Proceedings of the 7th Workshop on Intelligent Techniques for Web Personalization & Recommender Systems (ITWP'09), Pasadena, California, USA, July 11-17, 2009 in conjunction with the 21st International Joint Conference on Artificial Intelligence - IJCAI 2009 (original) (raw)

Online Selection of Mediated and Domain-Specific Predictions for Improved Recommender Systems

2009

Abstract Recommender systems use a set of reviewers and advice givers with the goal of providing accurate userdependent product predictions. In general, these systems assign weights to different reviewers as a function of their similarity to each user. As products are known to be from different domains, a recommender system also considers product domain information in its predictions.

Informed recommender: Basing recommendations on consumer product reviews

Intelligent Systems, …, 2007

R ecommender systems attempt to predict items in which a user might be interested, given some information about the user's and items' profiles. Most existing recommender systems use content-based or collaborative filtering methods or hybrid methods that combine both techniques (see the sidebar for more details). We created Informed Recommender to address the problem of using consumer opinion about products, expressed online in free-form text, to generate product recommendations.

A Result Review Analysis of Product Recommendation System in Domain Sensitive Manner

2020

1,2G.H. Raisoni University Amravati, Maharashtra --------------------------------------------------------------------------***---------------------------------------------------------------------------ABSTRACT: With the wide variety of products and services available on the web, it is difficult for users to choose the product or service that most meets their needs. In order to reduce or even eliminate this difficulty, recommender systems have emerged. A recommender system is used in various fields to recommend items of interest to users. One of the main areas where this concept is currently used is e-commerce that interacts directly with customers by suggesting products of interest with the aim of improving its sales. Motivated by the observation, a novel Domain-sensitive Recommendation (DsRec) algorithm is proposed, to make the rating prediction by exploring the user-item subgroup analysis simultaneously, in which a user-item subgroup is deemed as a domain consisting of a subset of...

IRJET- A Result Review Analysis of Product Recommendation System in Domain Sensitive Manner

IRJET, 2020

With the wide variety of products and services available on the web, it is difficult for users to choose the product or service that most meets their needs. In order to reduce or even eliminate this difficulty, recommender systems have emerged. A recommender system is used in various fields to recommend items of interest to users. One of the main areas where this concept is currently used is e-commerce that interacts directly with customers by suggesting products of interest with the aim of improving its sales. Motivated by the observation, a novel Domain-sensitive Recommendation (DsRec) algorithm is proposed, to make the rating prediction by exploring the user-item subgroup analysis simultaneously, in which a user-item subgroup is deemed as a domain consisting of a subset of items with similar attributes and a subset of users who have interests in these items. Collaborative Filtering (CF) is an effective and widely adopted recommendation approach. Different from content-based recommender systems which rely on the profiles of users and items for predictions, CF approaches make predictions by only utilizing the user-item interaction information such as transaction history or item satisfaction expressed in ratings, etc.

Prediction of E-Commerce Product Ratings Based on Similar Users

International Journal of Engineering and Computer Science, 2021

Together with the fast advancement of continuous expansion and the Internet of E-commerce scope, product quantity, as well as assortment, boost fast. Merchants offer many goods via going shopping customers and websites generally consider a huge amount of moment to discover the products of theirs.Within e-commerce sites, the item rating is among the primary key ingredients of an excellent pc user expertise. Many methods are working with whose users to consider the goods they wish. A comparable item suggestion is among the favorite modes working with whose customers look for items in line with the item scores. In general, the suggestions aren't personalized to a particular pc user. Exploring a great deal of solutions tends to make customers runoff as a result of the info clog but not offering proper reviews for solutions.Traditional algorithms has data sparsity and cold start issues. To overcome these problems we use cosine similarity method to identify the similarity between thos...

Recommender Systems: An Overview

AI Magazine

Recommender systems are tools for interacting with large and complex information spaces. They provide a personalized view of such spaces, prioritizing items likely to be of interest to the user. The field, christened in 1995, has grown enormously in the variety of problems addressed and techniques employed, as well as in its practical applications. Recommender systems research has incorporated a wide variety of artificial intelligence techniques including machine learning, data mining, user modeling, case-based reasoning, and constraint satisfaction, among others. Personalized recommendations are an important part of many on-line e-commerce applications such as Amazon.com, Netflix, and Pandora. This wealth of practical application experience has provided inspiration to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of the articles in this special issue is to take stock of the current landscape of recommender systems research and id...

Matching Recommendation Technologies and Domains

Recommender Systems Handbook, 2010

Recommender systems form an extremely diverse body of technologies and approaches. The chapter aims to assist researchers and developers identify the recommendation technology that are most likely to be applicable to different domains of recommendation. Unlike other taxonomies of recommender systems, our approach is centered on the question of knowledge: what knowledge does a recommender system need in order to function, and where does that knowledge come from? Different recommendation domains (books vs condominiums, for example) provide different opportunities for the gathering and application of knowledge. These considerations give rise to a mapping between domain characteristics and recommendation technologies.

Recommendation System Issues, Approaches and Challenges Based on User Reviews

Journal of Web Engineering, 2022

With the ever-increasing volume of online information, recommender systems have been effective as a strategy to overcome information overload. They have a wide range of applications in many fields, including e-learning, e-commerce, e-government and scientific research. Recommender systems are search engines that are based on the user’s browsing history to suggest a product that expresses their interests. Being usually in the form of textual comments and ratings, such reviews are a valuable source of information about users’ perceptions. Recommender systems (RSs) apply various approaches to predict users’ interest on information, products and services among a huge amount of available items. In this paper, we will describe the recommender system, discuss ongoing research in this field, and address the challenges, limitations and the techniques adopted. This paper also discusses how review texts are interpreted to solve some of the major problems with traditional recommendation techniq...

Understanding Personalization of Recommender System : A Domain Perspective

2018

The abundance of information paved the way for personalization of web information retrieval systems in order to garner the attention of the web users. With an inclination towards customer oriented service, the online systems render recommendations to provide items of interest to the web user. Personalization in recommendation systems is achieved by creation of custom alternatives for delivering the right experience to the right user at the right time through the right device. Domain relevant personalization is the need of the hour and research in recommendation system is towards identifying the domain specific characteristics for providing more accurate recommendations. This research article provides an overview of various domain adaptation strategies incorporated and practiced in the recommendation system literature. The article focus on domain based personal agents in the prior research of the recommendation system for the domain of music, video, product sale, tourism, social netw...