Item-Based Vs User-Based Collaborative Recommendation Predictions

2018 ◽  
pp. 165-170
Author(s):  
Joel Azzopardi
Keyword(s):  
Collaborative Recommendation

WWW collaborative recommendation based on reliability

2006 ◽  
Vol 23 (2) ◽  
pp. 255-258
Author(s):  
Haoyang Che ◽  
Jiacai Zhang
Keyword(s):  
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

scholarly journals Statistical analysis of k-nearest neighbor collaborative recommendation

2010 ◽  
Vol 38 (3) ◽  
pp. 1568-1592 ◽  
Author(s):  
Gérard Biau ◽  
Benoît Cadre ◽  
Laurent Rouvière
Keyword(s):  
Statistical Analysis ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Collaborative Recommendation

A Webpage Ranking Algorithm Based on Collaborative Recommendation

2013 ◽  
Vol 765-767 ◽  
pp. 998-1002
Author(s):  
Shao Xuan Zhang ◽  
Tian Liu
Keyword(s):  
Sorting Algorithm ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Search Results ◽  
Collaborative Recommendation ◽  
Interest Model ◽  
Sorting Efficiency ◽  
User Interest Model ◽  
Relevant Calculation ◽  
Personalized Ranking

In view of the present personalized ranking of search results user interest model construction difficult, relevant calculation imprecise problems, proposes a combination of user interest model and collaborative recommendation algorithm for personalized ranking method. The method from the user search history, including the submit query, click the relevant webpage information to train users interest model, then using collaborative recommendation algorithm to obtain with common interests and neighbor users, on the basis of these neighbors on the webpage and webpage recommendation level associated with the users to sort the search results. Experimental results show that: the algorithm the average minimum precision than general sorting algorithm was increased by about 0.1, with an increase in the number of neighbors of the user, minimum accuracy increased. Compared with other ranking algorithms, using collaborative recommendation algorithm is helpful for improving webpage with the user interest relevance precision, thereby improving the sorting efficiency, help to improve the search experience of the user.

Multidimensional credibility model for neighbor selection in collaborative recommendation

2009 ◽  
Vol 36 (3) ◽  
pp. 7114-7122 ◽  
Author(s):  
Kwiseok Kwon ◽  
Jinhyung Cho ◽  
Yongtae Park
Keyword(s):  
Collaborative Recommendation ◽  
Neighbor Selection
Sign in / Sign up

Export Citation Format

Share Document