Web personalization expert with combining collaborative filtering and association rule mining technique

Lee, Chi-Hoon; Kim, Y.-H.; Rhee, Phill Kyu

doi:10.1016/s0957-4174(01)00034-3

Cited by 115 publications

(45 citation statements)

References 5 publications

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“…The rating inference needs to consider both an individual's preferences, the opinions given by the other users, especially the local experts [2,13], and the similarity between them. This inference demands three aspects of computing: a) estimating the expertise of a user, b) computing the similarity between users, and c) collaborative social opinion inference for a location incorporating the results of the former two computation, e.g., using collaborative filtering (CF) model [8,12].…”

Section: Introductionmentioning

confidence: 99%

Location-based and preference-aware recommendation using sparse geo-social networking data

Bao

Zheng

Mokbel

2012

Proceedings of the 20th International Conference on Advances in Geographic Information Systems

591

374

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The popularity of location-based social networks provide us with a new platform to understand users' preferences based on their location histories. In this paper, we present a location-based and preference-aware recommender system that offers a particular user a set of venues (such as restaurants) within a geospatial range with the consideration of both: 1) User preferences, which are automatically learned from her location history and 2) Social opinions, which are mined from the location histories of the local experts. This recommender system can facilitate people's travel not only near their living areas but also to a city that is new to them. As a user can only visit a limited number of locations, the userlocations matrix is very sparse, leading to a big challenge to traditional collaborative filtering-based location recommender systems. The problem becomes even more challenging when people travel to a new city. To this end, we propose a novel location recommender system, which consists of two main parts: offline modeling and online recommendation. The offline modeling part models each individual's personal preferences with a weighted category hierarchy (WCH) and infers the expertise of each user in a city with respect to different category of locations according to their location histories using an iterative learning model. The online recommendation part selects candidate local experts in a geospatial range that matches the user's preferences using a preference-aware candidate selection algorithm and then infers a score of the candidate locations based on the opinions of the selected local experts. Finally, the top-k ranked locations are returned as the recommendations for the user. We evaluated our system with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than baselines, while having a good efficiency of providing location recommendations.

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Section: Introductionmentioning

confidence: 99%

Location-based and preference-aware recommendation using sparse geo-social networking data

Bao

Zheng

Mokbel

2012

Proceedings of the 20th International Conference on Advances in Geographic Information Systems

591

374

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“…Recommender systems can be classified depending on the type of method used for making recommendations (Cheung, Kwok, Law, & Tsui, 2003;Lee, Kim, & Rhee 2001). The two main categories are content-based methods and collaborative filtering algorithms.…”

Section: Related Workmentioning

confidence: 99%

A hybrid recommendation approach for a tourism system

Lucas

Luz

Moreno

et al. 2013

Expert Systems with Applications

156

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A B S T R A C TMany current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulner-able to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.K e y w o r d s : Recommender systems, Associative classification, Fuzzy logic

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“…In [6] Joel P. Lucas , Nuno Luz , María N. Moreno Ricardo Anacleto , Ana Almeida Figueiredo , Constantino Martins proposed a recommendation method by using the hybrid approach of collaborative filtering and content-based filtering .The data mining techniques which is used is classification based on association is applied. It is also known as associative classification method which can combine the concept of classification and association [7] They had shown comparison method which had improved the quality of recommendation instead of using only eitherassociation or classification.…”

Section: Literature Surveymentioning

confidence: 99%

A Modified Approach towards Tourism Recommendation System with Collaborative Filtering and Association Rule Mining

Gandhi¹,

Mistry²,

Patel³

2014

IJCA

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In the tourism recommendation system, the number of users and items is very large. But traditional recommendation system uses partial information for identifying similar characteristics of users. Collaborative filtering is the primary approach of any recommendation system. It provides a recommendation which is easy to understand. It is based on similarities of user opinions like rating or likes and dislikes. So the recommendation provided by collaborative cannot be considered as quality recommendation. Recommendation after association rule mining is having high support and confidence level. So that will be considered as strong recommendation. The hybridization of both collaborative filtering and association rule mining can produce strong and quality recommendation even when sufficient data are not available. This paper combines recommendation for tourism application by using a hybridization of traditional collaborative filtering technique and data mining techniques.

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Web personalization expert with combining collaborative filtering and association rule mining technique

Cited by 115 publications

References 5 publications

Location-based and preference-aware recommendation using sparse geo-social networking data

Location-based and preference-aware recommendation using sparse geo-social networking data

A hybrid recommendation approach for a tourism system

A Modified Approach towards Tourism Recommendation System with Collaborative Filtering and Association Rule Mining

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