Data Mining and Recommender System: A Review (original) (raw)
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APPLICATION OF DATA MINING TO RECOMMENDER SYSTEMS
17th International CIRCLE Conference 2021, 2021
In the last few decades, globalization, hyper-production of all kinds of goods, and mostof all, global IT and online sales have brought new opportunities for customers. Storesare no longer limited by the size of their sales space, storefronts, and shelves. Shelvesin physical stores are now represented by menu listings and links in virtual stores thatcan be "indefinitely" large. Making the right decision based on the information we getfrom a few friends or acquaintances is no longer possible. Now, much more usefulinformation is needed, i.e., other people's opinions and experiences (which we don'tknow) to make the right decision. The development of recommendation systems hascaught the attention of many experts from a variety of disciplines, including data mining,statistical and mathematical methods, machine learning, and human-computerinteraction research. The recommendation system is a specialized part of filtering (datamining), based on finding, things, or objects that might be useful or interesting to theuser. This paper will outline the principles of recommender systems and provide simpleexamples that illustrate specific problems. Through the appliance of classificationmethods, it was shown which recommendation systems exist and on what basis theydiffer. Methods used by these systems are described, but no specific programmingalgorithms were introduced. The "black box" method was used, which is typical for the analysis of complex systems.
2005
The huge amount of information on the Internet creates a problem for the users -information overload. For this reason, finding the worthwhile information is becoming a challenge. To aid users a new approach based on Recommender System. This type of system applies information filtering in order to recommend items to a user based on the user's profile and historical consumption. Recommender Systems present some difficulties: (i) user overspecialization and (ii) the new user problem. The main contribution of this paper is the description of a Framework to discover new knowledge based on a data mining technique and user's relevance opinion. This knowledge is represented as a set of rules in a knowledge base, which has been used to address the difficulties cited before and to help in the information filtering process. This paper reports on work, which is part of the W-RECMAS (a Recommender System to Web based on Multi-Agent System for academic paper recommendation) project.
Recommendation System using Data Mining a Review
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023
Recommendation systems are widely used for suggesting products, social media content, and web content to users. These systems utilize information filtering techniques to predict the preferences or ratings that a user would give to a particular item. This paper presents an overview of various data filtering techniques, algorithms, and application areas utilized in recommendation systems. It also includes a comparison between different algorithms used for recommendation systems.
Survey Paper on Recommendation System using Data Mining Techniques
— The aim of proposed systems (also called as collaborative filtering systems) is to suggest items which a client is expected to order. In this paper we describe the recommendation system related research and then Introduces various techniques and approaches used by the recommender system User-based approach, Item based approach, Hybrid recommendation approaches and related research in the recommender system. Normally, recommended systems are used online to propose items that users discover interesting, thereby, benefiting both the user and merchant Recommender systems benefit the user by building him suggestions on things that he is probable to buy and the business by raise of sales. we also explained the challenges, issues in data mining and how to build a recommendation system to improve performance accuracy by applying the techniques.
A Review on Recommender System
International Journal of Computer Application, 2013
A statistics reveals that the number of people selling goods over the internet has increased by more than 100percent since 2006. Almost everyone depend on the internet for everything such as reading newspapers, magazines, books and for searching research papers, to buy latest models of all gadgets and also for entertainment like hearing songs, watching movies, and for food recipes. The internet has changed the way of living. The reason behind this is 73% time consuming and still finding exactly what we need from information available is a tedious. We expect someone to recommend the best from huge data that fulfill ones need, tastes, behavior, interest etc. The "Information Overload"term was first coined by Alvin Toffler in his book named "Future Shock" in 1970 which is one of major issue the internet facing today. To address this issue and provide users best recommendations a System is developed called Recommender System. Recommender System applies various Data Mining methodologies to recommend efficiently for all active users based on their interest, preferences and ratings given for previous items and even based on similar users. In this paper we also analyze various issues and evaluation metrics used to measure the performance of the Recommender System.
A data mining framework for building a web-page recommender system
Proceedings of the 2004 IEEE International Conference on Information Reuse and Integration, 2004. IRI 2004., 2004
In this paper, we propose a new framework based on data mining algorithms for building a Web-page recommender system. A recommender system is an intermediary program (or an agent) with a user interface that automatically and intelligently generates a list of information which suits an individual's needs. Two information filtering methods for providing the recommended information are considered: (1) by analyzing the information content, i.e., content-based filtering, and (2) by referencing other user access behaviors, i.e., collaborative filtering. By using the data mining algorithms, the information filtering processes can be performed prior to the actual recommending process. As a result, the system response time could be improved and thus, making the framework scalable.
Machine Learning Approach to Recommender System for Web Mining
Intelligent Data Communication Technologies and Internet of Things, 2019
One of the major challenges face by webmasters is the introduction of numerous choices to the customer, which leads to repetitive and difficulty in locating the correct item or data on the webpage. In the traditional approach, if the data was changed, pooling approach was possible, only if data variation was within the cluster information. In case the data exceeds the limit, classification was difficult to perform. Therefore, we need to have a classification approach that can work under these conditions. In the proposed work we have implemented Hybrid of ANN and KNN approach and found improvement in the recommendation system with greater accuracy.
A survey on data mining techniques in recommender systems
Soft Computing, 2017
Recommender systems have been regarded as gaining a more significant role with the emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF predicts the interests of an active user based on the opinions of users with similar interests. To extract information on the preference of users for a set of items and evaluate the performance of the recommender system's techniques and algorithms, a critical analysis can be conducted. This study therefore employs a critical analysis on 131 articles in CF area from 36 journals published between the years 2010 and 2016. This analysis seems to be the exclusive survey which supports and motivates the community of researchers and practitioners. It is done by using the applications of users' activities and intelligence computing and data mining techniques on CF recommendation systems. In addition, it provides a classification of the literature on academic database according to the benchmark recommendation databases, two users' feedbacks (explicit and implicit feedbacks) which reflect their activities and categories of intelligence computing and data mining techniques. Eventually, this study provides a road map to guide future direction on recommender systems research and facilitates the accumulated and derived knowledge on the application Communicated by V. Loia.
The Application of Data-Mining to Recommender Systems
Encyclopedia of Data Warehousing and Mining, Second Edition, 2009
In a world where the number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They connect users with items to “consume” (purchase, view, listen to, etc.) by associating the content of recommended items or the opinions of other individuals with the consuming user’s actions or opinions. Such systems have become powerful tools in domains from electronic commerce to digital libraries and knowledge management. For example, a consumer of just about any major online retailer who expresses an interest in an item – either through viewing a product description or by placing the item in his “shopping cart” – will likely receive recommendations for additional products. These products can be recommended based on the top overall sellers on a site, on the demographics of the consumer, or on an analysis of the past buying behavior of the consumer as a prediction for future buying behavior. This paper will address the technology used to generate rec...
A personalized recommender system based on web usage mining and decision tree induction
Expert Systems With Applications, 2002
A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies. q