A Joint Summarization and Pre-Trained Model for Review-Based Recommendation

Currently, reviews on the Internet contain abundant information about users and products, and this information is of great value to recommendation systems. As a result, review-based recommendations have begun to show their effectiveness and research value. Due to the accumulation of a large number of reviews, it has become very important to extract useful information from reviews. Automatic summarization can capture important information from a set of documents and present it in the form of a brief summary. Therefore, integrating automatic summarization into recommendation systems is a potential approach for solving this problem. Based on this idea, we propose a joint summarization and pre-trained recommendation model for review-based rate prediction. Through automatic summarization and a pre-trained language model, the overall recommendation model learns a fine-grained summary representation of the key content as well as the relationships between words and sentences in each review. The review summary representations of users and items are finally incorporated into a neural collaborative filtering (CF) framework with interactive attention mechanisms to predict the rating scores. We perform experiments on the Amazon dataset and compare our method with several competitive baselines. Experimental results show that the performance of the proposed model is obviously better than that of the baselines. Relative to the current best results, the average improvements obtained on four sub-datasets randomly selected from the Amazon dataset are approximately 3.29%.

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Personalized Suggestion For Music Based On Collaborative Filtering

International Journal Of Engineering And Computer Science ◽

10.18535/ijecs/v9i05.4487 ◽

2020 ◽

Vol 9 (05) ◽

pp. 25047-25051

Author(s):

Aniket Salunke ◽

Ruchika Kukreja ◽

Jayesh Kharche ◽

Amit Nerurkar

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Recommendation Systems ◽

Cold Start ◽

The Internet ◽

Music Recommendation ◽

User Similarity ◽

Content Based Filtering ◽

Music Recommendation System ◽

Cold Start Problem

With the advancement of technology there are millions of songs available on the internet and this creates problem for a person to choose from this vast pool of songs. So, there should be some middleman who must do this task on behalf of user and present most relevant songs that perfectly fits the user’s taste. This task is done by recommendation system. Music recommendation system predicts the user liking towards a particular song based on the listening history and profile. Most of the music recommendation system available today will give most recently played song or songs which have overall highest rating as suggestions to users but these suggestions are not personalized. The paper purposes how the recommendation systems can be used to give personalized suggestions to each and every user with the help of collaborative filtering which uses user similarity to give suggestions. The paper aims at implementing this idea and solving the cold start problem using content based filtering at the start.

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A Hybrid Ontology-Based Recommendation System in e-Commerce

Algorithms ◽

10.3390/a12110239 ◽

2019 ◽

Vol 12 (11) ◽

pp. 239 ◽

Cited By ~ 2

Author(s):

Márcio Guia ◽

Rodrigo Rocha Silva ◽

Jorge Bernardino

Keyword(s):

Collaborative Filtering ◽

Experimental Evaluation ◽

Recommendation System ◽

Knowledge Engineering ◽

Hybrid Approach ◽

Recommendation Systems ◽

The Internet ◽

Active User ◽

Expensive Process

The growth of the Internet has increased the amount of data and information available to any person at any time. Recommendation Systems help users find the items that meet their preferences, among the large number of items available. Techniques such as collaborative filtering and content-based recommenders have played an important role in the implementation of recommendation systems. In the last few years, other techniques, such as, ontology-based recommenders, have gained significance when reffering better active user recommendations; however, building an ontology-based recommender is an expensive process, which requires considerable skills in Knowledge Engineering. This paper presents a new hybrid approach that combines the simplicity of collaborative filtering with the efficiency of the ontology-based recommenders. The experimental evaluation demonstrates that the proposed approach presents higher quality recommendations when compared to collaborative filtering. The main improvement is verified on the results regarding the products, which, in spite of belonging to unknown categories to the users, still match their preferences and become recommended.

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RDMMFET: Representation of Dense Multimodality Fusion Encoder Based on Transformer

Mobile Information Systems ◽

10.1155/2021/2662064 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Xu Zhang ◽

DeZhi Han ◽

Chin-Chen Chang

Keyword(s):

Question Answering ◽

Language Model ◽

Visual Images ◽

Text And Image ◽

Data Set ◽

Fine Grained ◽

Natural Language Question ◽

The Relationship ◽

Language Question ◽

Better Than

Visual question answering (VQA) is the natural language question-answering of visual images. The model of VQA needs to make corresponding answers according to specific questions based on understanding images, the most important of which is to understand the relationship between images and language. Therefore, this paper proposes a new model, Representation of Dense Multimodality Fusion Encoder Based on Transformer, for short, RDMMFET, which can learn the related knowledge between vision and language. The RDMMFET model consists of three parts: dense language encoder, image encoder, and multimodality fusion encoder. In addition, we designed three types of pretraining tasks: masked language model, masked image model, and multimodality fusion task. These pretraining tasks can help to understand the fine-grained alignment between text and image regions. Simulation results on the VQA v2.0 data set show that the RDMMFET model can work better than the previous model. Finally, we conducted detailed ablation studies on the RDMMFET model and provided the results of attention visualization, which proves that the RDMMFET model can significantly improve the effect of VQA.

