User Reviews Based Rating Prediction in Recommender System

2021 ◽  
pp. 771-782
Author(s):  
Wenchuan Shi ◽  
Liejun Wang ◽  
Shuli Cheng ◽  
Yongming Li
Keyword(s):  
Recommender System ◽  
User Reviews ◽  
Rating Prediction

Review-Based Recommender Systems: A Proposed Rating Prediction Scheme Using Word Embedding Representation of Reviews

2020 ◽  
Author(s):  
S Hasanzadeh ◽  
S M Fakhrahmad ◽  
M Taheri
Keyword(s):  
Vector Space ◽  
Recommender Systems ◽  
Recommender System ◽  
Word Embedding ◽  
Bag Of Words ◽  
Prediction System ◽  
User Reviews ◽  
Prediction Scheme ◽  
Rating Prediction ◽  
The Given

Abstract Recommender systems nowadays play an important role in providing helpful information for users, especially in ecommerce applications. Many of the proposed models use rating histories of the users in order to predict unknown ratings. Recently, users’ reviews as a valuable source of knowledge have attracted the attention of researchers in this field and a new category denoted as review-based recommender systems has emerged. In this study, we make use of the information included in user reviews as well as available rating scores to develop a review-based rating prediction system. The proposed scheme attempts to handle the uncertainty problem of the rating histories, by fuzzifying the given ratings. Another advantage of the proposed system is the use of a word embedding representation model for textual reviews, instead of using traditional models such as binary bag of words and TFIDF 1 vector space. It also makes use of the helpfulness voting scores, in order to prune data and achieve better results. The effectiveness of the rating prediction scheme as well as the final recommender system was evaluated against the Amazon dataset. Experimental results revealed that the proposed recommender system outperforms its counterparts and can be used as a suitable tool in ecommerce environments.

scholarly journals Explainable Recommendation via Interpretable Feature Mapping and Evaluation of Explainability

2020 ◽  
Author(s):  
Deng Pan ◽  
Xiangrui Li ◽  
Xin Li ◽  
Dongxiao Zhu
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Ground Truth ◽  
Research Community ◽  
Feature Mapping ◽  
Trade Off ◽  
Rating Prediction ◽  
Strong Performance ◽  
And Performance ◽  
Explainable Recommendation

Latent factor collaborative filtering (CF) has been a widely used technique for recommender system by learning the semantic representations of users and items. Recently, explainable recommendation has attracted much attention from research community. However, trade-off exists between explainability and performance of the recommendation where metadata is often needed to alleviate the dilemma. We present a novel feature mapping approach that maps the uninterpretable general features onto the interpretable aspect features, achieving both satisfactory accuracy and explainability in the recommendations by simultaneous minimization of rating prediction loss and interpretation loss. To evaluate the explainability, we propose two new evaluation metrics specifically designed for aspect-level explanation using surrogate ground truth. Experimental results demonstrate a strong performance in both recommendation and explaining explanation, eliminating the need for metadata. Code is available from https://github.com/pd90506/AMCF.

Influence of Rating Prediction on the Accuracy of a Group Recommender System that Detects Groups

2017 ◽  
pp. 1-1 ◽  
Author(s):  
Ludovico Boratto ◽  
Salvatore Carta ◽  
Gianni Fenu ◽  
Fabrizio Mulas ◽  
Paolo Pilloni
Keyword(s):  
Recommender System ◽  
Rating Prediction ◽  
Group Recommender

scholarly journals Recommender System for Engineering College

2020 ◽  
Vol 9 (7S) ◽  
pp. 55-59
Keyword(s):  
Recommender System ◽  
User Reviews ◽  
Educational History

In this paper, we present research on developing a recommender system that helps students who want to take admission in engineering colleges. There are various engineering colleges in Gujarat. After completion of 12th Science, if students want to seek their admission in engineering, there are so many choices. Students generally face problems in choosing the better college as per their merit number. Colleges may have facilities such as campus facilities, university grants, the infrastructure of institutes, hostel facility, NBA and NAAC grading, placement record, tie-up with industries, faculties or educational history too. Students and parents do not have exact information about these all. Moreover, there is no such website or an application which gives the suggestion or recommend institutions where students can get admission. After studying these issues facing by parents and students, we are going to develop a recommender system for engineering institutes which can recommend to students as per their merit number and user reviews

