Discovery of User Preference through Genetic Algorithm and Bayesian Categorization for Recommendation

Ko, Su-Jeong; Lee, Jung-Hyun

doi:10.1007/3-540-46140-x_36

Cited by 3 publications

(2 citation statements)

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“…The contribution of genetic algorithms towards information filtering applications has been studied in various scenarios like the application of GA K-means [27] to an online shopping market segmentation proved that GA K-means clustering performed better than K-means clustering and self-organizing maps (SOM). In another application Genetic algorithm [28] has been used to group users based on products categorized by Naive Bayes classifier. Consequently recommendations were made to the user on the basis of grouped user preferences and information of categorized items.…”

Section: Introductionmentioning

confidence: 99%

A Multi -Perspective Evaluation of MA and GA for Collaborative Filtering Recommender System

Banati¹,

Mehta²

2010

IJCSIT

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Section: Introductionmentioning

confidence: 99%

A Multi -Perspective Evaluation of MA and GA for Collaborative Filtering Recommender System

Banati¹,

Mehta²

2010

IJCSIT

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“…Several algorithms that combine the knowledge from Artificial Intelligence (AI) (Mobasher et al 2004), Network (Chien et al, 1999), and other fields have also been implemented in the recommendation systems. Genetic algorithm along with Naïve Bayes Classifier is to define the relationships among users and items (Ko et al, 2001). Genetic algorithm first completes clustering for discovering relationships among system users in order to find the global optimum.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Filtering for Information Recommendation Systems

Chen

McLeod

2006

Encyclopedia of E-Commerce, E-Government, and Mobile Commerce

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In order to draw users’ attention and to increase their satisfaction toward online information search results, search-engine developers and vendors try to predict user preferences based on users’ behavior. Recommendations are provided by the search engines or online vendors to the users. Recommendation systems are implemented on commercial and nonprofit Web sites to predict user preferences. For commercial Web sites, accurate predictions may result in higher selling rates. The main functions of recommendation systems include analyzing user data and extracting useful information for further predictions. Recommendation systems are designed to allow users to locate preferable items quickly and to avoid possible information overload. Recommendation systems apply data-mining techniques to determine the similarity among thousands or even millions of data. Collaborative-filtering techniques have been successful in enabling the prediction of user preferences in recommendation systems (Hill, Stead, Rosenstein, & Furnas, 1995, Shardanand & Maes, 1995). There are three major processes in recommendation systems: object data collections and representations, similarity decisions, and recommendation computations. Collaborative filtering aims at finding the relationships among new individual data and existing data in order to further determine their similarity and provide recommendations. How to define the similarity is an important issue. How similar should two objects be in order to finalize the preference prediction? Similarity decisions are concluded differently by collaborative-filtering techniques. For example, people that like and dislike movies in the same categories would be considered as the ones with similar behavior (Chee, Han, & Wang, 2001). The concept of the nearest-neighbor algorithm has been included in the implementation of recommendation systems (Resnick, Iacovou, Suchak, Bergstrom, & Riedl, 1994). The designs of pioneer recommendation systems focus on entertainment fields (Dahlen, Konstan, Herlocker, Good, Borchers, & Riedl, 1998; Resnick et al.; Shardanand & Maes; Hill et al.). The challenge of conventional collaborative-filtering algorithms is the scalability issue (Sarwar, Karypis, Konstan, & Riedl, 2000a). Conventional algorithms explore the relationships among system users in large data sets. User data are dynamic, which means the data vary within a short time period. Current users may change their behavior patterns, and new users may enter the system at any moment. Millions of user data, which are called neighbors, are to be examined in real time in order to provide recommendations (Herlocker, Konstan, Borchers, & Riedl, 1999). Searching among millions of neighbors is a time-consuming process. To solve this, item-based collaborative-filtering algorithms are proposed to enable reductions of computations because properties of items are relatively static (Sarwar, Karypis, Konstan, & Riedl, 2001). Suggest is a top-N recommendation engine implemented with item-based recommendation algorithms (Deshpande & Karypis, 2004; Karypis, 2000). Meanwhile, the amount of items is usually less than the number of users. In early 2004, Amazon Investor Relations (2004) stated that the Amazon.com apparel and accessories store provided about 150,000 items but had more than 1 million customer accounts that had ordered from this store. Amazon.com employs an item-based algorithm for collaborative-filtering-based recommendations (Linden, Smith, & York, 2003) to avoid the disadvantages of conventional collaborative-filtering algorithms.

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