14 research outputs found
An agent system for information retrieval in an academic environment
No full textIn the recent years, academic domains just like other domains have undergone a tremendous growth on both content and users. This growth has lead to information overload in which academician are finding it difficult to locate the right academic paper at the right time. Search Engines, were designed originally to be helpful in searching for relevent resluts and returning them to users. Yet, due to thousands of petentially relevant sites, thus search engines are losing their usefulness. To address this problem, a development of a reliable multi-agent system that is able to guide academician through the big ocean of information by filtering the information and recommending them relevant papers is vital. However, recommending an item/academic paper is not easy since it depends on many factors such as the user’s current interest, as user’s interest changes over time, size of content, and number of users. This thesis presents the development of multi-agent system that helps academicians in the process of retrieving relevant academic papers by recommending them papers based on their current interest. The recommendation is generated using a Hybrid recommendation approach, which is a combination of the two well known recommendation approach, content-based filtering approach and collaborative filtering approach. The system consists of four agents working together. The first agent is Monitoring Agent that monitors User’s browsing behavior to implicitly observe users’ current interest. The second module is the Categorizer Agent that automatically organizes papers downloaded by users into subcategories based on ACM Association Computing Machinery CCS (Computing Classification System) structure by considering papers’ content similarity. The third agent is the Recommender Agent that recommends papers to users based on Hybrid approach, and the last agent is the Search Agent that allows users to search for academic papers locally. The use of multi-agent technology has overcome many problems that a traditional recommendation system suffers from. The accuracy of the Hybrid approach used by the proposed system in the recommendation is then compared with two other common recommendation approaches, content-based filtering approach and collaborative filtering approach by counting the precision value of each approach. Based on the results, the system was able to recommend well based on user’s current interest using Hybrid approach. Besides that, the categorizer agent has shown promising results in categorizing of academic papers based on the proposed ACM CCS system
Understanding factors underlying actual consumption of organic food: The moderating effect of future orientation
No full textThe majority of past studies focused on investigating the motivational factors to purchase organic food as a proxy to foster organic food consumption. However, the preceding studies’ foci do not embrace the consumption itself where purchasing may come secondary to consumption decisions. Consumption reflects high involvement with the product; and the barriers and motivations are as real as the product itself, which makes it an ideal moment to examine the motivation. The research model was analyzed using the Partial Least Square Structural Equation Modeling technique. Results show that product-specific attitude (PSA), willingness to pay (WTP) and perceived availability (PA) had a significant positive influence on individuals’ organic food consumption (OFC), while environmental attitude (EA) and subjective norms (SN) were not significantly related. The moderating role of future orientation (FO) between PSA, EA, WTP and OFC were examined and found to be significant except for EA. The result suggests that PSA and WTP are stronger and higher respectively when future orientation is high. The research provides a significant insight and better understanding of organic food actual consumption behavior and adds a new momentum to the growing literature. Discussions and implications of these findings are further discussed
Document categorizer agent for computer science academic papers
No full textThis paper presents Document Categorizer Agent
that categorizes computer science academic papers in .pdf format such as journals and proceedings.In this paper, we propose the use of set of term stored in a database to categorize computer science papers.
Few methods and algorithms from related work are considered in improving the categorization process.We have evaluated our document categorizer agent on a number of computer science papers.The categorization process is done by parsing the document, calculating the frequency of each term and matching the terms found with the dataset found in the database.We have shown that the use of this term database can be used to categorize documents.The categorizer agent focuses on categorizing the text document into predetermined category based on the extracted keyword. This can help in making the searching process more efficient and saves the user’s time in searching for the desired document
Document recommender agent based on hybrid approach
No full textAs Internet continues to grow, user tends to rely heavily on search engines. However, these search engines tend to generate a huge number of search results and potentially making it difficult for users to find the most relevant sites. This has resulted in search engines losing their usefulness. These users might be academicians who are searching for relevant academic papers within their interests. The need for a system that can assist in choosing the most relevant papers among the long list of results presented by search engines becomes crucial. In this paper, we propose Document Recommender Agent, that can recommend the most relevant papers based on the academician’s interest. This recommender agent adopts a hybrid recommendation approach. In this paper we also show that recommendation based on the proposed hybrid approach is better that the content-based and the collaborative approaches
