• Title/Summary/Keyword: Hybrid recommender system

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Scalable Hybrid Recommender System with Temporal Information (시간 정보를 이용한 확장성 있는 하이브리드 Recommender 시스템)

  • Ullah, Farman;Sarwar, Ghulam;Kim, Jae-Woo;Moon, Kyeong-Deok;Kim, Jin-Tae;Lee, Sung-Chang
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.12 no.2
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    • pp.61-68
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    • 2012
  • Recommender Systems have gained much popularity among researchers and is applied in a number of applications. The exponential growth of users and products poses some key challenges for recommender systems. Recommender Systems mostly suffer from scalability and accuracy. The accuracy of Recommender system is somehow inversely proportional to its scalability. In this paper we proposed a Context Aware Hybrid Recommender System using matrix reduction for Hybrid model and clustering technique for predication of item features. In our approach we used user item-feature rating, User Demographic information and context information i.e. specific time and day to improve scalability and accuracy. Our Algorithm produce better results because we reduce the dimension of items features matrix by using different reduction techniques and use user demographic information, construct context aware hybrid user model, cluster the similar user offline, find the nearest neighbors, predict the item features and recommend the Top N- items.

Development of Web-based Intelligent Recommender Systems using Advanced Data Mining Techniques (개선된 데이터 마이닝 기술에 의한 웹 기반 지능형 추천시스템 구축)

  • Kim Kyoung-Jae;Ahn Hyunchul
    • Journal of Information Technology Applications and Management
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    • v.12 no.3
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    • pp.41-56
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    • 2005
  • Product recommender system is one of the most popular techniques for customer relationship management. In addition, collaborative filtering (CF) has been known to be one of the most successful recommendation techniques in product recommender systems. However, CF has some limitations such as sparsity and scalability problems. This study proposes hybrid cluster analysis and case-based reasoning (CBR) to address these problems. CBR may relieve the sparsity problem because it recommends products using customer profile and transaction data, but it may still give rise to scalability problem. Thus, this study uses cluster analysis to reduce search space prior to CBR for scalability Problem. For cluster analysis, this study employs hybrid genetic and K-Means algorithms to avoid possibility of convergence in local minima of typical cluster analyses. This study also develops a Web-based prototype system to test the superiority of the proposed model.

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Comparison of deep learning-based autoencoders for recommender systems (오토인코더를 이용한 딥러닝 기반 추천시스템 모형의 비교 연구)

  • Lee, Hyo Jin;Jung, Yoonsuh
    • The Korean Journal of Applied Statistics
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    • v.34 no.3
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    • pp.329-345
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    • 2021
  • Recommender systems use data from customers to suggest personalized products. The recommender systems can be categorized into three cases; collaborative filtering, contents-based filtering, and hybrid recommender system that combines the first two filtering methods. In this work, we introduce and compare deep learning-based recommender system using autoencoder. Autoencoder is an unsupervised deep learning that can effective solve the problem of sparsity in the data matrix. Five versions of autoencoder-based deep learning models are compared via three real data sets. The first three methods are collaborative filtering and the others are hybrid methods. The data sets are composed of customers' ratings having integer values from one to five. The three data sets are sparse data matrix with many zeroes due to non-responses.

Recommender system using BERT sentiment analysis (BERT 기반 감성분석을 이용한 추천시스템)

  • Park, Ho-yeon;Kim, Kyoung-jae
    • Journal of Intelligence and Information Systems
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    • v.27 no.2
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    • pp.1-15
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    • 2021
  • If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

Reinforcement Learning Algorithm Based Hybrid Filtering Image Recommender System (강화 학습 알고리즘을 통한 하이브리드 필터링 이미지 추천 시스템)

  • Shen, Yan;Shin, Hak-Chul;Kim, Dae-Gi;Hong, Yo-Hoon;Rhee, Phill-Kyu
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.12 no.3
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    • pp.75-81
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    • 2012
  • With the advance of internet technology and fast growing of data volume, it become very hard to find a demanding information from the huge amount of data. Recommender system can solve the delema by helping a user to find required information. This paper proposes a reinforcement learning based hybrid recommendation system to predict user's preference. The hybrid recommendation system combines the content based filtering and collaborate filtering, and the system was tested using 2000 images. We used mean abstract error(MAE) to compare the performance of the collaborative filtering, the content based filtering, the naive hybrid filtering, and the reinforcement learning algorithm based hybrid filtering methods. The experiment result shows that the performance of the proposed hybrid filtering performance based on reinforcement learning is superior to other methods.

