Recommendation with Diversity: Mass Diffusion Model Based on Trust Network and Object Reputation

Yun Bai,Weiquan Cai

doi:10.1155/2021/5527568

Abstract

The traditional mass diffusion recommendation algorithm only relies on the user’s object collection relationship, resulting in poor recommendation performance for users with small purchases (i.e., small-degree user), and it is difficult to balance the accuracy and diversity of the recommendation system. This paper introduces the trust relationship into the resource allocation process of the traditional mass diffusion algorithm and proposes the Dual Wing Mass Diffusion model (DWMD), which constructs a dual wing graph based on trust relationships and object collection relationships. Implicit trust is mined according to the network structure of the trust relationship and integrated into the resource allocation process, and then merging the positive effects of object reputation on a recommendation through tunable scaling parameters. The user controls the tunable scaling parameter to achieve the best recommendation performance. The experimental results show that the DWMD method significantly improves diversity and novelty while ensuring high accuracy and effectively improves the accuracy and diversity balance. The improved recommendation performance for small-degree users proves that the trust relationship can effectively alleviate the generalized cold start problem of the recommendation algorithm for users who collect a small number of objects.

Highlights

Introduction e explosive growth ofInternet information and the prosperity of e-commerce has made the problem of information overload increasingly serious. e mass of commodities makes it difficult for consumers to choose their favorite commodities and makes it difficult for commodity producers to distinguish their single products from the mass of commodities
In view of the above problems, this paper proposes a dual wing mass diffusion model based on trust network and object reputation (DWMD for short). e model constructs tripartite graphs according to the trust relationship and object selection relationship and combines the two relations with tunable parameters to balance the dilemma of accuracy and diversity of recommendation
In order to verify the validity and superiority of Dual Wing Mass Diffusion model (DWMD) proposed, our algorithm is compared with four benchmark methods on the Epinions and Cao data sets, which is the item-based collaborative filtering recommendation model (Item-based CF), mass diffusion (MD) [7], CosRA + T model, and trust-based mass diffusion model (TrustMD) [29]

Summary

Explicit Trust

According to life experience: If two people trust a third person at the same time, there may be a potential trust relationship between them, which is called coupling trust. Implicit trust refers to the trust relationship inferred from the structure of the trust network. Input: “user-object” binary network G(U, O, E), rating matrix R, and object user ui; Output: the final resources of objects 􏼈fr1, . M) according to equation (2); (3) Calculate the resources of object oβ N) according to equation (3); ALGORITHM 1: Algorithm of mass diffusion with object reputation. Input: “object-user-user” triple network G(U, O, E, B), rating matrix R, and object user ui; Output: the final resources of objects 􏼈ft1rust, . ALGORITHM 2: Algorithm of mass diffusion based on trust relationships

O4 0 u4

Data and Metrics

Experiments and Results