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"Recommendation": models, code, and papers

Fully Hyperbolic Graph Convolution Network for Recommendation

Aug 10, 2021
Liping Wang, Fenyu Hu, Shu Wu, Liang Wang

Recently, Graph Convolution Network (GCN) based methods have achieved outstanding performance for recommendation. These methods embed users and items in Euclidean space, and perform graph convolution on user-item interaction graphs. However, real-world datasets usually exhibit tree-like hierarchical structures, which make Euclidean space less effective in capturing user-item relationship. In contrast, hyperbolic space, as a continuous analogue of a tree-graph, provides a promising alternative. In this paper, we propose a fully hyperbolic GCN model for recommendation, where all operations are performed in hyperbolic space. Utilizing the advantage of hyperbolic space, our method is able to embed users/items with less distortion and capture user-item interaction relationship more accurately. Extensive experiments on public benchmark datasets show that our method outperforms both Euclidean and hyperbolic counterparts and requires far lower embedding dimensionality to achieve comparable performance.

* Accepted by CIKM 2021 short paper track 

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A Hybrid Recommender System for Patient-Doctor Matchmaking in Primary Care

Aug 09, 2018
Qiwei Han, Mengxin Ji, Inigo Martinez de Rituerto de Troya, Manas Gaur, Leid Zejnilovic

We partner with a leading European healthcare provider and design a mechanism to match patients with family doctors in primary care. We define the matchmaking process for several distinct use cases given different levels of available information about patients. Then, we adopt a hybrid recommender system to present each patient a list of family doctor recommendations. In particular, we model patient trust of family doctors using a large-scale dataset of consultation histories, while accounting for the temporal dynamics of their relationships. Our proposed approach shows higher predictive accuracy than both a heuristic baseline and a collaborative filtering approach, and the proposed trust measure further improves model performance.

* This paper is accepted at DSAA 2018 as a full paper, Proc. of the 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy 

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Alternating Linear Bandits for Online Matrix-Factorization Recommendation

Oct 22, 2018
Hamid Dadkhahi, Sahand Negahban

We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. At each time step, the user (selected by the environment) consumes an item (selected by the agent) and provides a rating of the selected item. In this paper, we propose a novel algorithm for online matrix factorization recommendation that combines linear bandits and alternating least squares. In this formulation, the bandit feedback is equal to the difference between the ratings of the best and selected items. We evaluate the performance of the proposed algorithm over time using both cumulative regret and average cumulative NDCG. Simulation results over three synthetic datasets as well as three real-world datasets for online collaborative filtering indicate the superior performance of the proposed algorithm over two state-of-the-art online algorithms.


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A Recommender System based on Idiotypic Artificial Immune Networks

May 16, 2008
Steve Cayzer, Uwe Aickelin

The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an Artificial Immune System (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by Collaborative Filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen-antibody interaction for matching and idiotypic antibody-antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.

* Journal of Mathematical Modelling and Algorithms, 4(2), pp 181-198, 2005 

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Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks

Sep 14, 2019
Chuan Shi, Xiaotian Han, Li Song, Xiao Wang, Senzhang Wang, Junping Du, Philip S. Yu

Recently, recommender systems play a pivotal role in alleviating the problem of information overload. Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the interaction information between users and items, although some recently extended models utilize some auxiliary information to learn a unified latent factor for users and items. The unified latent factor only represents the characteristics of users and the properties of items from the aspect of purchase history. However, the characteristics of users and the properties of items may stem from different aspects, e.g., the brand-aspect and category-aspect of items. Moreover, the latent factor models usually use the shallow projection, which cannot capture the characteristics of users and items well. In this paper, we propose a Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors. Through modelling the rich object properties and relations in recommender system as a heterogeneous information network, NeuACF first extracts different aspect-level similarity matrices of users and items respectively through different meta-paths, and then feeds an elaborately designed deep neural network with these matrices to learn aspect-level latent factors. Finally, the aspect-level latent factors are fused for the top-N recommendation. Moreover, to fuse information from different aspects more effectively, we further propose NeuACF++ to fuse aspect-level latent factors with self-attention mechanism. Extensive experiments on three real world datasets show that NeuACF and NeuACF++ significantly outperform both existing latent factor models and recent neural network models.