Tourism Destination Recommender System for the Cold Start Problem

  • Zheng, Xiaoyao;Luo, Yonglong;Xu, Zhiyun;Yu, Qingying;Lu, Lin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.7
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    • pp.3192-3212
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    • 2016
  • With the advent and popularity of e-commerce, an increasing number of consumers prefer to order tourism products online. A recommender system can help these users contend with information overload; however, such a system is affected by the cold start problem. Online tourism destination searching is a more difficult task than others on account of its more restrictive factors. In this paper, we therefore propose a tourism destination recommender system that employs opinion-mining technology to refine user preferences and item opinion reputations. These elements are then fused into a hybrid collaborative filtering method by combining user- and item-based collaborative filtering approaches. Meanwhile, we embed an artificial interactive module in our recommender system to alleviate the cold start problem. Compared with several well-known cold start recommendation approaches, our method provides improved recommendation accuracy and quality. A series of experimental evaluations using a publicly available dataset demonstrate that the proposed recommender system outperforms existing recommender systems in addressing the cold start problem.

Strategies for Selecting Initial Item Lists in Collaborative Filtering Recommender Systems

  • Lee, Hong-Joo;Kim, Jong-Woo;Park, Sung-Joo
    • Management Science and Financial Engineering
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    • v.11 no.3
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    • pp.137-153
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    • 2005
  • Collaborative filtering-based recommendation systems make personalized recommendations based on users' ratings on products. Recommender systems must collect sufficient rating information from users to provide relevant recommendations because less user rating information results in poorer performance of recommender systems. To learn about new users, recommendation systems must first present users with an initial item list. In this study, we designed and analyzed seven selection strategies including the popularity, favorite, clustering, genre, and entropy methods. We investigated how these strategies performed using MovieLens, a public dataset. While the favorite and popularity methods tended to produce the highest average score and greatest average number of ratings, respectively, a hybrid of both favorite and popularity methods or a hybrid of demographic, favorite, and popularity methods also performed within acceptable ranges for both rating scores and numbers of ratings.

A Hybrid Collaborative Filtering-based Product Recommender System using Search Keywords (검색 키워드를 활용한 하이브리드 협업필터링 기반 상품 추천 시스템)

  • Lee, Yunju;Won, Haram;Shim, Jaeseung;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.26 no.1
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    • pp.151-166
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    • 2020
  • A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer's search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.

A Consumer Perception based on the Type of Recommender System : A Privacy Calculus Perspective (상품 추천 서비스 유형에 따른 소비자 반응 연구 : 프라이버시 계산 모델을 중심으로)

  • Choi, Hye-Jin;Cho, Chang-Hoan
    • The Journal of the Korea Contents Association
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    • v.20 no.3
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    • pp.254-266
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    • 2020
  • The purpose of this study is to analyze the influence of the type of recommender system on consumer's perceived benefit and privacy risk. The result showed that the perceived usefulness and intension to click was high in the order of Hybrid-filtering, Bestseller, and SNS-based system. Privacy concern was high in order of SNS-based system, Hybrid-filtering, and Bestseller. Moderating effects of perceived personalization on the type of recommender system and perceived usefulness were significant. Finally perceived usefulness had positive effect, and privacy concern had negative effect on consumer's intension to click. This study has significant implications for digital marketing bt comparing consumer responses according to the type of recommended service. The result of this study can be helpful for providing and developing future recommender service.

Clustering-based Hybrid Filtering Algorithm

  • Qing Li;Kim, Byeong-Man;Shin, Yoon-Sik;Lim, En-Ki
    • Proceedings of the Korean Information Science Society Conference
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    • 2003.10a
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    • pp.10-12
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    • 2003
  • Recommender systems help consumers to find the useful products from the overloaded information. Researchers have developed content-based recommenders, collaborative recommenders, and a few hybrid systems. In this research, we extend the classic collaborative recommenders by clustering method to form a hybrid recommender system. Using the clustering method, we can recommend the products based on not only the user ratings but also other useful information from user profiles or attributes of items. Through our experiments on well-known MovieLens data set, we found that the information provided by the attributes of item on the item-based collaborative filter shows advantage over the information provided by user profiles on the user-based collaborative filter.

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