Journal ArticleDOI
Neural Serendipity Recommendation: Exploring the Balance between Accuracy and Novelty with Sparse Explicit Feedback
TLDR
This research presents a novel and scalable approach that combines reinforcement learning with reinforcement learning to provide real-time feedback on the quality of a recommender system's recommendations.Abstract:
Recommender systems have been playing an important role in providing personalized information to users. However, there is always a trade-off between accuracy and novelty in recommender systems. Usually, many users are suffering from redundant or inaccurate recommendation results. To this end, in this article, we put efforts into exploring the hidden knowledge of observed ratings to alleviate this recommendation dilemma. Specifically, we utilize some basic concepts to define a concept, Serendipity, which is characterized by high-satisfaction and low-initial-interest. Based on this concept, we propose a two-phase recommendation problem which aims to strike a balance between accuracy and novelty achieved by serendipity prediction and personalized recommendation. Along this line, a Neural Serendipity Recommendation (NSR) method is first developed by combining Muti-Layer Percetron and Matrix Factorization for serendipity prediction. Then, a weighted candidate filtering method is designed for personalized recommendation. Finally, extensive experiments on real-world data demonstrate that NSR can achieve a superior serendipity by a 12% improvement in average while maintaining stable accuracy compared with state-of-the-art methods.read more
Citations
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Journal ArticleDOI
Deep neural network approach for a serendipity-oriented recommendation system
TL;DR: In this paper, a convolutional neural network (CNN) is integrated with the Particle Swarm Optimization (PSO) algorithm to generate serendipitous recommendations, which is based on the focus shift points, consisting of unexpectedness and relevance parameters.
Journal ArticleDOI
DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
TL;DR: An objective function to measure the divergence of distributions between recommendation lists and historical behaviors and a decoupled-aggregated model which extracts information from two individual sequence encoders with different objectives to further improve the recommendation.
Journal ArticleDOI
Human Origin-Destination Flow Prediction Based on Large Scale Mobile Signal Data
TL;DR: Large-scale mobile phone signal data is used to achieve citywide human OD flow prediction between the coverage of varying signal base stations and a TGCN model combined with a graph fusion module is adopted to pretrain the dynamic population distribution prediction task.
Journal ArticleDOI
A Unified Collaborative Representation Learning for Neural-Network Based Recommender Systems
TL;DR: Zhang et al. as mentioned in this paper proposed Magnetic Metric Learning (MML) to map users and items into a unified latent vector space, enhancing the representation learning for NN-RSs.
Journal ArticleDOI
Practitioners Versus Users: A Value-Sensitive Evaluation of Current Industrial Recommender System Design
TL;DR: Value Sensitive Design is adopted to comprehensively explore how practitioners and users recognize different values of current industrial recommender systems, focusing on five values: recommendation quality, privacy, transparency, fairness, and trustworthiness.
References
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Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Proceedings ArticleDOI
Neural Collaborative Filtering
TL;DR: This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering.
Journal ArticleDOI
Cumulated gain-based evaluation of IR techniques
TL;DR: This article proposes several novel measures that compute the cumulative gain the user obtains by examining the retrieval result up to a given ranked position, and test results indicate that the proposed measures credit IR methods for their ability to retrieve highly relevant documents and allow testing of statistical significance of effectiveness differences.
Journal ArticleDOI
Probabilistic neural networks
TL;DR: A probabilistic neural network that can compute nonlinear decision boundaries which approach the Bayes optimal is formed, and a fourlayer neural network of the type proposed can map any input pattern to any number of classifications.
Journal ArticleDOI
Recommender systems survey
TL;DR: An overview of recommender systems as well as collaborative filtering methods and algorithms is provided, which explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.