Learning from Substitutable and Complementary Relations for Graph-based Sequential Product Recommendation

Sequential product recommendation, aiming at predicting the products that a target user will interact with soon, has become a hotspot topic. Most of the sequential recommendation models focus on learning from users’ interacted product sequences in a purely data-driven manner. However, they largely overlook the knowledgeable substitutable and complementary relations between products. To address this issue, we propose a novel Substitutable and Complementary Graph-based Sequential Product Recommendation model, namely, SCG-SPRe. The innovations of SCG-SPRe lie in its two main modules: (1) The module of interactive graph neural networks jointly encodes the high-order product correlations in the substitutable graph and the complementary graph into two types of relation-specific product representations. (2) The module of kernel-enhanced transformer networks adaptively fuses multiple temporal kernels to characterize the unique temporal patterns between a candidate product to be recommended and any interacted product in a target behavior sequence. Thanks to the seamless integration of the two modules, SCG-SPRe obtains candidate-dependent user representations for different candidate products to compute the corresponding ranking scores. We conduct extensive experiments on three public datasets, demonstrating SCG-SPRe is superior to competitive sequential recommendation baselines and validating the benefits of explicitly modeling the product-product relations.

A Novel Approach of Product Recommendation using Utility Based Association Rules

An exorbitant source of data is easily available but the actual task lies in using this data efficiently. In this article, the aim is to analyse the significant information embedded in the customer purchase behaviour to recommend new products to them. Our proposed scheme is a two-fold approach. First, the authors retrieve various product correlations from the vast library of user transactions. Based on these product correlations, utility based association rules are learned which depict the customer purchase behaviour. These rules are then applied in a recommender system for novel product suggestions to the customers. With improved utility based mining the paper tries to incorporate the usefulness of an item set like cost, profit or any other factor along with their frequency. In this paper the authors have deployed the rules discovered from both the conventional Frequent Item Set Mining and Improved Utility Based Mining on an e-commerce platform to compare the accuracy of the algorithms. The obtained results establish the efficacy of the proposed algorithm.

Personalized Product Recommendation and User Satisfaction

A recommendation system is a significant part of artificial intelligence (AI) to help users' access information at any time and from anywhere. Online product recommender systems are widely used to recommend products based on consumers' preferences. The traditional recommendation algorithms of recommendation engines do not meet the needs of users in the AI environment when exposed to large amounts of data resulting in a low recommendation efficiency. To address this, a personalized recommendation system was introduced. These personalized recommendation systems (PRS) are an important component for ecommerce players in the Indian e-commerce aspects. Since personalized recommendations are becoming increasingly popular, this study examines information processing theory with respect to personalized recommendations and their impact on user satisfaction. Further, relationships between the variables were examined by conducting regression analysis and found a positive correlation exists between personalized product recommendation and user satisfaction.

Research on Deep Learning-Based Algorithm and Model for Personalized Recommendation of Resources

Resource recommendation system is a new type of management system, which uses personalized information to solve business needs such as customer consultation and product recommendation, and provides users with high quality services and achieves accurate marketing, so nowadays resource recommendation system has a pivotal role in modern resource management. In this paper, I study the algorithm and model of resource personalized recommendation based on deep learning, taking human resource recommendation as an example.

Product Recommendation Based on Eye Tracking Data Using Fixation Duration

E-commerce can be used to increase companies or sellers’ profits. For consumers, e-commerce can help them shop faster. The weakness of e-commerce is that there is too much product information presented in the catalog which in turn makes consumers confused. The solution is by providing product recommendations. As the development of sensor technology, eye tracker can capture user attention when shopping. The user attention was used as data of consumer interest in the product in the form of fixation duration following the Bojko taxonomy. The fixation duration data was processed into product purchase prediction data to know consumers’ desire to buy the products by using Chandon method. Both data could be used as variables to make product recommendations based on eye tracking data. The implementation of the product recommendations based on eye tracking data was an eye tracking experiment at selvahouse.com which sells hijab and women modest wear. The result was a list of products that have similarities to other products. The product recommendation method used was item-to-item collaborative filtering. The novelty of this research is the use of eye tracking data, namely the fixation duration and product purchase prediction data as variables for product recommendations. Product recommendation that produced by eye tracking data can be solution of product recommendation’s problems, namely sparsity and cold start.

Product Brokering Efficiency as a mediator of Online Product Recommendation and Customer Loyalty

Marketing strategies in e-commerce have a main goal, that is to pursue customer loyalty. Sociolla is an e-commerce company that sells cosmetic products and has a recommendation feature to make it easier for customers during the shopping process. These recommendations can trigger customer satisfaction and generate loyalty. The purpose of this study was to examine the correlation between online product recommendation and customer loyalty with product brokering efficiency as a mediator. 179 Sociolla customers were recruited in this study using convenience sampling. The data were analyzed using the SPSS-Process Hayes model 4. Results showed that perceived decision quality acts as a mediator in the relationship between enablers and customer loyalty (β = .20, [ .13; .27]). It can be concluded that recommendations that are comprehensive, clear, and meet the customer needs will make it easier for customers to make purchasing decisions, which ultimately leads the customers to form loyalty toward the products.

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

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.