Adversarial auto-encoder for rating prediction with ratings and reviews

2021 ◽  
pp. 1-10
Author(s):  
Jin Yi ◽  
Jiajin Huang ◽  
Jin Qin
Keyword(s):  
Real World ◽  
Empirical Studies ◽  
Auxiliary Information ◽  
Rating Data ◽  
Adversarial Learning ◽  
Rich Information ◽  
Rating Prediction ◽  
Sparsity Problem ◽  
Real World Datasets

Recommender systems have been widely used in our life in recent years to facilitate our life. And it is very important and meaningful to improve recommendation performance. Generally, recommendation methods use users’ historical ratings on items to predict ratings on their unrated items to make recommendations. However, with the increase of the number of users and items, the degree of data sparsity increases, and the quality of recommendations decreases sharply. In order to solve the sparsity problem, other auxiliary information is combined to mine users’ preferences for higher recommendation quality. Similar to rating data, review data also contain rich information about users’ preferences on items. This paper proposes a novel recommendation model, which harnesses an adversarial learning among auto-encoders to improve recommendation quality by minimizing the gap of the rating and review relation between a user and an item. The empirical studies on real-world datasets show that the proposed method improves the recommendation performance.

scholarly journals Session centered Recommendation Utilizing Future Contexts in Social Media

2021 ◽  
Vol 29 (3) ◽  
pp. 91-104
Author(s):  
Sanjeev Dhawan ◽  
Kulvinder Singh ◽  
Adrian Rabaea ◽  
Amit Batra
Keyword(s):  
Data Augmentation ◽  
Empirical Studies ◽  
Sequential Data ◽  
Space Filling ◽  
The Future ◽  
Future Data ◽  
Real World Datasets ◽  
Sequential Information ◽  
Better Than

Abstract Session centered recommender systems has emerged as an interesting and challenging topic amid researchers during the past few years. In order to make a prediction in the sequential data, prevailing approaches utilize either left to right design autoregressive or data augmentation methods. As these approaches are used to utilize the sequential information pertaining to user conduct, the information about the future context of an objective interaction is totally ignored while making prediction. As a matter of fact, we claim that during the course of training, the future data after the objective interaction are present and this supplies indispensable signal on preferences of users and if utilized can increase the quality of recommendation. It is a subtle task to incorporate future contexts into the process of training, as the rules of machine learning are not followed and can result in loss of data. Therefore, in order to solve this problem, we suggest a novel encoder decoder prototype termed as space filling centered Recommender (SRec), which is used to train the encoder and decoder utilizing space filling approach. Particularly, an incomplete sequence is taken into consideration by the encoder as input (few items are absent) and then decoder is used to predict these items which are absent initially based on the encoded interpretation. The general SRec prototype is instantiated by us employing convolutional neural network (CNN) by giving emphasis on both e ciency and accuracy. The empirical studies and investigation on two real world datasets are conducted by us including short, medium and long sequences, which exhibits that SRec performs better than traditional sequential recommendation approaches.

Enhancing rating prediction for recommendation by metric learning with gray relational analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiangmei Chen ◽  
Wende Zhang ◽  
Qishan Zhang
Keyword(s):  
Metric Learning ◽  
Prediction Algorithm ◽  
Gray Relational Analysis ◽  
Rating Data ◽  
Data Sparsity ◽  
Content Type ◽  
Relational Analysis ◽  
Rating Prediction ◽  
Real World Datasets ◽  
The Impact

