A Purchase Prediction Based on Collaborative Filtering Algorithm

For a more accurate prediction of the probability of consumers to purchase a commodity, this paper build a users’ behavior model based on correlation analysis with apriori algorithm. The model is built by learning from users’ history data and behaviors’ at present, an experimental result demonstrates that this model can effectively predict consumer buying behavior, and it is better than some traditional methods.

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CAN DISSIMILAR USERS CONTRIBUTE TO ACCURACY AND DIVERSITY OF PERSONALIZED RECOMMENDATION?

International Journal of Modern Physics C ◽

10.1142/s0129183110015786 ◽

2010 ◽

Vol 21 (10) ◽

pp. 1217-1227 ◽

Cited By ~ 24

Author(s):

WEI ZENG ◽

MING-SHENG SHANG ◽

QIAN-MING ZHANG ◽

LINYUAN LÜ ◽

TAO ZHOU

Keyword(s):

Collaborative Filtering ◽

Recommender System ◽

High Accuracy ◽

Personalized Recommendation ◽

Recommendation Algorithm ◽

Filtering Algorithm ◽

Recommendation Algorithms ◽

Collaborative Filtering Algorithm ◽

High Diversity ◽

Better Than

Recommender systems are becoming a popular and important set of personalization techniques that assist individual users with navigating through the rapidly growing amount of information. A good recommender system should be able to not only find out the objects preferred by users, but also help users in discovering their personalized tastes. The former corresponds to high accuracy of the recommendation, while the latter to high diversity. A big challenge is to design an algorithm that provides both highly accurate and diverse recommendation. Traditional recommendation algorithms only take into account the contributions of similar users, thus, they tend to recommend popular items for users ignoring the diversity of recommendations. In this paper, we propose a recommendation algorithm by considering both the effects of similar and dissimilar users under the framework of collaborative filtering. Extensive analyses on three datasets, namely MovieLens, Netflix and Amazon, show that our method performs much better than the standard collaborative filtering algorithm for both accuracy and diversity.

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HOTELS RECOMMENDATION METHODS BASED ON PATTERN RECOGNITION AND FACTORIZATION MACHINES

Latin American Applied Research - An international journal ◽

10.52292/j.laar.2018.221 ◽

2018 ◽

Vol 48 (3) ◽

pp. 169-174

Author(s):

Y. CHEN ◽

D. YAO

Keyword(s):

Pattern Recognition ◽

Collaborative Filtering ◽

Formal Methods ◽

Geographical Location ◽

Cost Performance ◽

Filtering Algorithm ◽

Data Sparseness ◽

Tourist Satisfaction ◽

Collaborative Filtering Algorithm ◽

Better Than

The traditional recommendation methods for hotels usually compute rating similarity and make recommendation based on collaborative filtering algorithm. It is due to having no consideration of the tourist’ and hotels’ multi-faceted attributes. Thus, the accuracy of recommendation would be affected. To solve this problem, a series of formal methods are adopted to define the various attributes of hotels and tourists. To begin with it, get hotel star factor, hotel hardware facilities, cost performance, geographical location and the characters of the preference of the star of the tourists, and after that a partial weighting model is used to compute a recommended label value. Finally, the factorization machines (FMs) is used to make recommendations. The experimental results show that the proposed methods can solve data sparseness problem to some extent. Additionally, both its recommendation and ranking accuracy are better than those of the traditional collaborative filtering algorithm, which can improve the tourist satisfaction in personalized hotels recommendation.

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An Interest Feature Spatial Approach for Personalized Recommendation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.58-60.2219 ◽

2011 ◽

Vol 58-60 ◽

pp. 2219-2224

Author(s):

Yin Tian Liu ◽

Hai Qing Zhang ◽

Hai Fei Xu ◽

Ying Ming Liu

Keyword(s):

Collaborative Filtering ◽

Personalized Recommendation ◽

Web Pages ◽

Network Resources ◽

Filtering Algorithm ◽

Spatial Approach ◽

Collaborative Filtering Algorithm ◽

Feature Values ◽

Better Than

To expand user's actions of personalized recommendation, this paper introduces an Interest Feature Spatial based Recommendation Model. This model combines both collection behavior data of network users and content data of web pages located by URL address. The main content includes: (1) Proposing the construction of interest feature spatial based on SHG-Tree; (2) Proposing the formula to calculate interest feature values of network resources; (3) Proposing four interest match algorithms along with six types of personalized recommendation schemes. Experiments show that the recommendation service can achieve millisecond responding, the precision, especially recall metric is better than item-based collaborative filtering algorithm.

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