Hybrid Collaborative Fusion Based Product Recommendation Exploiting Sentiments from Implicit and Explicit Reviews

2021 ◽  
pp. 2141013
Author(s):  
N Zafar Ali Khan ◽  
R. Mahalakshmi
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
User Profile ◽  
Personalized Recommendation ◽  
Product Reviews ◽  
Aspect Mining ◽  
Product Recommendation ◽  
Collaborative Recommendation ◽  
Demographic Attributes ◽  
Implicit And Explicit

Product recommendation is an important functionality in online ecommerce systems. The goal of the recommendation system is to recommend products with has higher purchase success ratio. User profile, product purchase history etc. have been used in many works to provide high quality recommendations. Product reviews is one of the important source for personalized recommendation. Typical collaborative recommendation systems are built upon user rating on products. But in many cases, these rating information are inaccurate or not available. There is also a problem of biased reviews decreasing the accuracy of recommendation systems. This work proposes a aspect mining collaborative fusion based recommendation system considering both the implicit and explicit reviews. The sentiments about different aspects mined from reviews are translated to multi-dimensional ratings. These ratings are then fused with user profile and demographic attributes to improve the quality of recommendation. The proposed recommendation system has 3.79% lower RMSE, 4.51% lower MAE and 22% lower MRE compared to most recent collaborative filtering based recommendation system.

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

scholarly journals Product recommendation system based user purchase criteria and product reviews

2019 ◽  
Vol 9 (6) ◽  
pp. 5454
Author(s):  
Jinyoung Kim ◽  
Doyeun Hwang ◽  
Hoekyung Jung
Keyword(s):  
Morphological Analysis ◽  
Recommendation System ◽  
Product Evaluation ◽  
Shopping Mall ◽  
Product Reviews ◽  
Purchasing Decisions ◽  
Product Review ◽  
Product Recommendation ◽  
Average Value ◽  
Purchase Criteria

<span lang="EN-US">In this paper, we propose a system that provides customized product recommendation information after crawling product review data of internet shopping mall with unstructured </span><span lang="EN-US">data</span><span lang="EN-US">, morphological analysis using Python. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product. User searches for a proudct to be purchased and select the most important purchase criteria when purchasing the product.</span><span lang="EN-US"> And extracts and analyzes only the review including the purchase criterion selected by the user among the product reviews left by other users. The positive and negative evaluations contained in the extracted product review data are quantified and using the average value, we extract the top 10 products with good product evaluation, sort and recommend to users. And provides user-customized information that reflects the user's preference by arranging and providing a center around the criteria that the user occupies the largest portion of the product purchase. This allows users to reduce the time it takes to purchase a product and make more efficient purchasing decisions.</span>

Personalized Content Recommendation Engine for Web Publishing Services Using Textmining and Predictive Analytics

2017 ◽  
pp. 113-124
Author(s):  
Başar Öztayşi ◽  
Ahmet Tezcan Tekin ◽  
Cansu Özdikicioğlu ◽  
Kerim Caner Tümkaya
Keyword(s):  
Recommendation System ◽  
Predictive Analytics ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Web Contents ◽  
Interest Level ◽  
History Of ◽  
Content Recommendation ◽  
Publishing Services ◽  
Web Publishing

Recommendation systems have become very important especially for internet based business such as e-commerce and web publishing. While content based filtering and collaborative filtering are most commonly used groups in recommendation systems there are still researches for new approaches. In this study, a personalized recommendation system based on text mining and predictive analytics is proposed for a real world web publishing company. The approach given in this chapter first preprocesses existing web contents, integrate the structured data with history of a specific user and create an extended TDM for the user. Then this data is used for prediction of the users interest in new content. In order to reach that point, SVM, K-NN and Naïve Bayesian methods are used. Finally, the best performing method is used for determining the interest level of the user in a new content. Based on the forecasted interest levels the system recommends among the alternatives.

Research on Intelligent Recommendation Method and its Application on Internet Bookstore

2010 ◽  
Vol 121-122 ◽  
pp. 447-452
Author(s):  
Qing Zhang Chen ◽  
Yu Jie Pei ◽  
Yan Jin ◽  
Li Yan Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
The Internet

As the current personalized recommendation systems of Internet bookstore are limited too much in function, this paper build a kind of Internet bookstore recommendation system based on “Strategic Data Mining”, which can provide personalized recommendations that they really want. It helps us to get the weight attribute of type of book by using AHP, the weight attributes spoken on behalf of its owner, and we add it in association rules. Then the method clusters the customer and type of book, and gives some strategies of personalized recommendation. Internet bookstore recommendation system is implemented with ASP.NET in this article. The experimental results indicate that the Internet bookstore recommendation system is feasible.

scholarly journals Internal and Contextual Attention Network for Cold-start Multi-channel Matching in Recommendation

2020 ◽  
Author(s):  
Ruobing Xie ◽  
Zhijie Qiu ◽  
Jun Rao ◽  
Yi Liu ◽  
Bo Zhang ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Recommendation System ◽  
Contextual Information ◽  
Recommendation Systems ◽  
Cold Start ◽  
Personalized Recommendation ◽  
Small Subset ◽  
Multiple Channels ◽  
Attention Network

Real-world integrated personalized recommendation systems usually deal with millions of heterogeneous items. It is extremely challenging to conduct full corpus retrieval with complicated models due to the tremendous computation costs. Hence, most large-scale recommendation systems consist of two modules: a multi-channel matching module to efficiently retrieve a small subset of candidates, and a ranking module for precise personalized recommendation. However, multi-channel matching usually suffers from cold-start problems when adding new channels or new data sources. To solve this issue, we propose a novel Internal and contextual attention network (ICAN), which highlights channel-specific contextual information and feature field interactions between multiple channels. In experiments, we conduct both offline and online evaluations with case studies on a real-world integrated recommendation system. The significant improvements confirm the effectiveness and robustness of ICAN, especially for cold-start channels. Currently, ICAN has been deployed on WeChat Top Stories used by millions of users. The source code can be obtained from https://github.com/zhijieqiu/ICAN.

