Research on Item Model in Content-Based Filtering Recommender Systems

2011 ◽  
Vol 480-481 ◽  
pp. 1235-1239 ◽  
Author(s):  
Song Jie Gong
Keyword(s):  
Recommender Systems ◽  
Coming Out ◽  
Information Overload ◽  
Probability Model ◽  
Information Filtering ◽  
Software Tool ◽  
Model Representation ◽  
Space Model ◽  
Advantages And Disadvantages ◽  
Content Based Filtering

With the popularization of the Internet and the development of E-commerce, the information on the Networks has increased greatly and the E-Commerce system’s structure becomes more complicated when it provides more and more choices for users. People all have experienced the feeling of being overwhelmed by the number of new books, articles, and movies coming out each year. Many researchers pay more attention on building a proper tool which can help users obtain personalized resources. Personalized recommender systems are one such software tool in which information retrieve, information filtering, and content-based filtering techniques are used to help users obtain recommendations for unseen items based on their preferences. In this paper, described item models in content-based filtering recommender systems in order to alleviate the information overload issues. The paper presented three item models as following: vector space model representation, probability model representation and improved probabilistic model representation. These item models have their own advantages and disadvantages, and can choose according to specific circumstances.

E-Commerce Personalized Recommendation

2014 ◽  
Vol 989-994 ◽  
pp. 4996-4999 ◽  
Author(s):  
Yan Zhang
Keyword(s):  
Information Retrieval ◽  
Electronic Commerce ◽  
Association Rule ◽  
Information Overload ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
Application Layer ◽  
Advantages And Disadvantages ◽  
Theory Research ◽  
Content Based Filtering

With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site, the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.

A State-of-the-Art Survey on Context-Aware Recommender Systems and Applications

2021 ◽  
Vol 12 (3) ◽  
pp. 1-20
Author(s):  
Quang-Hung Le ◽  
Son-Lam Vu ◽  
Thi-Kim-Phuong Nguyen ◽  
Thi-Xinh Le
Keyword(s):  
Recommender Systems ◽  
Recommendation System ◽  
Contextual Information ◽  
Information Filtering ◽  
Weather Conditions ◽  
Research Literature ◽  
Future Research ◽  
Context Aware ◽  
Good Tool ◽  
Advantages And Disadvantages

In the digital transformation era, increasingly more individuals and organizations use or create services in digital spaces. Many business transactions have been moving from the offline to online mode. For example, sellers intend to introduce their products on e-commerce platforms rather than display them on store shelves as in traditional business. Although this new format business has advantages, such as more space for product displays, more efficient searches for a specific item, and providing a good tool for both buyers and sellers to manage their products, it is also accompanied by the obviously important problem that users are confused when choosing an appropriate item due to a large amount of information. For this reason, the need for a recommendation system appears. Informally, a recommender system is similar to an information filtering system that helps identify a set of items that best satisfy users' demands based on their preference profiles. The integration of contextual information (e.g., location, weather conditions, and user's mood) into recommender systems to improve their performance has recently received considerable attention in the research literature. However, incorporating such contextual information into recommendation models is a challenging task because of the increase in both the dimensionality and sparsity of the model. Different approaches with their own advantages and disadvantages have been proposed. This paper provides a comprehensive survey on context-aware recommender systems in recent years. In particular, the authors pay more attention to journal and conference proceedings papers published from 2016 to 2020. In addition, this paper also presents open issues for context-aware recommender systems and discuss promising directions for future research.

On the integration of Machine Learning algorithms and operations research techniques in the development of a hybrid Recommender System

2021 ◽  
pp. 1-14
Author(s):  
Panagiotis Giannopoulos ◽  
Georgios Kournetas ◽  
Nikos Karacapilidis
Keyword(s):  
Machine Learning ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Learning Algorithms ◽  
Information Filtering ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbors ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Recommender Systems is a highly applicable subclass of information filtering systems, aiming to provide users with personalized item suggestions. These systems build on collaborative filtering and content-based methods to overcome the information overload issue. Hybrid recommender systems combine the abovementioned methods and are generally proved to be more efficient than the classical approaches. In this paper, we propose a novel approach for the development of a hybrid recommender system that is able to make recommendations under the limitation of processing small amounts of data with strong intercorrelation. The proposed hybrid solution integrates Machine Learning and Multi-Criteria Decision Analysis algorithms. The experimental evaluation of the proposed solution indicates that it performs better than widely used Machine Learning algorithms such as the k-Nearest Neighbors and Decision Trees.

