scholarly journals SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback

2020 ◽  
Vol 34 (04) ◽  
pp. 6127-6136
Author(s):  
Chao Wang ◽  
Hengshu Zhu ◽  
Chen Zhu ◽  
Chuan Qin ◽  
Hui Xiong
Keyword(s):  
Bayesian Approach ◽  
Posterior Probability ◽  
State Of The Art ◽  
User Preferences ◽  
Implicit Feedback ◽  
Pairwise Preference ◽  
Entire List ◽  
Collaborative Ranking ◽  
Real World Datasets ◽  
Made In

The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the implicit feedback only generates positive and unobserved labels. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” due to the precondition of the entire list permutation. To this end, in this paper, we propose a novel setwise Bayesian approach for collaborative ranking, namely SetRank, to inherently accommodate the characteristics of implicit feedback in recommender system. Specifically, SetRank aims at maximizing the posterior probability of novel setwise preference comparisons and can be implemented with matrix factorization and neural networks. Meanwhile, we also present the theoretical analysis of SetRank to show that the bound of excess risk can be proportional to √M/N, where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.

scholarly journals MFNP: A Meta-optimized Model for Few-shot Next POI Recommendation

2021 ◽  
Author(s):  
Huimin Sun ◽  
Jiajie Xu ◽  
Kai Zheng ◽  
Pengpeng Zhao ◽  
Pingfu Chao ◽  
...  
Keyword(s):  
State Of The Art ◽  
Cold Start ◽  
User Preferences ◽  
Location Based Services ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Meta Learning ◽  
Optimized Model ◽  
Real World Datasets ◽  
Cold Start Problem

Next Point-of-Interest (POI) recommendation is of great value for location-based services. Existing solutions mainly rely on extensive observed data and are brittle to users with few interactions. Unfortunately, the problem of few-shot next POI recommendation has not been well studied yet. In this paper, we propose a novel meta-optimized model MFNP, which can rapidly adapt to users with few check-in records. Towards the cold-start problem, it seamlessly integrates carefully designed user-specific and region-specific tasks in meta-learning, such that region-aware user preferences can be captured via a rational fusion of region-independent personal preferences and region-dependent crowd preferences. In modelling region-dependent crowd preferences, a cluster-based adaptive network is adopted to capture shared preferences from similar users for knowledge transfer. Experimental results on two real-world datasets show that our model outperforms the state-of-the-art methods on next POI recommendation for cold-start users.

scholarly journals Neighborhood Attentional Memory Networks for Recommendation Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Tianlong Gu ◽  
Hongliang Chen ◽  
Chenzhong Bin ◽  
Liang Chang ◽  
Wei Chen
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
State Of The Art ◽  
Implicit Feedback ◽  
Learning Techniques ◽  
Ranking Score ◽  
Memory Modules ◽  
Output Module ◽  
Neighborhood Relations ◽  
Real World Datasets

Deep learning systems have been phenomenally successful in the fields of computer vision, speech recognition, and natural language processing. Recently, researchers have adopted deep learning techniques to tackle collaborative filtering with implicit feedback. However, the existing methods generally profile both users and items directly, while neglecting the similarities between users’ and items’ neighborhoods. To this end, we propose the neighborhood attentional memory networks (NAMN), a deep learning recommendation model applying two dedicated memory networks to capture users’ neighborhood relations and items’ neighborhood relations respectively. Specifically, we first design the user neighborhood component and the item neighborhood component based on memory networks and attention mechanisms. Then, by the associative addressing scheme with the user and item memories in the neighborhood components, we capture the complex user-item neighborhood relations. Stacking multiple memory modules together yields deeper architectures exploring higher-order complex user-item neighborhood relations. Finally, the output module jointly exploits the user and item neighborhood information with the user and item memories to obtain the ranking score. Extensive experiments on three real-world datasets demonstrate significant improvements of the proposed NAMN method over the state-of-the-art methods.

scholarly journals Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 214-221 ◽  
Author(s):  
Ke Sun ◽  
Tieyun Qian ◽  
Tong Chen ◽  
Yile Liang ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
State Of The Art ◽  
User Preferences ◽  
Short Term ◽  
Preference Modeling ◽  
Point Of Interest ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Novel Method ◽  
Real World Datasets

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods.

scholarly journals DMRAN:A Hierarchical Fine-Grained Attention-Based Network for Recommendation

2019 ◽  
Author(s):  
Huizhao Wang ◽  
Guanfeng Liu ◽  
An Liu ◽  
Zhixu Li ◽  
Kai Zheng
Keyword(s):  
Real World ◽  
State Of The Art ◽  
User Preferences ◽  
One Dimensional ◽  
Fine Grained ◽  
Item Level ◽  
Efficiency And Effectiveness ◽  
Time Encoding ◽  
Real World Datasets

The conventional methods for the next-item recommendation are generally based on RNN or one- dimensional attention with time encoding. They are either hard to preserve the long-term dependencies between different interactions, or hard to capture fine-grained user preferences. In this paper, we propose a Double Most Relevant Attention Network (DMRAN) that contains two layers, i.e., Item level Attention and Feature Level Self- attention, which are to pick out the most relevant items from the sequence of user’s historical behaviors, and extract the most relevant aspects of relevant items, respectively. Then, we can capture the fine-grained user preferences to better support the next-item recommendation. Extensive experiments on two real-world datasets illustrate that DMRAN can improve the efficiency and effectiveness of the recommendation compared with the state-of-the-art methods.

