A Hybrid Recommender System Based on User-Recommender Interaction

Zhang, Heng‐Ru; Min, Fan; He, Xu; Xu, Yuanyuan

doi:10.1155/2015/145636

Cited by 28 publications

(13 citation statements)

References 37 publications

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“…The problem of scalability would be solved mainly by the reduction of data size during recommendation. The clustering of users with similar rating patterns also helps decrease neighborhood scope during recommendations, thus improving online performances [22].…”

Section: Hybrid-basedmentioning

confidence: 99%

Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

Yeh¹,

Kashef²

2020

AIT

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A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. The blockchain is transforming industries by enabling innovative business practices. Its revolutionary power has permeated areas such as banking, financing, trading, manufacturing, supply chain management, healthcare, and government. Blockchain and the Internet of Things (BIOT) apply the usage of blockchain in the inter-IOT communication system, therefore, security and privacy factors are achievable. The integration of blockchain technology and IoT creates modern decentralized systems. The BIOT models can be applied by various industries including e-commerce to promote decentralization, scalability, and security. This research calls for innovative and advanced research on Blockchain and recommendation systems. We aim at building a secure and trust-based system using the advantages of blockchain-supported secure multiparty computation by adding smart contracts with the main blockchain protocol. Combining the recommendation systems and blockchain technology allows online activities to be more secure and private. A system is constructed for enterprises to collaboratively create a secure database and host a steadily updated model using smart contract systems. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recommendation system with acceptable performance.

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Section: Hybrid-basedmentioning

confidence: 99%

Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

Yeh¹,

Kashef²

2020

AIT

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“…and S i,j 1 = 1, S i,j 2 = 0.5, S i,j 3 = 0.75, S i,j 4 = 0.25. Therefore, we reordered the items' candidate set by Equations (11) and (12) to generate better recommendation results to the users.…”

Section: The Recommendation Algorithm Based On the User's Implicit Fementioning

confidence: 99%

A Personalized Recommendation Algorithm Based on the User’s Implicit Feedback in E-Commerce

Wang

2018

Future Internet

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A recommendation system can recommend items of interest to users. However, due to the scarcity of user rating data and the similarity of single ratings, the accuracy of traditional collaborative filtering algorithms (CF) is limited. Compared with user rating data, the user’s behavior log is easier to obtain and contains a large amount of implicit feedback information, such as the purchase behavior, comparison behavior, and sequences of items (item-sequences). In this paper, we proposed a personalized recommendation algorithm based on a user’s implicit feedback (BUIF). BUIF considers not only the user’s purchase behavior but also the user’s comparison behavior and item-sequences. We extracted the purchase behavior, comparison behavior, and item-sequences from the user’s behavior log; calculated the user’s similarity by purchase behavior and comparison behavior; and extended word-embedding to item-embedding to obtain the item’s similarity. Based on the above method, we built a secondary reordering model to generate the recommendation results for users. The results of the experiment on the JData dataset show that our algorithm shows better improvement in regard to recommendation accuracy over other CF algorithms.

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“…Min and Zhu explained the concept about granular association rule in detail [22]. Then, some rough set based methods are proposed to solve the new user and new item problem by extending Min work [23, 24]. …”

Section: Related Workmentioning

confidence: 99%

Preference Mining Using Neighborhood Rough Set Model on Two Universes

Zeng

2016

Computational Intelligence and Neuroscience

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Preference mining plays an important role in e-commerce and video websites for enhancing user satisfaction and loyalty. Some classical methods are not available for the cold-start problem when the user or the item is new. In this paper, we propose a new model, called parametric neighborhood rough set on two universes (NRSTU), to describe the user and item data structures. Furthermore, the neighborhood lower approximation operator is used for defining the preference rules. Then, we provide the means for recommending items to users by using these rules. Finally, we give an experimental example to show the details of NRSTU-based preference mining for cold-start problem. The parameters of the model are also discussed. The experimental results show that the proposed method presents an effective solution for preference mining. In particular, NRSTU improves the recommendation accuracy by about 19% compared to the traditional method.

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A Hybrid Recommender System Based on User-Recommender Interaction

Cited by 28 publications

References 37 publications

Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

Trust-Based Collaborative Filtering Recommendation Systems on the Blockchain

A Personalized Recommendation Algorithm Based on the User’s Implicit Feedback in E-Commerce

Preference Mining Using Neighborhood Rough Set Model on Two Universes

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