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Joint Variational Autoencoders for Recommendation with Implicit Feedback

Aug 17, 2020
Bahare Askari, Jaroslaw Szlichta, Amirali Salehi-Abari

Variational Autoencoders (VAEs) have recently shown promising performance in collaborative filtering with implicit feedback. These existing recommendation models learn user representations to reconstruct or predict user preferences. We introduce joint variational autoencoders (JoVA), an ensemble of two VAEs, in which VAEs jointly learn both user and item representations and collectively reconstruct and predict user preferences. This design allows JoVA to capture user-user and item-item correlations simultaneously. By extending the objective function of JoVA with a hinge-based pairwise loss function (JoVA-Hinge), we further specialize it for top-k recommendation with implicit feedback. Our extensive experiments on several real-world datasets show that JoVA-Hinge outperforms a broad set of state-of-the-art collaborative filtering methods, under a variety of commonly-used metrics. Our empirical results also confirm the outperformance of JoVA-Hinge over existing methods for cold-start users with a limited number of training data.


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MLP4Rec: A Pure MLP Architecture for Sequential Recommendations

Apr 25, 2022
Muyang Li, Xiangyu Zhao, Chuan Lyu, Minghao Zhao, Runze Wu, Ruocheng Guo

Self-attention models have achieved state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, they rely on positional embeddings to retain the sequential information, which may break the semantics of item embeddings. In addition, most existing works assume that such sequential dependencies exist solely in the item embeddings, but neglect their existence among the item features. In this work, we propose a novel sequential recommender system (MLP4Rec) based on the recent advances of MLP-based architectures, which is naturally sensitive to the order of items in a sequence. To be specific, we develop a tri-directional fusion scheme to coherently capture sequential, cross-channel and cross-feature correlations. Extensive experiments demonstrate the effectiveness of MLP4Rec over various representative baselines upon two benchmark datasets. The simple architecture of MLP4Rec also leads to the linear computational complexity as well as much fewer model parameters than existing self-attention methods.


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A quantum-inspired classical algorithm for recommendation systems

Jul 10, 2018
Ewin Tang

A recommendation system suggests products to users based on data about user preferences. It is typically modeled by a problem of completing an $m\times n$ matrix of small rank $k$. We give the first classical algorithm to produce a recommendation in $O(\text{poly}(k)\text{polylog}(m,n))$ time, which is an exponential improvement on previous algorithms that run in time linear in $m$ and $n$. Our strategy is inspired by a quantum algorithm by Kerenidis and Prakash: like the quantum algorithm, instead of reconstructing a user's full list of preferences, we only seek a randomized sample from the user's preferences. Our main result is an algorithm that samples high-weight entries from a low-rank approximation of the input matrix in time independent of $m$ and $n$, given natural sampling assumptions on that input matrix. As a consequence, we show that Kerenidis and Prakash's quantum machine learning (QML) algorithm, one of the strongest candidates for provably exponential speedups in QML, does not in fact give an exponential speedup over classical algorithms.

* 35 pages 

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Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs

Nov 20, 2021
Gavin Edwards, Sebastian Nilsson, Benedek Rozemberczki, Eliseo Papa

For Artificial Intelligence to have a greater impact in biology and medicine, it is crucial that recommendations are both accurate and transparent. In other domains, a neurosymbolic approach of multi-hop reasoning on knowledge graphs has been shown to produce transparent explanations. However, there is a lack of research applying it to complex biomedical datasets and problems. In this paper, the approach is explored for drug discovery to draw solid conclusions on its applicability. For the first time, we systematically apply it to multiple biomedical datasets and recommendation tasks with fair benchmark comparisons. The approach is found to outperform the best baselines by 21.7% on average whilst producing novel, biologically relevant explanations.


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Affective Recommendation System for Tourists by Using Emotion Generating Calculations

Apr 09, 2018
Takumi Ichimura, Issei Tachibana

An emotion orientated intelligent interface consists of Emotion Generating Calculations (EGC) and Mental State Transition Network (MSTN). We have developed the Android EGC application software which the agent works to evaluate the feelings in the conversation. In this paper, we develop the tourist information system which can estimate the user's feelings at the sightseeing spot. The system can recommend the sightseeing spot and the local food corresponded to the user's feeling. The system calculates the recommendation list by the estimate function which consists of Google search results, the important degree of a term at the sightseeing website, and the the aroused emotion by EGC. In order to show the effectiveness, this paper describes the experimental results for some situations during Hiroshima sightseeing.

* Proc. of IEEE 7th International Workshop on Computational Intelligence and Applications (IWCIA2014) 
* 6 pages, 10 figures. arXiv admin note: substantial text overlap with arXiv:1804.02657 and arXiv:1804.03994 

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