Performance Evaluation of DLSARS Framework in Intelligent Product Recommendation Systems

2020 ◽  
Author(s):  
Dr. G. Lavanya Devi ◽  
S Parvathi Vallabhaneni ◽  
Dr. Jhansi Rani Singothu
Keyword(s):  
Performance Evaluation ◽  
Recommendation Systems ◽  
Product Recommendation ◽  
Intelligent Product

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 A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1430
Author(s):  
Guisheng Chen ◽  
Zhanshan Li
Keyword(s):  
State Of The Art ◽  
Recommendation Systems ◽  
Statistical Characteristics ◽  
Periodic Pattern ◽  
Probability Models ◽  
Periodic Patterns ◽  
Market Basket ◽  
Product Recommendation ◽  
Precise Prediction ◽  
Pattern Prediction

Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.

scholarly journals Performance Evaluation of DLSARS Framework in Intelligent Product Recommendation Systems

2020 ◽  
pp. 165-175
Keyword(s):  
Performance Evaluation ◽  
Deep Learning ◽  
Recommendation System ◽  
The Other ◽  
Program Model ◽  
Learning Models ◽  
Technical Approach ◽  
Huge Number ◽  
Intelligent Product ◽  
The Right

The recommendation framework is vital tool for efficient E-commerce contacts between customers and retailers. Efficient and friendly contacts to find the right product have a huge effect on the sales results. In the basis of a technical approach, four of the program model guidelines are: collective filtering, content-based and demographic filtering. Collaborative filtering is considered superior to other methods in the list. Of necessity, in terms of fortuity, novelty and precision, it provides advantages. The DLSARS Framework is a deep learning-based sentiment analysis for the DLSARS recommendation system that uses deep learning models for a proposed system. The dataset selected for this research is synthetic dataset which consists of huge number of reviews for every product. The proposed models display superiorities and compare the findings with other existing models. The proposed DLSARS frame with bigram approach is superior to the other domain on the E-commerce domain.

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?’

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