Agent-based computational investing recommender system

The motivation behind personal information agents resides in the enormous amount of information available on the Web, which has created a pressing need for effective personalized techniques. In order to assists Web search these agents rely on user profiles modeling information preferences, interests and habits that help to contextualize user queries. In communities of people with similar interests, collaboration among agents fosters knowledge sharing and, consequently, potentially improves the results of individual agents by taking advantage of the knowledge acquired by other agents. In this paper, we propose an agent-based recommender system for supporting collaborative Web search in groups of users with partial similarity of interests. Empirical evaluation showed that the interaction among personal agents increases the performance of the overall recommender system, demonstrating the potential of the approach to reduce the burden of finding information on the Web.

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Agent based mobile recommender system

2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) ◽

10.1109/iceee.2014.6978332 ◽

2014 ◽

Cited By ~ 1

Author(s):

Sahar Moin ◽

Aslam Muhamamd ◽

A. M. Martinez-Enriquez

Keyword(s):

Recommender System ◽

Agent Based

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Case-Based Reasoning and Agent Based Job Offer Recommender System

Advances in Intelligent Systems and Computing - International Joint Conference SOCO’18-CISIS’18-ICEUTE’18 ◽

10.1007/978-3-319-94120-2_3 ◽

2018 ◽

pp. 21-33 ◽

Cited By ~ 5

Author(s):

Alfonso González-Briones ◽

Alberto Rivas ◽

Pablo Chamoso ◽

Roberto Casado-Vara ◽

Juan Manuel Corchado

Keyword(s):

Recommender System ◽

Case Based Reasoning ◽

Agent Based ◽

Case Based

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AN AGENT-BASED RECOMMENDER SYSTEM USING IMPLICIT FEEDBACK, IMPROVED INTER-USER SIMILARITY AND RATING PREDICTION

Proceedings of the 11th Joint International Computer Conference ◽

10.1142/9789812701534_0084 ◽

2005 ◽

Cited By ~ 1

Author(s):

Qian WANG ◽

Ji LIU ◽

Xingxing LI

Keyword(s):

Recommender System ◽

Implicit Feedback ◽

Agent Based ◽

User Similarity ◽

Rating Prediction

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Distributed recommender system using multi-agent for social networks

10.32920/ryerson.14648478 ◽

2021 ◽

Author(s):

Lubaid Ahmed

Keyword(s):

Social Networks ◽

Social Network ◽

Recommender Systems ◽

Recommender System ◽

Network Simulator ◽

Clustering Methods ◽

Agent Based Simulation ◽

Agent Based ◽

Data Mining Algorithms ◽

Multi Agent

Social networks have become significant tools due to the vast and useful information existing in them. The social platforms also act as the storage of entered choices of millions of users for various applications such as political surveys, research studies, marketing product preferences and many more. Social network recommender systems exploit this information and direct users in selecting their choices. It is clear that recommender systems should be efficient enough to be able to process the huge magnitude of data that has been generated in recent years by social network users. This research proposes a foundation of an efficient and scalable recommender system to be able to process large amount of data (i.e. Big data) in a short amount of time. The main goal is providing scalability and efficiency of the recommender system. The simulation of the prototype of such a distributed recommender system by using multi-agent based technologies shows promising results. These prototypes provide recommendations to users about other users with the similar interests in online and distributed manner as real recommender systems. The agents can simulate users or can be used as the containers of algorithms for comparing the similarity between users by different approaches, such as cosine similarity and clustering methods for testing and examining real scenarios. To be able to test these prototypes in agent-based simulation environment an agent-based framework is developed. This framework has three modules named social network crawler, social network simulator and employed prototype of the distributed recommender system that use different text and data mining algorithms. Finally, newly developed performance metric (called Scalability Factor) is introduced that shows the minimum number of servers needed to be able to run the agent systems in parallel. This thesis shows using a distributed and parallel model for recommender systems is the key to increase the speed of recommendation convergence and as a result to provide scalability. Multi-agent based simulation results, coupled with numerical analysis affirm that the proposed solution provides scalability and efficiency for recommender systems.

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