Meta-Rule Based Recommender Systems for Educational Applications

2012 ◽  
pp. 211-231 ◽  
Author(s):  
Vicente Arturo Romero Zaldivar ◽  
Daniel Burgos ◽  
Abelardo Pardo
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Relevant Information ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Rule Based ◽  
User Data ◽  
Rule Sets ◽  
Rule Set ◽  
Educational Applications

Recommendation Systems are central in current applications to help the user find relevant information spread in large amounts of data. Most Recommendation Systems are more effective when huge amounts of user data are available. Educational applications are not popular enough to generate large amount of data. In this context, rule-based Recommendation Systems seem a better solution. Rules can offer specific recommendations with even no usage information. However, large rule-sets are hard to maintain, reengineer, and adapt to user preferences. Meta-rules can generalize a rule-set which provides bases for adaptation. In this chapter, the authors present the benefits of meta-rules, implemented as part of Meta-Mender, a meta-rule based Recommendation System. This is an effective solution to provide a personalized recommendation to the learner, and constitutes a new approach to Recommendation Systems.

Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review

2017 ◽  
Vol 44 (3) ◽  
pp. 331-344 ◽  
Author(s):  
Youdong Yun ◽  
Danial Hooshyar ◽  
Jaechoon Jo ◽  
Heuiseok Lim
Keyword(s):  
Collaborative Filtering ◽  
Quantitative Data ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Opinion Mining ◽  
Recommendation Systems ◽  
Product Recommendation ◽  
Collaborative Filtering Algorithm ◽  
Product Rating ◽  
Positive Recommendation

The most commonly used algorithm in recommendation systems is collaborative filtering. However, despite its wide use, the prediction accuracy of this algorithm is unexceptional. Furthermore, whether quantitative data such as product rating or purchase history reflect users’ actual taste is questionable. In this article, we propose a method to utilise user review data extracted with opinion mining for product recommendation systems. To evaluate the proposed method, we perform product recommendation test on Amazon product data, with and without the additional opinion mining result on Amazon purchase review data. The performances of these two variants are compared by means of precision, recall, true positive recommendation (TPR) and false positive recommendation (FPR). In this comparison, a large improvement in prediction accuracy was observed when the opinion mining data were taken into account. Based on these results, we answer two main questions: ‘Why is collaborative filtering algorithm not effective?’ and ‘Do quantitative data such as product rating or purchase history reflect users’ actual tastes?’

An Enhanced Similarity Measure Used in Personalized Recommendation Algorithms

2010 ◽  
Vol 159 ◽  
pp. 671-675 ◽  
Author(s):  
Song Jie Gong
Keyword(s):  
Collaborative Filtering ◽  
Similarity Measure ◽  
Recommendation System ◽  
Pearson Correlation ◽  
Recommendation Systems ◽  
Personalized Recommendation ◽  
Rating Data ◽  
Measure Methods ◽  
Collaborative Filtering Algorithm

Personalized recommendation systems combine the data mining technology with users browse profile and provide recommendation set to user forecasted by their interests. Collaborative filtering algorithm is one of the most successful methods for building personalized recommendation system, and is extensively used in many fields to date. With the development of E-commerce, the magnitudes of users and items grow rapidly, resulting in the extreme sparsity of user rating data. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. To alleviate the problem, an enhanced Pearson correlation similarity measure method is introduced in the personalized collaborative filtering recommendation algorithm. The approach considers the common correlation rating of users. The recommendation using the enhanced similarity measure can improve the neighbors influence in the course of recommendation and enhance the accuracy and the quality of recommendation systems effectively.

scholarly journals An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaofeng Li ◽  
Dong Li
Keyword(s):  
Collaborative Filtering ◽  
Community Detection ◽  
Recommendation System ◽  
Detection Algorithm ◽  
Personalized Recommendation ◽  
Filtering Algorithm ◽  
Collaborative Recommendation ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Community Detection Algorithm

The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.

Personalized Recommendation Algorithm Based on Product Reviews

2018 ◽  
Vol 16 (3) ◽  
pp. 22-38
Author(s):  
Zhibo Wang ◽  
Mengyuan Wan ◽  
Xiaohui Cui ◽  
Lin Liu ◽  
Zixin Liu ◽  
...  
Keyword(s):  
Recommendation System ◽  
Information Overload ◽  
Recall Rate ◽  
Personalized Recommendation ◽  
The Internet ◽  
Product Reviews ◽  
Recommendation Algorithm ◽  
Preference Model ◽  
Product Features ◽  
Huge Data

Under the background of leap-forward development for the internet, e-commerce has played an important role in people's daily life, but huge data sizes have also brought problems, such as information overload which can be solved by using a recommendation system effectively. However, with the development of the e-commerce, the amount of the product catalogs and users becomes larger, which causes lower performance of the traditional recommendation system. This article comes up with a personalized recommendation algorithm based on the data mining of product reviews to optimize the performance of the new recommendation system. Features of the product were extracted, for which the users' sentiment polarity was analyzed. This article develops a recommendation system based on the user's preference model and the product features to get the recommendation result. Experimental results show that a personalized recommendation has significantly improved the accuracy and recall rate when compared with a traditional recommendation algorithm.

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