Contrastive Trajectory Learning for Tour Recommendation

2022 ◽  
Vol 13 (1) ◽  
pp. 1-25
Author(s):  
Fan Zhou ◽  
Pengyu Wang ◽  
Xovee Xu ◽  
Wenxin Tai ◽  
Goce Trajcevski
Keyword(s):  
Data Augmentation ◽  
State Of The Art ◽  
Implicit Feedback ◽  
Data Sparsity ◽  
Weak Supervision ◽  
Point Of Interest ◽  
Data Correlations ◽  
Recommendation Accuracy ◽  
Real World Datasets ◽  
Trajectory Learning

The main objective of Personalized Tour Recommendation (PTR) is to generate a sequence of point-of-interest (POIs) for a particular tourist, according to the user-specific constraints such as duration time, start and end points, the number of attractions planned to visit, and so on. Previous PTR solutions are based on either heuristics for solving the orienteering problem to maximize a global reward with a specified budget or approaches attempting to learn user visiting preferences and transition patterns with the stochastic process or recurrent neural networks. However, existing learning methodologies rely on historical trips to train the model and use the next visited POI as the supervised signal, which may not fully capture the coherence of preferences and thus recommend similar trips to different users, primarily due to the data sparsity problem and long-tailed distribution of POI popularity. This work presents a novel tour recommendation model by distilling knowledge and supervision signals from the trips in a self-supervised manner. We propose Contrastive Trajectory Learning for Tour Recommendation (CTLTR), which utilizes the intrinsic POI dependencies and traveling intent to discover extra knowledge and augments the sparse data via pre-training auxiliary self-supervised objectives. CTLTR provides a principled way to characterize the inherent data correlations while tackling the implicit feedback and weak supervision problems by learning robust representations applicable for tour planning. We introduce a hierarchical recurrent encoder-decoder to identify tourists’ intentions and use the contrastive loss to discover subsequence semantics and their sequential patterns through maximizing the mutual information. Additionally, we observe that a data augmentation step as the preliminary of contrastive learning can solve the overfitting issue resulting from data sparsity. We conduct extensive experiments on a range of real-world datasets and demonstrate that our model can significantly improve the recommendation performance over the state-of-the-art baselines in terms of both recommendation accuracy and visiting orders.

scholarly journals Bidirectional Inference Networks:A Class of Deep Bayesian Networks for Health Profiling

2019 ◽  
Vol 33 ◽  
pp. 766-773 ◽  
Author(s):  
Hao Wang ◽  
Chengzhi Mao ◽  
Hao He ◽  
Mingmin Zhao ◽  
Tommi S. Jaakkola ◽  
...  
Keyword(s):  
Bayesian Networks ◽  
Posterior Probability ◽  
State Of The Art ◽  
High Dimensional ◽  
Probabilistic Neural Networks ◽  
Single Model ◽  
Sleep Study ◽  
Training Stage ◽  
Real World Datasets ◽  
Conditional Dependency

We consider the problem of inferring the values of an arbitrary set of variables (e.g., risk of diseases) given other observed variables (e.g., symptoms and diagnosed diseases) and high-dimensional signals (e.g., MRI images or EEG). This is a common problem in healthcare since variables of interest often differ for different patients. Existing methods including Bayesian networks and structured prediction either do not incorporate high-dimensional signals or fail to model conditional dependencies among variables. To address these issues, we propose bidirectional inference networks (BIN), which stich together multiple probabilistic neural networks, each modeling a conditional dependency. Predictions are then made via iteratively updating variables using backpropagation (BP) to maximize corresponding posterior probability. Furthermore, we extend BIN to composite BIN (CBIN), which involves the iterative prediction process in the training stage and improves both accuracy and computational efficiency by adaptively smoothing the optimization landscape. Experiments on synthetic and real-world datasets (a sleep study and a dermatology dataset) show that CBIN is a single model that can achieve state-of-the-art performance and obtain better accuracy in most inference tasks than multiple models each specifically trained for a different task.