PurposeThe purpose of the paper is to improve the rating prediction accuracy in recommender systems (RSs) by metric learning (ML) method. The similarity metric of user and item is calculated with gray relational analysis.Design/methodology/approachFirst, the potential features of users and items are captured by exploiting ML, such that the rating prediction can be performed. In metric space, the user and item positions can be learned by training their embedding vectors. Second, instead of the traditional distance measurements, the gray relational analysis is employed in the evaluation of the position similarity between user and item, because the latter can reduce the impact of data sparsity and further explore the rating data correlation. On the basis of the above improvements, a new rating prediction algorithm is proposed. Experiments are implemented to validate the effectiveness of the algorithm.FindingsThe novel algorithm is evaluated by the extensive experiments on two real-world datasets. Experimental results demonstrate that the proposed model achieves remarkable performance on the rating prediction task.Practical implicationsThe rating prediction algorithm is adopted to predict the users' preference, and then, it provides personalized recommendations for users. In fact, this method can expand to the field of classification and provide potentials for this domain.Originality/valueThe algorithm can uncover the finer grained preference by ML. Furthermore, the similarity can be measured using gray relational analysis, which can mitigate the limitation of data sparsity.

scholarly journals Normalization based Multi-Criteria Collaborative Filtering Approach for Recommendation System

Rekayasa ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 234-239
Author(s):  
Noor Ifada ◽  
Nur Fitriani Dwi Putri ◽  
Mochammad Kautsar Sophan
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Method Development ◽  
Recommendation System ◽  
Rating Data ◽  
The Real ◽  
Rating Prediction ◽  
Filtering Approach ◽  
Better Than

A multi-criteria collaborative filtering recommendation system allows its users to rate items based on several criteria. Users instinctively have different tendencies in rating items that some of them are quite generous while others tend to be pretty stingy.  Given the diverse rating patterns, implementing a normalization technique in the system is beneficial to reveal the latent relationship within the multi-criteria rating data. This paper analyses and compares the performances of two methods that implement the normalization based multi-criteria collaborative filtering approach. The framework of the method development consists of three main processes, i.e.: multi-criteria rating representation, multi-criteria rating normalization, and rating prediction using a multi-criteria collaborative filtering approach. The developed methods are labelled based on the implemented normalization technique and multi-criteria collaborative filtering approaches, i.e., Decoupling normalization and Multi-Criteria User-based approach (DMCUser) and Decoupling normalization and Multi-Criteria User-based approach (DMCItem). Experiment results using the real-world Yelp Dataset show that DMCItem outperforms DMCUser at most  in terms of Precision and Normalized Discounted Cumulative Gain (NDCG). Though DMCUser can perform better than DMCItem at large , it is still more practical to implement DMCItem rather than DMCUser in a multi-criteria recommendation system since users tend to show more interest to items at the top list.

HC-COVID

2022 ◽  
Vol 6 (GROUP) ◽  
pp. 1-25
Author(s):  
Ziyi Kou ◽  
Lanyu Shang ◽  
Yang Zhang ◽  
Dong Wang
Keyword(s):  
Social Media ◽  
Real World ◽  
State Of The Art ◽  
Knowledge Graph ◽  
Detection Accuracy ◽  
Critical Problem ◽  
Real World Datasets ◽  
Collaborative Efforts ◽  
Different Levels

The proliferation of social media has promoted the spread of misinformation that raises many concerns in our society. This paper focuses on a critical problem of explainable COVID-19 misinformation detection that aims to accurately identify and explain misleading COVID-19 claims on social media. Motivated by the lack of COVID-19 relevant knowledge in existing solutions, we construct a novel crowdsource knowledge graph based approach to incorporate the COVID-19 knowledge facts by leveraging the collaborative efforts of expert and non-expert crowd workers. Two important challenges exist in developing our solution: i) how to effectively coordinate the crowd efforts from both expert and non-expert workers to generate the relevant knowledge facts for detecting COVID-19 misinformation; ii) How to leverage the knowledge facts from the constructed knowledge graph to accurately explain the detected COVID-19 misinformation. To address the above challenges, we develop HC-COVID, a hierarchical crowdsource knowledge graph based framework that explicitly models the COVID-19 knowledge facts contributed by crowd workers with different levels of expertise and accurately identifies the related knowledge facts to explain the detection results. We evaluate HC-COVID using two public real-world datasets on social media. Evaluation results demonstrate that HC-COVID significantly outperforms state-of-the-art baselines in terms of the detection accuracy of misleading COVID-19 claims and the quality of the explanations.

