Interpreting Contextual Effects By Contextual Modeling In Recommender Systems

TL;DR

This paper investigates how different contextual modeling approaches in context-aware recommender systems can be interpreted to understand the influence of context on recommendations, comparing explanations and performance on real-world datasets.

Contribution

It explores and compares methods for interpreting contextual effects within recommendation models, enhancing understanding of how context influences recommendations.

Findings

Different contextual models offer varying interpretability of effects.

Interpretations correlate with recommendation accuracy.

Model restructuring improves explanation clarity.

Abstract

Recommender systems have been widely applied to assist user's decision making by providing a list of personalized item recommendations. Context-aware recommender systems (CARS) additionally take context information into considering in the recommendation process, since user's tastes on the items may vary from contexts to contexts. Several context-aware recommendation algorithms have been proposed and developed to improve the quality of recommendations. However, there are limited research which explore and discuss the capability of interpreting the contextual effects by the recommendation models. In this paper, we specifically focus on different contextual modeling approaches, reshape the structure of the models, and exploit how to utilize the existing contextual modeling to interpret the contextual effects in the recommender systems. We compare the explanations of contextual effects, as well as the recommendation performance over two-real world data sets in order to examine the quality of interpretations.