A Hybrid Recommender System for Rating Prediction of Arabic Reviews

2020 ◽  
pp. 2050010
Author(s):  
Fouzi Harrag ◽  
Abdulmalik Salman Al-Salman ◽  
Alaa Alquahtani
Keyword(s):  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
Opinion Mining ◽  
Good Accuracy ◽  
Hybrid Approach ◽  
Data Sparsity ◽  
Hybrid Recommender System ◽  
Rating Prediction ◽  
Hybrid Recommender

Recommender systems nowadays are playing an important role in the delivery of services and information to users. Sentiment analysis (also known as opinion mining) is the process of determining the attitude of textual opinions, whether they are positive, negative or neutral. Data sparsity is representing a big issue for recommender systems because of the insufficiency of user rating or absence of data about users or items. This research proposed a hybrid approach combining sentiment analysis and recommender systems to tackle the problem of data sparsity problems by predicting the rating of products from users’ reviews using text mining and NLP techniques. This research focuses especially on Arabic reviews, where the model is evaluated using Opinion Corpus for Arabic (OCA) dataset. Our system was efficient, and it showed a good accuracy of nearly 85% in predicting the rating from reviews.

scholarly journals Designing and Implementation of Rating Prediction

2019 ◽  
Vol 8 (11) ◽  
pp. 2847-2852
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Rating System ◽  
The Real ◽  
Fine Grained ◽  
Elo Rating System ◽  
Rating Prediction ◽  
Farthest Point

Recommender system is one of indivisible parts in web business areas. Recommender system construes the system which underwrites things for the client who wish to purchase things .One of the real inconveniences that, figuratively speaking, stays in recommender system is the virus begin problem(inactive things) which can be seen as an obstruction that spurns the cool begin things from the present things. In this paper, we want to move beyond this farthest point for cold-begin clients/things by the help of existing things. It is developed by utilizing Elo Rating system. The Elo system is widely gotten in chess competitions; we propose a novel rating association technique to get settled with the profiles of cold-begin things. The purpose of assembly of our Strategy is to give a fine-grained View on the shrouded profiles of cold-begin clients/things by inspecting the separations between nippy begin things and existing Products. To uncover the limit of methodology, we instantiate our technique on two typical strategies in recommender systems, i.e., the structure factorization based, and neighborhood based pleasing sifting. Starter assessments on five genuine instructive documents embrace the amazing quality of our methodology over the present procedures in virus begin situation

scholarly journals Measuring user’s influence in the Yelp recommender system

2017 ◽  
Vol 1 (2) ◽  
pp. 91-104 ◽  
Author(s):  
Andres Bejarano ◽  
Agrima Jindal ◽  
Bharat Bhargava
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Design Methodology ◽  
User Reviews ◽  
Content Type ◽  
Attribute Weights ◽  
User Influence ◽  
Proposed Model ◽  
The Social

Purpose Recommender systems collect information about users and businesses and how they are related. Such relation is given in terms of reviews and votes on reviews. User reviews gather opinions, rating scores and review influence. The latter component is crucial for determining which users are more relevant in a recommender system, that is, the users whose reviews are more popular than the average user’s reviews. Design/methodology/approach A model of measure of user influence is proposed based on review and social attributes of the user. User influence is also used for determining how influenced has been a business being based on popular reviews. Findings Results indicate there is a connection between social attributes and user influence. Such results are relevant for marketing, credibility estimation and Sybil detections, among others. Originality/value The proposed model allows search parameterization based on the social attribute weights of users, reviews and businesses. Such weights defines the relevance on each attribute, which can be adjusted according to the search needs. Popularity results are then a function of weight preferences on user, reviews and businesses data join.

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