scholarly journals Dense Sub-networks Discovery in Temporal Networks

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Riccardo Dondi ◽  
Mohammad Mehdi Hosseinzadeh
Keyword(s):  
Local Search ◽  
Real World ◽  
Experimental Results ◽  
Temporal Network ◽  
Temporal Networks ◽  
Dense Subgraph ◽  
Search Technique ◽  
Dense Subgraphs ◽  
Real World Datasets

AbstractTemporal networks have been successfully applied to analyse dynamics of networks. In this paper we focus on an approach recently introduced to identify dense subgraphs in a temporal network and we present a heuristic, based on the local search technique, for the problem. The experimental results we present on synthetic and real-world datasets show that our heuristic provides mostly better solutions (denser solutions) and that the heuristic is fast (comparable with the fastest method in literature, which is outperformed in terms of quality of the solutions). We present also experimental results of two variants of our method based on two different subroutines to compute a dense subgraph of a given graph.

scholarly journals Robust Adversarial Imitation Learning via Adaptively-Selected Demonstrations

2021 ◽  
Author(s):  
Yunke Wang ◽  
Chang Xu ◽  
Bo Du
Keyword(s):  
Real World ◽  
Auxiliary Information ◽  
Weighting Scheme ◽  
Imitation Learning ◽  
Robust Method ◽  
Reward Function ◽  
Set Up ◽  
Binary Weight

The agent in imitation learning (IL) is expected to mimic the behavior of the expert. Its performance relies highly on the quality of given expert demonstrations. However, the assumption that collected demonstrations are optimal cannot always hold in real-world tasks, which would seriously influence the performance of the learned agent. In this paper, we propose a robust method within the framework of Generative Adversarial Imitation Learning (GAIL) to address imperfect demonstration issue, in which good demonstrations can be adaptively selected for training while bad demonstrations are abandoned. Specifically, a binary weight is assigned to each expert demonstration to indicate whether to select it for training. The reward function in GAIL is employed to determine this weight (i.e. higher reward results in higher weight). Compared to some existing solutions that require some auxiliary information about this weight, we set up the connection between weight and model so that we can jointly optimize GAIL and learn the latent weight. Besides hard binary weighting, we also propose a soft weighting scheme. Experiments in the Mujoco demonstrate the proposed method outperforms other GAIL-based methods when dealing with imperfect demonstrations.

scholarly journals Auxiliary Information-Enhanced Recommendations

2021 ◽  
Vol 11 (19) ◽  
pp. 8830
Author(s):  
Shoujin Wang ◽  
Wanggen Wan ◽  
Tong Qu ◽  
Yanqiu Dong
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Auxiliary Information ◽  
Recommendation Algorithm ◽  
Textual Information ◽  
Image Information ◽  
Sequential Dependencies ◽  
Recommendation Algorithms ◽  
The Rich ◽  
Real World Datasets

Sequential recommendations have attracted increasing attention from both academia and industry in recent years. They predict a given user’s next choice of items by mainly modeling the sequential relations over a sequence of the user’s interactions with the items. However, most of the existing sequential recommendation algorithms mainly focus on the sequential dependencies between item IDs within sequences, while ignoring the rich and complex relations embedded in the auxiliary information, such as items’ image information and textual information. Such complex relations can help us better understand users’ preferences towards items, and thus benefit from the recommendations. To bridge this gap, we propose an auxiliary information-enhanced sequential recommendation algorithm called memory fusion network for recommendation (MFN4Rec) to incorporate both items’ image and textual information for sequential recommendations. Accordingly, item IDs, item image information and item textual information are regarded as three modalities. By comprehensively modelling the sequential relations within modalities and interaction relations across modalities, MFN4Rec can learn a more informative representation of users’ preferences for more accurate recommendations. Extensive experiments on two real-world datasets demonstrate the superiority of MFN4Rec over state-of-the-art sequential recommendation algorithms.