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Agricultural Product Recommendation Model based on BMF

Applied Mathematics and Nonlinear Sciences ◽

10.2478/amns.2020.2.00060 ◽

2020 ◽

Vol 5 (2) ◽

pp. 415-424

Author(s):

Fucheng Wan ◽

Dengyun Zhu ◽

Xiangzhen He ◽

Qi Guo ◽

Dongjiao Zhang ◽

...

Keyword(s):

Deep Learning ◽

Collaborative Filtering ◽

Language Model ◽

Agricultural Product ◽

Vector Construction ◽

Proposed Model ◽

Memory Network ◽

Scoring Matrix ◽

Short Time ◽

Matrix Factorisation

AbstractIn this article, based on the collaborative deep learning (CDL) and convolutional matrix factorisation (ConvMF), the language model BERT is used to replace the traditional word vector construction method, and the bidirectional long–short time memory network Bi-LSTM is used to construct an improved collaborative filtering model BMF, which not only solves the phenomenon of ‘polysemy’, but also alleviates the problem of sparse scoring matrix data. Experiments show that the proposed model is effective and superior to CDL and ConvMF. The trained MSE value is 1.031, which is 9.7% lower than ConvMF.

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Neural Collaborative with Sentence BERT for News Recommender System

JOIV International Journal on Informatics Visualization ◽

10.30630/joiv.5.4.678 ◽

2021 ◽

Vol 5 (4) ◽

pp. 448

Author(s):

Budi Juarto ◽

Abba Suganda Girsang

Keyword(s):

Neural Networks ◽

Collaborative Filtering ◽

Recommender System ◽

Recommendation System ◽

Recommendation Systems ◽

Roc Curves ◽

Leave One Out ◽

Evaluation Measurement ◽

Better Than ◽

Roc Score

The number of news produced every day is as much as 3 million per day, making readers have many choices in choosing news according to each reader's topic and category preferences. The recommendation system can make it easier for users to choose the news to read. The method that can be used in providing recommendations from the same user is collaborative filtering. Neural collaborative filtering is usually being used for recommendation systems by combining collaborative filtering with neural networks. However, this method has the disadvantage of recommending the similarity of news content such as news titles and content to users. This research wants to develop neural collaborative filtering using sentences BERT. Sentence BERT is applied to news titles and news contents that are converted into sentence embedding. The results of this sentence embedding are used in neural collaboration with item id, user id, and news category. We use a Microsoft news dataset of 50,000 users and 51,282 news, with 5,475,542 interactions between users and news. The evaluation carried out in this study uses precision, recall, and ROC curves to predict news clicks by the user. Another evaluation uses a hit ratio with the leave one out method. The evaluation results obtained a precision value of 99.14%, recall of 92.48%, f1-score of 95.69%, and ROC score of 98%. Evaluation measurement using the hit ratio@10 produces a hit ratio of 74% at fiftieth epochs for neural collaborative with sentence BERT which is better than neural collaborative filtering (NCF) and NCF with news category.

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A Recommendation System Framework Based on Web Mining

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.151.576 ◽

2012 ◽

Vol 151 ◽

pp. 576-582 ◽

Cited By ~ 1

Author(s):

Zhen Jian Yang ◽

Ke Wen Xia

Keyword(s):

Collaborative Filtering ◽

Web Mining ◽

Recommendation System ◽

Recommendation Systems ◽

Web Content ◽

Research Papers ◽

Rating Data ◽

Web Structure ◽

Better Than

Presently recommendation systems have gradually become an important part in E-Commerce, more and more research papers about recommendation systems in E-Commerce appeared in many kinds of conferences and journals. With expanding of E-Commerce it also faces series of challenges. Traditional collaborative filtering recommendation technique is hard to provide recommendation service for unregistered users. To overcome this problem, we suggested a framework of recommendation system based on web mining. It is made up of two parts, offline and online. This method first clustered web usage data, web content data and web structure data respectively, then provided high-quality recommendation services based on mining results. Compared with traditional collaborative filtering techniques, recommendation systems based on web mining are convenient for users because user need not to provide user-rating data explicitly. In end of this paper, accuracy of recommendation system based on web mining was tested and compared with traditional collaborative filtering recommendation system. Testing results showed that, quality of recommendation system based on web mining is better than quality of traditional collaborative filtering recommendation system.

