Personalized Recommendation Technique Research Articles

A Personalized Recommendation Technique for Travel Route Based on Fuzzy Consistent Matrix

Recommending travel routes for tourists has become a research hotspot in the present tourism industry. Various machine learning methods have been well studied for recommendation systems. However, the accuracy of these systems is not up to the expected levels. In order to increase the ranking accuracy, as well as, the recall rate of the recommended routes by the personalized travel recommendation algorithm, in this paper, a fuzzy consistent matrix technique is introduced for personalized travel recommendation systems. First of all, the user’s travel interest data are obtained through the fuzzy consistency matrix. Next, the user’s travel interest data are de-noised to determine the user’s preferred interest points and the coverage of the user’s interest points. Besides this, the constraints of the route recommendation systems have also become a research topic. Therefore, the proposed method also uses the fuzzy consistent matrix technology to determine the users’ similarity and classifies the personalized recommendation algorithm. Finally, a personalized travel recommendation model is constructed to comprehend the personalized travel route recommendation. The investigational outcomes, using real datasets, illustrate that when the number of routes is 5000, then the accuracy rate of the proposed method is approximately 97.21%, and the accuracy rate of the recommendation ranking is 97.54%. We also observed that when using 60 GB of data, the recall rate of the proposed method, for personalized travel recommendation routes, can reach as high as 97.59%.

Time-Aware CF and Temporal Association Rule-Based Personalized Hybrid Recommender System

Most recommender systems usually combine several recommendation methods to enhance the recommendation accuracy. Collaborative filtering (CF) is a best-known personalized recommendation technique. While temporal association rule-based recommendation algorithm can discover users' latent interests with time-specific leveraging historical behavior data without domain knowledge. The concept-drifting and user interest-drifting are two key problems affecting the recommendation performance. Aiming at the above problems, a time-aware CF and temporal association rule-based personalized hybrid recommender system, TP-HR, is proposed. The proposed time-aware CF algorithm considers evolving features of users' historical feedback. And time-aware users' similar neighbors selecting measure and time-aware item rating prediction function are proposed to keep track of the dynamics of users' preferences. The proposed temporal association rule-based recommendation algorithm considers the time context of users' historical behaviors when mining effective temporal association rules. Experimental results on real datasets show the feasibility and performance improvement of the proposed hybrid recommender system compared to other baseline approaches.

An Electronic Commerce Collaborative Filtering Recommedation Algorithm Based on User Context

With the speedy development of Internet, information technology has provided an unmatched amount of information resources. To help people to find helpful information, electronic commerce personalized recommendation technique emerges. Collaborative filtering is one successful personalized recommendation technology, and is widely used in many fields. But traditional collaborative filtering recommendation algorithm has the problem of sparsity, which will influence the efficiency of prediction. User context information is rarely considered in the recommendation process, especially in the collaborative filtering. In this paper, a new electronic commerce collaborative filtering recommendation algorithm is given which applies the user context information. This method combines the rating similarity and the user context similarity in the electronic commerce recommendation process to improve the prediction accuracy by efficiently managing the problem of data sparsity.

An Ontology-Based Collaborative Filtering Personalized Recommendation

Recommender systems have been successfully used to tackle the problem of information overload, where users of products have too many choices and overwhelming amount of information about each choice. Personalization is widely used in various fields to provide users with more suitable and personalized service. Many e-commerce web sites such as online shop retailers make use of recommendation systems. In order to make recommendations to a user, collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. The collaborative approach faces the hard issue of cold start problem and the matrix sparsity problem. The paper presents a collaborative filtering personalized recommendation approach based on ontology in the special domain. The method combines ontology technology and item-based collaborative filtering. The given recommendation approach can tackle the traditional recommenders problems, such as matrix sparsity and cold start problems.

효과적인 이메일 분류를 위한 빈발 항목집합 기반 최적 이메일 폴더 추천 기법

이메일이 중요한 정보 전달과 의사소통의 수단으로 널리 활용된 이래 사람들은 이메일을 내용에 따라 적절하게 분류하는 작업에 많은 노력을 기울려 왔다. 이메일은 문서의 길이나 문체가 다양하며 사용되는 단어들이 비정규적이다. 또한 이메일 분류 기준은 일반적으로 해당 이메일 사용자의 주관에 따라 정의된다. 따라서 기존의 일반적인 문서분류 기법으로는 이메일을 효율적으로 분류하는데 어려움이 있다. 상업용 이메일 프로그램에서 제공되는 분류 기능은 메일 클라이언트에서 지원하는 텍스트 필터링을 이용한다. 한편 이메일의 자동 분류에 관한 연구는 확률 기반의 나이브 베이지안 기법을 응용하여 정확도를 높일 수 있는 연구가 주로 진행되어 왔으며, 대부분 영문 이메일에 대한 연구이다. 본 논문에서는 빈발 패턴 마이닝 기법을 적용하여 한글 이메일에 대한 개인 맞춤형 폴더 추천기법을 제시한다. 이메일의 맞춤형 폴더 추천 기법은 이메일에 대한 전처리 과정과 빈발 항목집합을 이용한 메일 폴더의 프로파일 생성과정으로 구성된다. 생성된 프로파일은 분류 대상이 되는 각 메일이 개인별 맞춤형 기준에 따라 가장 적합한 이메일 폴더로 효과적으로 분류되는데 활용된다. 또한 제안된 기법을 적용한 이메일 분류 시스템을 구현한다. Since an e-mail has been an important mean of communication and information sharing, there have been much effort to classify e-mails efficiently by their contents. An e-mail has various forms in length and style, and words used in an e-mail are usually irregular. In addition, the criteria of an e-mail classification are subjective. As a result, it is quite difficult for the conventional text classification technique to be adapted to an e-mail classification efficiently. An e-mail classification technique in a commercial e-mail program uses a simple text filtering technique in an e-mail client. In the previous studies on automatic classification of an e-mail, the Naive Bayesian technique based on the probability has been used to improve the classification accuracy, and most of them are on an e-mail in English. This paper proposes the personalized recommendation technique of an email in Korean using a data mining technique of frequent patterns. The proposed technique consists of two phases such as the pre-processing of e-mails in an e-mail folder and the generating a profile for the e-mail folder. The generated profile is used for an e-mail to be classified into the most appropriate e-mail folder by the subjective criteria. The e-mail classification system is also implemented, which adapts the proposed technique.

A New Personalized Recommendation Technique Based on the Modified TOPSIS Method

Personalized recommendation service helping users to target the interesting information from the excessive information set has been widely concerned. In this paper, we firstly propose a new method named Modified TOPSIS Method utilizing the Improved Gray Correlation Analysis Method. Then, we present a new personalized recommendation technique based on the Modified TOPSIS Method. Finally, the verification method utilizing Spearman’s Rank Correlation Coefficient demonstrates that our new personalized recommendation technique is efficient.