Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions

Alpamis Kutlimuratov,Akmalbek Abdusalomov,Taeg Keun Whangbo

doi:10.3390/sym12111930

Abstract

Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two entities. Moreover, when tag information that is added to items by users themselves is coupled with hierarchically structured features, the rating prediction efficiency and system personalization are improved. To this effect, we developed a novel model that acquires hidden-level hierarchical features of users and items and combines them with the tag information of items that regularizes the matrix factorization process of a basic weighted non-negative matrix factorization (WNMF) model to complete our prediction model. The idea behind the proposed approach was to deeply factorize a basic WNMF model to obtain hidden hierarchical features of user’s preferences and item characteristics that reveal a deep relationship between them by regularizing the process with tag information as an auxiliary parameter. Experiments were conducted on the MovieLens 100K dataset, and the empirical results confirmed the potential of the proposed approach and its superiority over models that use the primary features of users and items or tag information separately in the prediction process.

Highlights

With the increase in the availability of data from online content providers, delivering valuable information that gratifies and holds a consumer’s interest has attracted significant attention; modeling an effective recommendation system is essential
It is worth noting that the proposed method completed the entire workflow for the rating prediction only in the case of items constituting tag information, while for the rest of the instances, it morphed into a basic weighted non-negative matrix factorization (WNMF) model, i.e., without solving for Equations (10)–(13)
Weighted non-negative matrix factorization: This was chosen as the base model for the proposed approach, where WNMF attempts to factorize a weighted user–item rating matrix into two non-negative submatrices to minimize the error between the true and predicted ratings

Summary

Introduction

With the increase in the availability of data from online content providers, delivering valuable information that gratifies and holds a consumer’s interest has attracted significant attention; modeling an effective recommendation system is essential. The primary objective of a recommendation system is to offer suggestions based on user preferences, which are solicited from historical data, such as ratings, reviews, and tags. Recommendation systems are designed based on the type of information obtained such that the diversity of information influences their implementation and structure. To this effect, two traditional approaches exist for building recommendation systems: content-based filtering (CBF) and collaborative

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Methods

Results

Conclusion