Text-Aware Graph Embeddings for Donation Behavior Prediction (2022)

Abstract: Predicting user behavior is essential for a large number of applications including recommender and dialog systems, and more broadly in domains such as healthcare, education, and economics. In this paper, we show that we can effectively predict donation behavior by using text-aware graph models, building upon graphs that connect user behaviors and their interests. Using a university donation dataset, we show that the graph representation significantly improves over learning from textual representations. Moreover, we show how incorporating implicit information inferred from text associated with the graph entities brings additional improvements. Our results demonstrate the role played by text-aware graph representations in predicting donation behavior.

## introduction understanding and predicting user behavior from their digital traces is important for many applications, such as recommender systems @xcite , information filtering @xcite , or dialogue agents @xcite , as well as numerous behavioral interventions in healthcare, education, economics, and more. prior research efforts have modeled user interests for predicting future behavior such as purchases @xcite or click-through likelihood @xcite , using signals like engagement with social media content or purchase history. traditional approaches to user behavior prediction use machine learning models that make use of input features in a linear fashion. these models, including the more advanced neural network architectures, assume that individual data samples are provided one at a time and independent of one another. example user modeling approaches include using recurrent neural networks to encode the behavioral history of each user @xcite or linearly aggregating different parts of a user's background and behavior, such as their demographics and online posting patterns @xcite . such approaches do not take full advantage of the relations between entities; for instance, two products in one's purchase history may be different but still be related to one another; or two users may have interests that are seemingly diverse, but which have some degree of similarity. richer input representations that incorporate such relations can improve the performance of downstream machine learning models used to predict user behavior. graph models are a prominent way of representing relational information between entities. in particular, knowledge graphs have been used widely in the context of recommender systems. for example, one can construct a knowledge graph consisting of clothing brands and items and retrieve the most relevant or similar items to recommend to a user based on their most recent clothing purchase @xcite . further, interactions between users and entities can also be included in the graph, such as clicks or purchases. such a graph and its resulting node embeddings can better capture the relations between entities that arise from the aggregate behaviors of all the users. however, these relations still only come from explicitly observed interactions like someone clicking on one entity and then also purchasing another entity, or multiple people co-clicking or copurchasing the same entity. in many contexts, the resulting knowledge graph is sparse, as there is an absence of many co-occurring user-entity interactions due to factors such as a very large number of entities, or users having on average a very low number of interactions. as such, the learning models applied on these sparse graphs can be lacking. in this paper, we explore user behavior prediction by using text-aware graph representations in the context of university alumni donations. we model alumni donation behavior through text and graph-based representations and evaluate our meth-ods by predicting how likely a potential alumnus will donate to specific charitable funds. we conduct our experiments using the history of donations and university engagement newsletters of a large midwest public university. we start by building a graph representation of alumni and associated entities, such as academic majors, university funds, and articles in engagement newsletters. alumni actions, such as donating to a fund or clicking on an article in an engagement newsletter, are represented as edges connecting an alumnus node with a fund or article node. node embedding representations derived from this graph are thus capturing how different funds or engagement articles are related with respect to the alumni who donated to or clicked on them. we then use this graph to predict the likelihood of an alumnus to donate to a given charitable fund. specifically, our paper makes the following two main research contributions. first, we propose a graph framework to represent and predict user behavior, and show that it improves significantly over a linear representation that does not incorporate relational information. second, we show how this graph representation can be further enriched with implicit links drawn using semantic connections between the textual information associated with the graph entities, leading to additional performance improvements in user behavior prediction. overall, through experiments on a large alumni donations dataset, we demonstrate the effectiveness of using graph representations enhanced with implicit information for the purpose of user behavior prediction. ## university alumni dataset we conduct our experiments on a dataset of alumni information maintained by a large, public university in the midwestern region of the united states. each alumnus is tied to their educational history; we primarily use their major during their highest level of study at the university. the language used in the data is english. we focus on those who have donated any amount back to their alma mater and who have also engaged with engineering alumni online newsletters, which are typically distributed by email on a regular basis. we have 2 years of newsletter content from january 2018 to march 2020, accompanied by the interaction history of alumni. the interaction history consists of when and how many times a click occurred, as well as what article was specifically clicked in the newsletter. likewise, we also have a history of donations that individual alumni have made to various causes at the university. given our focus on those who have engaged with newsletters, the corresponding history of donations for these alumni span between january 2015 to june 2020. we show statistics about entities in our dataset in ## representing alumni and funds we aim to represent each alumnus primarily with their clicks. as seen in the previous section, every article linked within a newsletter has an accompanying short preview or summary that is displayed in the newsletter. since this is what alumni initially see and what prompts their clicks, we use this text in our experiments, rather than the full article text. ## predicting user behavior we have seen that the alumni behavior graph model encapsulates relationships between entities in the resulting embedding space. we evaluate the alumni behavior graph model for downstream predictive use in the context of donation prediction. we construct a task where we predict whether an alumnus is likely to donate to a particular fund, showing that we can distinguish which funds someone is likely to donate to. ## results we compare the use of text-only glove features and graph-based node2vec features in our experiments to evaluate the benefit of our alumni behavior graph model. further, we evaluate our graph representations both when enhanced with text similaritybased edges and without to show the effects of this adding this implicit information to the graph. we show our alumni donation interest prediction results in in the results, we see that the graph embedding features generally perform better than the text-only we see that adding implicit edges derived from the textual content of the funds and articles generally improves performance over only having explicit action edges that designate donations and clicks. similarity links between articles are more helpful when we have knowledge of an alumnus' entire prior donation history. accuracy based on using only fund features is much higher than random, showing that the model is indeed learning trends in which types of funds, in terms of content and theme, are generally more well-received. we know the classifier isn't simply picking up on specific popular funds, since we downsampled frequently occurring funds. notably, when we use both features from alumni and funds, we generally see better performance, especially when using graph features and with fund edges added. this shows that the prediction model is capturing learning relationships between alumni and funds, and how compatible a given alumnus is as a potential donor for a fund. when we use only unique donation pairs, we see that the results remain largely comparable with using complete donation pairs. however, the performance is lower than with the use of complete donation pairs when using only features derived from alumni, showing that the complete donation pairs prediction model learned more about donation trends of specific alumni whereas the unique donation pairs model has to understand more of the implicit relatedness between funds and articles. finally, we see that combining text-only glove features with graph-based node2vec features yields the highest performance. this implies that there is still use in having both the semantic content of the entities and their relational information, and that they are complementary to each other. ## conclusion in this work, we explored the use of text-aware graph representations for user behavior prediction. using a large dataset consisting of university alumni donations and their interests as expressed through click-throughs on a university newsletter, we showed that the use of a graph framework to explicitly encode the relations between user behaviors and user interests leads to significant improvements over simple linear representations. moreover, we showed how further improvements can be obtained by enhancing the graph with implicit links inferred from the semantic distance between graph entities' associated textual data. our results demonstrate the role played by graph representations using explicit and implicit relations for the prediction of user behavior. future work can expand upon our results and explore how textual semantic links behave with different datasets with heterogeneous graph algorithms, as well as in larger-scale data settings combined with transformer-based algorithms.