Information Recommendation Method Research Based on Trust Network and Collaborative Filtering

2011 ◽  
Author(s):  
Yuanliang Gao ◽  
Boyi Xu ◽  
Hongming Cai
Keyword(s):  
Collaborative Filtering ◽  
Trust Network ◽  
Information Recommendation

scholarly journals A Location-Based Business Information Recommendation Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Shudong Liu ◽  
Xiangwu Meng
Keyword(s):  
Collaborative Filtering ◽  
Large Scale ◽  
Empirical Studies ◽  
Real World Data ◽  
Network Ties ◽  
Long Distance ◽  
Recommendation Algorithm ◽  
Business Information ◽  
Information Recommendation ◽  
Social Network Ties

Recently, many researches on information (e.g., POI, ADs) recommendation based on location have been done in both research and industry. In this paper, we firstly construct a region-based location graph (RLG), in which region node respectively connects with user node and business information node, and then we propose a location-based recommendation algorithm based on RLG, which can combine with user short-ranged mobility formed by daily activity and long-distance mobility formed by social network ties and sequentially can recommend local business information and long-distance business information to users. Moreover, it can combine user-based collaborative filtering with item-based collaborative filtering, and it can alleviate cold start problem which traditional recommender systems often suffer from. Empirical studies from large-scale real-world data from Yelp demonstrate that our method outperforms other methods on the aspect of recommendation accuracy.

Lazy collaborative filtering with dynamic neighborhoods

2018 ◽  
Vol 46 (2) ◽  
pp. 95-109
Author(s):  
Suganeshwari G. ◽  
Syed Ibrahim S.P. ◽  
Gang Li
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Temporal Information ◽  
High Quality ◽  
Good Efficiency ◽  
Content Type ◽  
Active User ◽  
Art Methods ◽  
Information Recommendation ◽  
State Of Art

Purpose The purpose of this paper is to address the scalability issue and produce high-quality recommendation that best matches the user’s current preference in the dynamically growing datasets in the context of memory-based collaborative filtering methods using temporal information. Design/methodology/approach The proposed method is formalized as time-dependent collaborative filtering method. For each item, a set of influential neighbors is identified by using the truncated version of similarity computation based on the timestamp. Then, recent n transactions are used to generate the recommendation that reflect the recent preference of the active user. The proposed method, lazy collaborative filtering with dynamic neighborhoods (LCFDN), is further scaled up by implementing in spark using parallel processing paradigm MapReduce. The experiments conducted on MovieLens dataset reveal that LCFDN implemented on MapReduce is more efficient and achieves good performance than the existing methods. Findings The results of the experimental study clearly show that not all ratings provide valuable information. Recommendation system based on LCFDN increases the efficiency of predictions by selecting the most influential neighbors based on the temporal information. The pruning of the recent transactions of the user also addresses the user’s preference drifts and is more scalable when compared to state-of-art methods. Research limitations/implications In the proposed method, LCFDN, the neighborhood space is dynamically adjusted based on the temporal information. In addition, the LCFDN also determines the user’s current interest based on the recent preference or purchase details. This method is designed to continuously track the user’s preference with the growing dataset which makes it suitable to be implemented in the e-commerce industry. Compared with the state-of-art methods, this method provides high-quality recommendation with good efficiency. Originality/value The LCFDN is an extension of collaborative filtering with temporal information used as context. The dynamic nature of data and user’s preference drifts are addressed in the proposed method by dynamically adapting the neighbors. To improve the scalability, the proposed method is implemented in big data environment using MapReduce. The proposed recommendation system provides greater prediction accuracy than the traditional recommender systems.

scholarly journals A Novel Contextual Information Recommendation Model and Its Application in e-Commerce Customer Satisfaction Management

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Feipeng Guo ◽  
Qibei Lu
Keyword(s):  
Customer Satisfaction ◽  
Collaborative Filtering ◽  
Distributed Cognition ◽  
Contextual Information ◽  
User Interest ◽  
User Interests ◽  
Good Ability ◽  
Extraction Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Information Recommendation

In the current supply chain environment, distributed cognition theory tells us that various types of context information in which a recommendation is provided are important for e-commerce customer satisfaction management. However, traditional recommendation model does not consider the distributed and differentiated impact of different contexts on user needs, and it also lacks adaptive capacity of contextual recommendation service. Thus, a contextual information recommendation model based on distributed cognition theory is proposed. Firstly, the model analyzes the differential impact of various sensitive contexts and specific examples on user interest and designs a user interest extraction algorithm based on distributed cognition theory. Then, the sensitive contexts extracted from user are introduced into the process of collaborative filtering recommendation. The model calculates similarity among user interests. Finally, a novel collaborative filtering algorithm integrating with context and user similarity is designed. The experimental results in e-commerce and benchmark dataset show that this model has a good ability to extract user interest and has higher recommendation accuracy compared with other methods.

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