The Research of E-Commerce Personalized Recommendation

2014 ◽  
Vol 556-562 ◽  
pp. 6762-6765
Author(s):  
Yan Zhang ◽  
Tao Kuang
Keyword(s):  
Information Retrieval ◽  
Electronic Commerce ◽  
Association Rule ◽  
Information Overload ◽  
Rapid Development ◽  
Personalized Recommendation ◽  
Application Layer ◽  
Advantages And Disadvantages ◽  
Theory Research ◽  
Content Based Filtering

With the rapid development of electronic commerce, the problem of "information overload" leads to the difficulty that user can't search the required goods effectively , personalized recommendation technology has been applied in e-commerce and popularization. By using the method of qualitative analysis of the current e-commerce site,the paper compare the information retrieval, association rule, content-based filtering and collaborative filtering four main recommendation technologies and analysis the advantages and disadvantages in the application layer, the recommendation technologies are introduced to review e-commerce research hot topic in the field of personalized recommendation, and analysis the current domestic e-commerce personalized recommendation theory research and application status, finally propose the challenges faced by e-commerce personalized recommendation domain.

scholarly journals Recommender systems in model-driven engineering

2021 ◽  
Author(s):  
Lissette Almonte ◽  
Esther Guerra ◽  
Iván Cantador ◽  
Juan de Lara
Keyword(s):  
Recommender Systems ◽  
Information Filtering ◽  
Research Work ◽  
Model Driven Engineering ◽  
Video Broadcasting ◽  
Model Driven ◽  
Challenges And Opportunities ◽  
Mapping Review ◽  
Development Processes ◽  
Systematic Mapping Review

AbstractRecommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.

scholarly journals A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5248
Author(s):  
Aleksandra Pawlicka ◽  
Marek Pawlicki ◽  
Rafał Kozik ◽  
Ryszard S. Choraś
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Recommender System ◽  
State Of The Art ◽  
The State ◽  
Advantages And Disadvantages ◽  
Comprehensive Survey ◽  
Security Concerns ◽  
Valuable Role

This paper discusses the valuable role recommender systems may play in cybersecurity. First, a comprehensive presentation of recommender system types is presented, as well as their advantages and disadvantages, possible applications and security concerns. Then, the paper collects and presents the state of the art concerning the use of recommender systems in cybersecurity; both the existing solutions and future ideas are presented. The contribution of this paper is two-fold: to date, to the best of our knowledge, there has been no work collecting the applications of recommenders for cybersecurity. Moreover, this paper attempts to complete a comprehensive survey of recommender types, after noticing that other works usually mention two–three types at once and neglect the others.

scholarly journals Graph Learning based Recommender Systems: A Review

2021 ◽  
Author(s):  
Shoujin Wang ◽  
Liang Hu ◽  
Yan Wang ◽  
Xiangnan He ◽  
Quan Z. Sheng ◽  
...  
Keyword(s):  
Systematic Review ◽  
Recommender Systems ◽  
Rapid Development ◽  
Research Area ◽  
Learning Approaches ◽  
Graph Learning ◽  
Promising Direction ◽  
Learning Techniques ◽  
Fast Development ◽  
Content Based Filtering

Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ advanced graph learning approaches to model users’ preferences and intentions as well as items’ characteristics and popularity for Recommender Systems (RS). Differently from other approaches, including content based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs is a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract knowledge from graphs to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area.

Web Usage Mining Approaches for Web Page Recommendation

2013 ◽  
pp. 271-288
Author(s):  
H. Inbarani ◽  
K. Thangavel
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Decision Rule ◽  
Web Usage Mining ◽  
Web Page ◽  
Web Personalization ◽  
Rule Based ◽  
Web Usage ◽  
User Ratings ◽  
Content Based Filtering

The technology behind personalization or Web page recommendation has undergone tremendous changes, and several Web-based personalization systems have been proposed in recent years. The main goal of Web personalization is to dynamically recommend Web pages based on online behavior of users. Although personalization can be accomplished in numerous ways, most Web personalization techniques fall into four major categories: decision rule-based filtering, content-based filtering, and collaborative filtering and Web usage mining. Decision rule-based filtering reviews users to obtain user demographics or static profiles, and then lets Web sites manually specify rules based on them. It delivers the appropriate content to a particular user based on the rules. However, it is not particularly useful because it depends on users knowing in advance the content that interests them. Content-based filtering relies on items being similar to what a user has liked previously. Collaborative filtering, also called social or group filtering, is the most successful personalization technology to date. Most successful recommender systems on the Web typically use explicit user ratings of products or preferences to sort user profile information into peer groups. It then tells users what products they might want to buy by combining their personal preferences with those of like-minded individuals. However, collaborative filtering has limited use for a new product that no one has seen or rated, and content-based filtering to obtain user profiles might miss novel or surprising information. Additionally, traditional Web personalization techniques, including collaborative or content-based filtering, have other problems, such as reliance on subject user ratings and static profiles or the inability to capture richer semantic relationships among Web objects. To overcome these shortcomings, the new Web personalization tool, nonintrusive personalization, attempts to increasingly incorporate Web usage mining techniques. Web usage mining can help improve the scalability, accuracy, and flexibility of recommender systems. Thus, Web usage mining can reduce the need for obtaining subjective user ratings or registration-based personal preferences. This chapter provides a survey of Web usage mining approaches.

Recommender Technologies and Emerging Applications

2019 ◽  
pp. 458-470
Author(s):  
Young Park
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Future Research ◽  
Research Topics ◽  
Emerging Trends ◽  
System Approaches ◽  
Age Of Information

This chapter presents a brief overview of the field of recommender technologies and their emerging application domains. The authors explain the current major recommender system approaches within a unifying model, discuss emerging applications of recommender systems beyond traditional e-commerce, and outline emerging trends and future research topics, along with additional readings in the area of recommender technologies and applications. They believe that personalized recommender technologies will continue to advance and be applied in a variety of traditional and emerging application domains to assist users in the age of information overload.

Sign in / Sign up

Export Citation Format

Share Document