A probabilistic model to resolve diversity-accuracy challenge of recommendation systems

TL;DR

This paper introduces a probabilistic hybrid recommendation model that allows tuning between diversity and accuracy, effectively resolving the common trade-off in recommender systems and outperforming traditional models.

Contribution

A novel probabilistic framework that enables adjustable diversity and accuracy in recommendations by tuning a single parameter, improving performance over classic models.

Findings

The model effectively balances diversity and accuracy.

Experiments show outperformance over traditional models.

Low computational complexity makes it suitable for real systems.

Abstract

Recommendation systems have wide-spread applications in both academia and industry. Traditionally, performance of recommendation systems has been measured by their precision. By introducing novelty and diversity as key qualities in recommender systems, recently increasing attention has been focused on this topic. Precision and novelty of recommendation are not in the same direction, and practical systems should make a trade-off between these two quantities. Thus, it is an important feature of a recommender system to make it possible to adjust diversity and accuracy of the recommendations by tuning the model. In this paper, we introduce a probabilistic structure to resolve the diversity-accuracy dilemma in recommender systems. We propose a hybrid model with adjustable level of diversity and precision such that one can perform this by tuning a single parameter. The proposed recommendation model consists of two models: one for maximization of the accuracy and the other one for specification of the recommendation list to tastes of users. Our experiments on two real datasets show the functionality of the model in resolving accuracy-diversity dilemma and outperformance of the model over other classic models. The proposed method could be extensively applied to real commercial systems due to its low computational complexity and significant performance.