Social Popularity based SVD++ Recommender System
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TLDR
Social popularity factor are incorporated in SVD++ factorization method as implicit feedback to improve accuracy and scalability of recommendations.Abstract:
Recommender systems have shown a lot of awareness in the past decade. Due to their great business value, recommender systems have also been successfully deployed in business, such as product recommendation at flipkart, HomeShop18, and music recommendation at Last.fm, Pandora, and movie recommendation at Flixstreet, MovieLens, and Jinni. In the past few years, the incredible growth of Web 2.0 web sites and applications constitute new challenges for Traditional recommender systems. Traditional recommender systems always ignore social interaction among users. But in our real life, when we are asking our friends or looking opinions, reviews for recommendations of Mobile or heart touching music, movies, electronic gadgets, restaurant, book, games, software Apps, we are actually using social information for recommendations. In this paper social popularity factor are incorporated in SVD++ factorization method as implicit feedback to improve accuracy and scalability of recommendations.read more
Citations
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Proceedings ArticleDOI
Hybrid Recommender System based on Autoencoders
TL;DR: In this article, a loss function adapted to input data with missing values was proposed to improve the test error on all users/items, while side information had more impact on cold users and items.
Proceedings ArticleDOI
Hybrid Recommender System based on Autoencoders
TL;DR: This paper enhanced the architecture of Recommender Systems by using a loss function adapted to input data with missing values, and by incorporating side information, demonstrating that while side information only slightly improve the test error averaged on all users/items, it has more impact on cold users/ items.
Journal ArticleDOI
TWIN: Personality-based Intelligent Recommender System
TL;DR: The research work of the third author is partially funded by the WIQ-EI (IRSES grant n. 269180) and DIANA APPLICATIONS (TIN2012-38603-C02-01) and done in the framework of the VLC/Campus Microcluster on Multimodal Interaction in Intelligent Systems.
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TL;DR: Wang et al. as mentioned in this paper proposed an improved collaborative recommendation algorithm based on optimized user similarity, where a balancing factor is added to the traditional cosine similarity algorithm, which is used to calculate the project rating scale differences between different users.
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