It is generally assumed that consumers in electronic shops feel more comfortable engaging in a dialog regarding their needs as opposed to inspecting detailed product features. However, empirical evaluations of needs-based recommender systems indicate that consumers are only confident about their buying decision if they can compare detailed information about product features. Ideally consumers s...

A product recommender system based on product-review information and metadata history was implemented in our project. The primary goal for our recommender system is predicting the rating value that a user will give to a product. We used collaborative filtering model with both user-based and itembased strategies, matrix factorization model and a graph-based Network Inference model as our rating ...

Recommender systems that inform consumers about their likely ideal product have become the cornerstone of eCommerce platforms that sell products from competing manufacturers. Using a model of an electronic marketplace in which two competing manufacturers produce substitutable products and sell the products through a common retail platform, we study the e ect of recommender systems on the market...

Recommender systems are important tools for users to identify their preferred items and for businesses to improve their products and services. In recent years, the use of online services for selection and reservation of hotels have witnessed a booming growth. Customer’ reviews have replaced the word of mouth marketing, but searching hotels based on user priorities is more time-consuming. This s...

As the information on the Web grows, the need of recommender systems to ease user navigations becomes evident. There exist many approaches of learning for Web usage based recommender systems. In this study, we apply and compare some of the methods of usage pattern discovery, like simple k-means clustering algorithm, fuzzy relational subtractive clustering algorithm, fuzzy mean field annealing c...

In customer relationship management (CRM), online recommender assumes an important role of suggesting the right product or information to the right customer automatically. Hence customers are empowered with the choices that are predicted to be preferred by the system. The underlying technique is often a collaborative filtering (CF) algorithm that harvests both information from similar products ...

In the context of recommender systems, there are two types of enties: users and items, and three types of relationships: users’ relationship, items’ connection and interactions between users and items. In most literatures, one or more of these entities and relationships are used to predict users’ preference or taste. In this paper, we propose a novel approach which incorporates these two entiti...

Recommender systems help users to encounter information or items that are of interest to them. Prior work on recommender systems has focused on eliciting preferences for items and neglected the personal traits in discount sensitivity. In this paper, we propose a recommender system that incorporates the influence of discounts. The effectiveness of the model is verified using a public retail data...

Recommender systems have changed the way people find products, points of interest, services or even new friends. The technology behind recommender systems has evolved to provide user preferences and social influences. In this paper, we present a first approach to develop a recommendation engine based on social metrics applied to graphs that represent object’s characteristics, user profiles and ...