting Serendipity
inferred← citeshttps://doi.org/10.1613/jair.1.12403openalex-snapshot
Efficient Retrieval of Matrix Factorization-Based Top-k Recommendations: A Survey of Recent Approaches
inferred← citeshttps://doi.org/10.3390/app122211765openalex-snapshot
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
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