Dual-Embedding based Deep Latent Factor Models for Recommendation

2021 ◽  
Vol 15 (5) ◽  
pp. 1-24
Author(s):  
Weiyu Cheng ◽  
Yanyan Shen ◽  
Linpeng Huang ◽  
Yanmin Zhu
Keyword(s):  
Collaborative Filtering ◽  
State Of The Art ◽  
Factor Models ◽  
Superior Performance ◽  
Implicit Feedback ◽  
The Novel ◽  
Latent Factor ◽  
Latent Factor Models ◽  
Real World Datasets

Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to the recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this article proposes a dual-embedding based deep latent factor method for recommendation with implicit feedback. In addition to learning a primitive embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users) and propose attentive neural methods to discriminate the importance of interacted users/items for dual-embedding learning. We design two dual-embedding based deep latent factor models, DELF and DESEQ, for pure collaborative filtering and temporal collaborative filtering (i.e., sequential recommendation), respectively. The novel attempt of the proposed models is to capture each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on four real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of our methods on item recommendation.

scholarly journals VAEGAN: A Collaborative Filtering Framework based on Adversarial Variational Autoencoders

2019 ◽  
Author(s):  
Xianwen Yu ◽  
Xiaoning Zhang ◽  
Yang Cao ◽  
Min Xia
Keyword(s):  
Collaborative Filtering ◽  
State Of The Art ◽  
High Accuracy ◽  
Generative Adversarial Networks ◽  
Implicit Feedback ◽  
Inference Model ◽  
Penalty Term ◽  
Adversarial Networks ◽  
Adversarial Training ◽  
Real World Datasets

Recently, Variational Autoencoders (VAEs) have been successfully applied to collaborative filtering for implicit feedback. However, the performance of the resulting model depends a lot on the expressiveness of the inference model and the latent representation is often too constrained to be expressive enough to capture the true posterior distribution. In this paper, a novel framework named VAEGAN is proposed to address the above issue. In VAEGAN, we first introduce Adversarial Variational Bayes (AVB) to train Variational Autoencoders with arbitrarily expressive inference model. By utilizing Generative Adversarial Networks (GANs) for implicit variational inference, the inference model provides better approximation to the posterior and maximum-likelihood assignment. Then the performance of our model is further improved by introducing an auxiliary discriminative network using adversarial training to achieve high accuracy in recommendation. Furthermore, contractive loss is added to the classical reconstruction cost function as a penalty term to yield robust features and improve the generalization performance. Finally, we show that the performance of our proposed VAEGAN significantly outperforms state-of-the-art baselines on several real-world datasets.

scholarly journals Improving Implicit Recommender Systems with View Data

2018 ◽  
Author(s):  
Jingtao Ding ◽  
Guanghui Yu ◽  
Xiangnan He ◽  
Yuhan Quan ◽  
Yong Li ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Time Complexity ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Implicit Feedback ◽  
Model Parameters ◽  
New Learning ◽  
Real World Datasets ◽  
Feedback Data

Most existing recommender systems leverage the primary feedback data only, such as the purchase records in E-commerce. In this work, we additionally integrate view data into implicit feedback based recommender systems (dubbed as Implicit Recommender Systems). We propose to model the pairwise ranking relations among purchased, viewed, and non-viewed interactions, being more effective and flexible than typical pointwise matrix factorization (MF) methods. However, such a pairwise formulation poses efficiency challenges in learning the model. To address this problem, we design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) learner. Notably, our algorithm can efficiently learn model parameters from the whole user-item matrix (including all missing data), with a rather low time complexity that is dependent on the observed data only. Extensive experiments on two real-world datasets demonstrate that our method outperforms several state-of-the-art MF methods by 10% ∼ 28.4%. Our implementation is available at: https://github.com/ dingjingtao/View_enhanced_ALS.

Biological Production of (S)-acetoin: A State-of-the-Art Review

2019 ◽  
Vol 19 (25) ◽  
pp. 2348-2356 ◽  
Author(s):  
Neng-Zhong Xie ◽  
Jian-Xiu Li ◽  
Ri-Bo Huang
Keyword(s):  
Side Effects ◽  
Chemical Synthesis ◽  
State Of The Art ◽  
Optically Active ◽  
Considerable Progress ◽  
Future Prospects ◽  
Chiral Compound ◽  
Biological Production ◽  
Acetoin Production ◽  
Made In

Acetoin is an important four-carbon compound that has many applications in foods, chemical synthesis, cosmetics, cigarettes, soaps, and detergents. Its stereoisomer (S)-acetoin, a high-value chiral compound, can also be used to synthesize optically active drugs, which could enhance targeting properties and reduce side effects. Recently, considerable progress has been made in the development of biotechnological routes for (S)-acetoin production. In this review, various strategies for biological (S)- acetoin production are summarized, and their constraints and possible solutions are described. Furthermore, future prospects of biological production of (S)-acetoin are discussed.

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