An Interest Feature Spatial Approach for Personalized Recommendation

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.

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

2010 ◽  
Vol 21 (10) ◽  
pp. 1217-1227 ◽  
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.

Personalized Knowledge Recommendation Based on Knowledge Graph in Petroleum Exploration and Development

2020 ◽  
Vol 34 (10) ◽  
pp. 2059033
Author(s):  
Gang Huang ◽  
Man Yuan ◽  
Chun-Sheng Li ◽  
Yong-he Wei
Keyword(s):  
Collaborative Filtering ◽  
Domain Knowledge ◽  
Cold Start ◽  
Petroleum Exploration ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Exploration And Development

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

scholarly journals HOTELS RECOMMENDATION METHODS BASED ON PATTERN RECOGNITION AND FACTORIZATION MACHINES

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.

A Purchase Prediction Based on Collaborative Filtering Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2241-2244
Author(s):  
Zheng Fu ◽  
Lan Feng Zhou
Keyword(s):  
Correlation Analysis ◽  
Collaborative Filtering ◽  
Experimental Result ◽  
Behavior Model ◽  
Apriori Algorithm ◽  
Buying Behavior ◽  
History Data ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Better Than

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.

A Collaborative Filtering Recommendation Algorithm Combined with User and Item

2014 ◽  
Vol 513-517 ◽  
pp. 1878-1881
Author(s):  
Feng Ming Liu ◽  
Hai Xia Li ◽  
Peng Dong
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
User Evaluation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

The collaborative filtering recommendation algorithm based on user is becoming the more personalized recommendation algorithm. But when the user evaluation for goods is very small and the user didnt evaluate the item, the commodity recommendation based on the item evaluation of user may not be accurate, and this is the sparseness in the collaborative filtering algorithm based on user. In order to solve this problem, this paper presents a collaborative filtering recommendation algorithm based on user and item. The experimental results show that this method has smaller MAE and greatly improve the quality of the recommendation in the recommendation system.

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