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Incorporating Price into Recommendation With Graph Convolutional Networks | IEEE Journals & Magazine | IEEE Xplore

Incorporating Price into Recommendation With Graph Convolutional Networks


Abstract:

In recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which...Show More

Abstract:

In recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which describes users or items, e.g., textual attributes, categorical demographics, product images, and so on. Price, an important factor in marketing — which determines whether a user will make the final purchase decision on an item — surprisingly, has received relatively little scrutiny. In this work, we aim at developing an effective method to predict user purchase intention with the focus on the price factor in recommender systems. The main difficulties are two-fold: 1) the preference and sensitivity of a user on item price are unknown, which are only implicitly reflected in the items that the user has purchased, and 2) how the item price affects a user’s intention depends largely on the product category, that is, the perception and affordability of a user on item price could vary significantly across categories. Towards the first difficulty, we propose to model the transitive relationship between user-to-item and item-to-price, taking the inspiration from the recently developed Graph Convolution Networks (GCN). The key idea is to propagate the influence of price on users with items as the bridge, so as to make the learned user representations be price-aware. For the second difficulty, we further integrate item categories into the propagation progress and model the possible pairwise interactions for predicting user-item interactions. We conduct extensive experiments on two real-world datasets, demonstrating the effectiveness of our GCN-based method in learning the price-aware preference of users. Further analysis reveals that modeling the price awareness is particularly useful for predicting user preference on items of unexplored categories.
Published in: IEEE Transactions on Knowledge and Data Engineering ( Volume: 35, Issue: 2, 01 February 2023)
Page(s): 1609 - 1623
Date of Publication: 22 June 2021

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1 Introduction

Recommendation is attracting increasing attention in both industry and academia, owing to the prevalence and success of recommender systems in many applications [1], [2], [3], [4]. From the perspective of product providers, the aim of building a recommender system is to increase the traffic and revenue, by recommending the items that a user will be likely to consume. As such, the key data source to leverage is the past consumption histories of users, since they provide direct evidence on a user’s interest. To this end, much research effort has been devoted to collaborative filtering (CF) [5], [6], [7], which casts the task as completing the user-item consumption matrix, and incorporating side information into CF, such as textual attributes [8], [9], [10], categorical demographics [11], social information [12] and product images [13]. To utilize such diverse data in an unified model, a general class of feature-based recommendation models have been proposed, such as the pioneer work of factorization machines (FM) [14] and several recent developments that augment FM with neural networks [15], [16], [17].

References

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