scholarly journals On Differentially Private Online Collaborative Recommendation Systems

2016 ◽  
pp. 210-226
Author(s):  
Seth Gilbert ◽  
Xiao Liu ◽  
Haifeng Yu
Keyword(s):  
Recommendation Systems ◽  
Collaborative Recommendation

Stream-based explainable recommendations via blockchain profiling

2021 ◽  
pp. 1-17
Author(s):  
Fátima Leal ◽  
Bruno Veloso ◽  
Benedita Malheiro ◽  
Juan Carlos Burguillo ◽  
Adriana E. Chis ◽  
...  
Keyword(s):  
Historical Data ◽  
Positive Impact ◽  
Empirical Evaluation ◽  
Recommendation Systems ◽  
Use Cases ◽  
Collaborative Filter ◽  
Model Based ◽  
Collaborative Recommendation ◽  
Smart Contract ◽  
User Trust

Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations.

scholarly journals Cluster searching strategies for collaborative recommendation systems

2013 ◽  
Vol 49 (3) ◽  
pp. 688-697 ◽  
Author(s):  
Ismail Sengor Altingovde ◽  
Özlem Nurcan Subakan ◽  
Özgür Ulusoy
Keyword(s):  
Recommendation Systems ◽  
Collaborative Recommendation

Collaborative Recommendation Systems and Link Analysis

2011 ◽  
pp. 69-97
Author(s):  
François Fouss
Keyword(s):  
Random Walk ◽  
Text Mining ◽  
Graph Mining ◽  
Web Mining ◽  
Relational Learning ◽  
Recommendation Systems ◽  
Link Analysis ◽  
Graph Structure ◽  
Collaborative Recommendation ◽  
As Graph

Link analysis is a framework usually associated with fields such as graph mining, relational learning, Web mining, text mining, hyper-text mining, visualization of link structures. It provides and analyzes relationships and associations between many objects of various types that are not apparent from isolated pieces of information. This chapter shows how to apply various link-analysis algorithms exploiting the graph structure of databases on collaborative-recommendation tasks. More precisely, two kinds of link-analysis algorithms are applied to recommend items to users: random-walk based models and kernel-based models. These link-analysis based algorithms do not use any feature of the items in order to compute the recommendations, they first compute a matrix containing the links between persons and items, and then derive recommendations from this matrix or part of it.

Hybrid Collaborative Fusion Based Product Recommendation Exploiting Sentiments from Implicit and Explicit Reviews

2021 ◽  
pp. 2141013
Author(s):  
N Zafar Ali Khan ◽  
R. Mahalakshmi
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
User Profile ◽  
Personalized Recommendation ◽  
Product Reviews ◽  
Aspect Mining ◽  
Product Recommendation ◽  
Collaborative Recommendation ◽  
Demographic Attributes ◽  
Implicit And Explicit

Product recommendation is an important functionality in online ecommerce systems. The goal of the recommendation system is to recommend products with has higher purchase success ratio. User profile, product purchase history etc. have been used in many works to provide high quality recommendations. Product reviews is one of the important source for personalized recommendation. Typical collaborative recommendation systems are built upon user rating on products. But in many cases, these rating information are inaccurate or not available. There is also a problem of biased reviews decreasing the accuracy of recommendation systems. This work proposes a aspect mining collaborative fusion based recommendation system considering both the implicit and explicit reviews. The sentiments about different aspects mined from reviews are translated to multi-dimensional ratings. These ratings are then fused with user profile and demographic attributes to improve the quality of recommendation. The proposed recommendation system has 3.79% lower RMSE, 4.51% lower MAE and 22% lower MRE compared to most recent collaborative filtering based recommendation system.

Introduction to Recommender Systems

2011 ◽  
pp. 21-37
Author(s):  
François Fouss
Keyword(s):  
Recommender Systems ◽  
Recent Work ◽  
Recommendation Systems ◽  
Demographic Information ◽  
Four Dimensions ◽  
Collaborative Recommendation ◽  
History Of ◽  
Work Done ◽  
The Way

Recommender systems try to provide people with recommendations of items they will appreciate, based on their past preferences, history of purchase, and demographic information. This chapter (1) introduces recommender systems, classifying them along four dimensions (i.e. the way the preferences are gathered, the used approach, the type of algorithm, and the way the results are provided) and describing recent work done in the area, and (2) provides more details about one such type of recommender systems, namely collaborative-recommendation systems. Such systems work by analyzing the items previously rated by all the users and are not based on the content of the items, as content-based systems.

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