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Content boosted hybrid filtering for solving pessimistic user problem in recommendation systems

Intelligent Data Analysis ◽

10.3233/ida-205244 ◽

2020 ◽

Vol 24 (6) ◽

pp. 1477-1496

Author(s):

Rajalakshmi Sivanaiah ◽

R. Sakaya Milton ◽

T.T. Mirnalinee

Keyword(s):

Collaborative Filtering ◽

Recommendation System ◽

Historical Data ◽

Predictive Accuracy ◽

Recommendation Systems ◽

Hybrid Filtering ◽

Data Content ◽

Filtering Technique ◽

Content Based Filtering ◽

Better Than

The main goal of a recommendation system is to recommend items of interest to users by analyzing their historical data. Content-based and collaborative filtering are the traditional recommendation strategies, each with its own strengths and weaknesses. Some of their weaknesses can be overcome by combining the two strategies. The resulting hybrid system performs qualitatively better than the traditional recommendation systems. However, historical data of some users may consist largely of only likes or only dislikes. Those users are termed as optimistic or pessimistic users respectively. On an average there are around 10 to 20% of pessimistic users present in a given dataset. For pessimistic users, whose profiles have mostly dislikes and very few likes, content-based filtering can hardly recommend any items of interest. In content-based filtering technique pessimistic users get poor recommendations of either uninteresting movies or no recommendations at all. This can be alleviated by boosting the content profiles of pessimistic users using the top-n recommendations of collaborative filtering. This content boosted hybrid filtering system provides a novel list of recommendations even for pessimistic users, with predictive accuracy better than that of a traditional content-based filtering system.

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Improving the Prediction Quality in Memory-Based Collaborative Filtering Using Categorical Features

Electronics ◽

10.3390/electronics10020214 ◽

2021 ◽

Vol 10 (2) ◽

pp. 214

Author(s):

Lei Chen ◽

Yuyu Yuan ◽

Jincui Yang ◽

Ahmed Zahir

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Prediction Accuracy ◽

Nearest Neighbor ◽

Side Information ◽

Weight Matrix ◽

K Nearest Neighbor ◽

Proposed Model ◽

Real World Datasets ◽

Better Than

Despite years of evolution of recommender systems, improving prediction accuracy remains one of the core problems among researchers and industry. It is common to use side information to bolster the accuracy of recommender systems. In this work, we focus on using item categories, specifically movie genres, to improve the prediction accuracy as well as coverage, precision, and recall. We derive the user’s taste for an item using the ratings expressed. Similarly, using the collective ratings given to an item, we identify how much each item belongs to a certain genre. These two vectors are then combined to get a user-item-weight matrix. In contrast to the similarity-based weight matrix in memory-based collaborative filtering, we use user-item-weight to make predictions. The user-item-weights can be used to explain to users why certain items have been recommended. We evaluate our proposed method using three real-world datasets. The proposed model performs significantly better than the baseline methods. In addition, we use the user-item-weight matrix to alleviate the sparsity problem associated with correlation-based similarity. In addition to that, the proposed model has a better computational complexity for making predictions than the k-nearest neighbor (kNN) method.

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Ischemic Stroke Prediction by Exploring Sleep Related Features

Applied Sciences ◽

10.3390/app11052083 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2083

Author(s):

Jia Xie ◽

Zhu Wang ◽

Zhiwen Yu ◽

Bin Guo ◽

Xingshe Zhou

Keyword(s):

Ischemic Stroke ◽

Life Quality ◽

Area Under The Curve ◽

Prediction Method ◽

Neural System ◽

Human Beings ◽

Filter Model ◽

Fine Grained ◽

Proposed Model ◽

Precision Recall Curve

Ischemic stroke is one of the typical chronic diseases caused by the degeneration of the neural system, which usually leads to great damages to human beings and reduces life quality significantly. Thereby, it is crucial to extract useful predictors from physiological signals, and further diagnose or predict ischemic stroke when there are no apparent symptoms. Specifically, in this study, we put forward a novel prediction method by exploring sleep related features. First, to characterize the pattern of ischemic stroke accurately, we extract a set of effective features from several aspects, including clinical features, fine-grained sleep structure-related features and electroencephalogram-related features. Second, a two-step prediction model is designed, which combines commonly used classifiers and a data filter model together to optimize the prediction result. We evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall, Precision Recall Curve and Area Under the Curve are 63%, 85%, 0.773 and 0.919, respectively.

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