A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce

Yu, Li; Liu, Lu; Li, Xuefeng

doi:10.1016/j.eswa.2004.08.013

Cited by 185 publications

(63 citation statements)

References 14 publications

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“…Li et al [7] proposed a hybrid CF to diversify recommended items based on related keywords; for instance, a user may claim his/her interest in both 'Football' and 'English', but s/he may have only provided ratings on 'Football'-related items. Hence, the RS can help him/her by finding relevant items in English football, for example.…”

Section: Related Workmentioning

confidence: 99%

Geographical Information in a Multi-domain Recommender System

Tang

Winoto

2014

Web-Age Information Management

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Abstract. Multi-domain recommendation is more challenging than that in the traditional single-domain one. In our previous study on the cross-domain recommendation, we have uncovered the tradeoff between the accuracy and the coverage of the recommendation. Later, we have also reported our findings on uncovering the association between user's interests of items across domains that are related to each other to a certain degree using another dataset collected from users with different demographic information. In this paper, we further discuss the comparison between our previous two experimental results and some practical implications in the design of recommendation systems.

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Section: Related Workmentioning

confidence: 99%

Geographical Information in a Multi-domain Recommender System

Tang

Winoto

2014

Web-Age Information Management

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“…Li et al [16] introduce a method that uses collaborative filtering approaches in e-commerce based on both users and items alike. They also show that collaborative filtering based on users is not successfully adaptive to datasets of users with different interests.…”

Section: Related Workmentioning

confidence: 99%

AMORE: design and implementation of a commercial-strength parallel hybrid movie recommendation engine

Christou

Amolochitis

2015

Knowl Inf Syst

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AMORE is a hybrid recommendation system that provides movie recommendation functionality to video-on-demand subscribers of a major triple-play service provider in Greece. Without any user relevance feedback for movies available, all recommendations are solely based on the users' viewing history. To overcome such limitations as well as the extra problem of user histories that are usually the merger of the preferences of all persons in each household, we have performed extensive experiments with open-source recommendation software such as Apache Mahout and Lens-Kit, as well as with our own implementations of several user-based, item-based, and content-based recommendation algorithms. Our results indicate that our own custom multi-threaded implementation of collaborative filtering combined with a custom content-based algorithm outperforms current state-of-the-art implementations of similar algorithms both in solution quality and in response time by margins exceeding 100 % in terms of recall quality and 6300 % in terms of running time. The hybrid nature of the ensemble allows the system to perform well and to overcome inherent limitations of collaborative filtering, such as various cold-start problems. AMORE has been deployed in a production environment where it has contributed to an increase in the provider's rental profits, while at the same time offers customer retention support.

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“…This equation is equivalent to: (8) This equation means the probability of the value of the portfolio lower than is 1-c, assume that the value of portfolio W follows normal distribution, is quantile of standard normal distribution, then: (9) represents standard normal distribution. VaR method is not only a method to measure the risk level of securities, but also can measure the risk preference of investors.…”

Section: Var Methodsmentioning

confidence: 99%

“…Hybrid filtering uses a combination of CF with demographic filtering or CF with content-based filtering [9]. Hybrid filtering is usually based on bioinspired or probabilistic methods such as genetic algorithms and fuzzy genetic, neural networks, Bayesian networks, clustering, and latent features (such as SVD) [10].…”

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confidence: 99%

An Investment Portfolio Recommendation System for Individual E-Commerce Users

Li¹,

Yu²

2018

dtetr

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Choosing appropriate portfolio which can maximize the revenue in a bearable risk level is the most crucial decision for investors. Traditionally, this kind of decision requires a great deal of efforts and time, and usually made by financial professionals. It's difficult for people without professional knowledge to choose appropriate investment portfolio by themselves. The objective of this paper is to develop a recommendation system which can recommend specific investment plan for different risk preference Internet investors. In the proposed recommendation system, the VaR method is used to measure the risk level of securities and risk preference of investors. In addition, a collaborative filtering algorithm is adopted to recommend portfolio that satisfy risk preference and requirements of investors, by considering the investor's history behavior and the history behavior of nearest neighbor investors with similar risk preference. Finally, experiments are conducted to demonstrate the feasibility of the proposed recommendation system.

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A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-Commerce

Cited by 185 publications

References 14 publications

Geographical Information in a Multi-domain Recommender System

Geographical Information in a Multi-domain Recommender System

AMORE: design and implementation of a commercial-strength parallel hybrid movie recommendation engine

An Investment Portfolio Recommendation System for Individual E-Commerce Users

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