A Taxonomy of Recommender Agents on the Internet

  title={A Taxonomy of Recommender Agents on the Internet},
  author={Miquel Montaner and Beatriz L{\'o}pez and Josep Llu{\'i}s de la Rosa i Esteva},
  journal={Artificial Intelligence Review},
Recently, Artificial Intelligence techniques have proved useful inhelping users to handle the large amount of information on the Internet.The idea of personalized search engines, intelligent software agents,and recommender systems has been widely accepted among users who requireassistance in searching, sorting, classifying, filtering and sharingthis vast quantity of information. In this paper, we present astate-of-the-art taxonomy of intelligent recommender agents on theInternet. We have… 

Recommender Agent Based on Hybrid Approach

The proposed Document Recommender Agent, that can recommend the most relevant papers based on the academician's interest adopts a hybrid recommendation approach and is shown to be better that the content-based and the collaborative approaches.


A contextual model of recommendation engine which keeping in mind the context and activities of a user, recommends resources in an academic networking portal is presented.

Collaborative recommender agents based on case-based reasoning and trust

This thesis proposes a new CBR approach to recommendation, a forgetting mechanism for case-based profiles that controls the relevance and age of past experiences, and the agentification of recommender systems in order to take advantage of interesting agent properties such as proactivity, encapsulation or social ability.

Chapter IV Semantic Web Technologies in the Service of Personalization

This chapter explores a novel approach based on reasoning about the semantics of both the users’ preferences and considered items, by resorting to less rigid inference mechanisms borrowed from the Semantic Web.

A Review on Advanced Algorithms in Recommender Systems

The main objective of this paper is to give the researchers an outline of the effective algorithms used in developing a stabilized powerful RS tool which suggests not-yet-experienced products but that may suit according to the users current preference.

A survey of contend-based filtering technique for personalized recommendations

A survey of content-based filtering recommender systems is presented and the landscape of different recommendation methods and their basic approaches are provided.

Preference Learning in Recommender Systems

The paper provides a general overview of the approaches to learning preference models in the context of recommender systems.

E-commerce recommenders' authority: applying the user's opinion relevance in recommender systems

A model (Mo-DROP) for the computation of the user's relevance of opinion (Recommneder's Rank metric) and its application in a specific domain of knowledge using information from Recommender Systems is described.

A hybrid movie recommender system based on neural networks

A combination of the results of content-based and collaborative filtering techniques is used in this work in order to construct a system that provides more precise recommendations concerning movies.

Towards a General Architecture for Building Intelligent , Flexible , and Adaptable Recommender System Based on MAS Technology

This paper presents the attempt to use agent technology to enhance recommender systems based on agent’s property advantages with the goal to analyze and design a general architecture easily adaptable to several domains.



Beyond Recommender Systems: Helping People Help Each Other

This work presents a framework for understanding recommender systems and surveys a number of distinct approaches in terms of this framework, and suggests two main research challenges: helping people form communities of interest while respecting personal privacy and developing algorithms that combine multiple types of information to compute recommendations.

E-Commerce Recommendation Applications

An explanation of how recommender systems are related to some traditional database analysis techniques is presented, and a taxonomy ofRecommender systems is created, including the inputs required from the consumers, the additional knowledge required from a database, the ways the recommendations are presented to consumers,The technologies used to create the recommendations, and the level of personalization of the recommendations.

Learning user information interests through extraction of semantically significant phrases

This paper describes an intelligent agent developed to address this problem similar to research systems under development for similar tasks, and presents the solution in the context of a Lotus Notes system, consisting of electronic mail, bulletin boards, news services, and databases.

Combining Content-based and Collaborative Recommendation Diierent Approaches to Recommendation Content-based Recommendation

The two approaches of content-based and collaborative recommendation are described, how a hybrid system can be created and described is explained and Fab, an implementation of such a system, is described.

Combining Collaborative Filtering with Personal Agents for Better Recommendations

This paper shows that a CF framework can be used to combine personal IF agents and the opinions of a community of users to produce better recommendations than either agents or users can produce alone.

Ontology based personalized search

  • A. PretschnerSusan Gauch
  • Computer Science
    Proceedings 11th International Conference on Tools with Artificial Intelligence
  • 1999
This paper explores ways of incorporating users' interests into the search process to improve the results and shows that fully automatic creation of large hierarchical user profiles is possible.

An intelligent agent for high-precision text filtering

An overview of a research project aimed at reducing information overload for individual computer users, which is based on art intelligent agent approach and embodies machine learning, adaptation and relevance feedback techniques in its construction.

Learning Collaborative Information Filters

This work proposes a representation for collaborative filtering tasks that allows the application of virtually any machine learning algorithm, and identifies the shortcomings of current collaborative filtering techniques and proposes the use of learning algorithms paired with feature extraction techniques that specifically address the limitations of previous approaches.

Empirical Analysis of Predictive Algorithms for Collaborative Filtering

Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.

Automatic personalization based on Web usage mining

The ability to track users’ browsing behavior down to individual mouse clicks has brought the vendor and end customer closer than ever before, and it is now possible for a vendor to personalize his product message for individual customers at a massive scale, a phenomenon that is being referred to as mass customization.