Collaboration Matrix Factorization on Rate and Review for Recommendation

2019 ◽  
Vol 30 (2) ◽  
pp. 27-43
Author(s):  
Zhicheng Wu ◽  
Huafeng Liu ◽  
Yanyan Xu ◽  
Liping Jing
Keyword(s):  
Neural Networks ◽  
Recommender Systems ◽  
Projection Method ◽  
Matrix Factorization ◽  
Auxiliary Information ◽  
Latent Factors ◽  
Latent Factor ◽  
Real World Datasets ◽  
Rating Information

According to the sparseness of rating information, the quality of recommender systems has been greatly restricted. In order to solve this problem, much auxiliary information has been used, such as social networks, review information, and item description. Convolutional neural networks (CNNs) have been widely employed by recommender systems, it greatly improved the rating prediction's accuracy especially when combined with traditional recommendation methods. However, a large amount of research focuses on the consistency between the rating-based latent factor and review-based latent factor. But in fact, these two parts are completely different. In this article, the authors propose a model named collaboration matrix factorization (CMF) that combines a projection method with a convolutional matrix factorization (ConvMF) to extract the collaboration between rating-based latent factors and review-based latent factors that comes from the results of the CNN process. Extensive experiments on three real-world datasets show that the projection method achieves significant improvements over the existing baseline.

scholarly journals Quality Matters: Assessing cQA Pair Quality via Transductive Multi-View Learning

2018 ◽  
Author(s):  
Xiaochi Wei ◽  
Heyan Huang ◽  
Liqiang Nie ◽  
Fuli Feng ◽  
Richang Hong ◽  
...  
Keyword(s):  
Regression Models ◽  
Question Answering ◽  
Community Based ◽  
Transductive Learning ◽  
Common Space ◽  
Proposed Model ◽  
Rich Information ◽  
The Common ◽  
Real World Datasets

Community-based question answering (cQA) sites have become important knowledge sharing platforms, as massive cQA pairs are archived, but the uneven quality of cQA pairs leaves information seekers unsatisfied. Various efforts have been dedicated to predicting the quality of cQA contents. Most of them concatenate different features into single vectors and then feed them into regression models. In fact, the quality of cQA pairs is influenced by different views, and the agreement among them is essential for quality assessment. Besides, the lacking of labeled data significantly hinders the quality prediction performance. Toward this end, we present a transductive multi-view learning model. It is designed to find a latent common space by unifying and preserving information from various views, including question, answer, QA relevance, asker, and answerer. Additionally, rich information in the unlabeled test cQA pairs are utilized via transductive learning to enhance the representation ability of the common space. Extensive experiments on real-world datasets have well-validated the proposed model.

scholarly journals Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach

2017 ◽  
Author(s):  
Lahari Poddar ◽  
Wynne Hsu ◽  
Mong Li Lee
Keyword(s):  
Bayesian Inference ◽  
Bayesian Approach ◽  
Real World ◽  
Predictive Ability ◽  
Auxiliary Variable ◽  
Quality Estimation ◽  
Intrinsic Quality ◽  
Real World Datasets ◽  
Fully Bayesian

User opinions expressed in the form of ratings can influence an individual's view of an item. However, the true quality of an item is often obfuscated by user biases, and it is not obvious from the observed ratings the importance different users place on different aspects of an item. We propose a probabilistic modeling of the observed aspect ratings to infer (i) each user's aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect ratings as ordered discrete data and encode the dependency between different aspects by using a latent Gaussian structure. We handle the Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully Bayesian inference. On two real world datasets, we demonstrate the predictive ability of our model and its effectiveness in learning explainable user biases to provide insights towards a more reliable product quality estimation.

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