Item-based Recommender Systems Research Articles

“Analyzing User-Based and Item-Based Recommender Systems: A Comparative Examination”

“Analyzing User-Based and Item-Based Recommender Systems: A Comparative Examination”

Implementing a Hybrid Recommendation System to Personalize Customer Experience in E-Commerce Domain

With the rapid use of the internet, a huge amount of data is generated over the network with every passing second. On the other hand, the user demands information that is relevant to his personal search. The processing of such huge data is a challenging task. To serve the user with his specific required information, there is a need for an information retrieval mechanism that can process this large data. A recommendation system is such required technology that retrieves information in order to improve users’ access and thereby recommending items that are relevant to his explicitly mentioned behavior and preferences. The recommendation algorithm analyzes the huge dataset and focuses to recommend accurate content to the user. There are several recommendation systems in use today, some popular among them are Netflix, YouTube, Tinder, and Amazon. In this article, discussion is done on various types of recommendation systems, issues of recommendation systems, and use cases of widely used recommendation engines and their potential benefits. The work introduced in this paper is an integration approach of domain-specific and item-based recommendation system. The performance of the proposed algorithm is measured on evaluation metrics precision and recall. Experimental results prove that the approach introduced in this paper performed well as compared to existing methods.

Pointer-Based Item-to-Item Collaborative Filtering Recommendation System Using a Machine Learning Model

The creation of digital marketing has enabled companies to adopt personalized item recommendations for their customers. This process keeps them ahead of the competition. One of the techniques used in item recommendation is known as item-based recommendation system or item–item collaborative filtering. Presently, item recommendation is based completely on ratings like 1–5, which is not included in the comment section. In this context, users or customers express their feelings and thoughts about products or services. This paper proposes a machine learning model system where 0, 2, 4 are used to rate products. 0 is negative, 2 is neutral, 4 is positive. This will be in addition to the existing review system that takes care of the users’ reviews and comments, without disrupting it. We have implemented this model by using Keras, Pandas and Sci-kit Learning libraries to run the internal work. The proposed approach improved prediction with [Formula: see text] accuracy for Yelp datasets of businesses across 11 metropolitan areas in four countries, along with a mean absolute error (MAE) of [Formula: see text], precision at [Formula: see text], recall at [Formula: see text] and F1-Score at [Formula: see text]. Our model shows scalability advantage and how organizations can revolutionize their recommender systems to attract possible customers and increase patronage. Also, the proposed similarity algorithm was compared to conventional algorithms to estimate its performance and accuracy in terms of its root mean square error (RMSE), precision and recall. Results of this experiment indicate that the similarity recommendation algorithm performs better than the conventional algorithm and enhances recommendation accuracy.

Item-Based Recommender Systems Applying Social-Economic Indicators

Despite the development of online items in the market, Item-based Recommender Systems play an essential role to assist consumers to access their targets rapidly. Research activities show that users (consumers) in various societies have different purchasing behaviors. In the latest studies, the similarity of users was calculated based on different indications such as Cultural Indicators. Nonetheless, they neglect social-economic indicators expected to be more factors that are efficient. In this research, a framework was designed for recommender systems called SEIRS that include several algorithms based on social-economic indicators. In fact, the similarity of items was defined in terms of the similarity between users’ evaluation based on social-economic indicators. The presented method categorizes items and recommends them to users so that they match with users’ favorites more precisely. The results were compared not only with the standard item-based system but also with different mixtures of social-based and economical-based indicators. In addition, the presented idea was used to solve the cold start problem that occurs when a newcomer customer with no information on his previous purchases and evaluations becomes available. Furthermore, it is possible to present suggestions that are more precise by using the SVD algorithm to solve the data sparsity. The present research applied three datasets: first, internet purchase by individuals (isoc_ibuy) dataset, second the related data on social-economic indicators from OECD, and third the actual BookCrossing dataset. The experimental results represent that in comparison with the standard recommender system, by applying social indicators the proposed recommender algorithm improved precision by 17.19%, and economic indicators improved precision by 17.60%, respectively. Applying both social and economic indicators provides 20.91% more accurate recommendations.