Session 6: Graphs

Date: Wednesday September 20, 4:05 PM – 5:25 PM (GMT+8)
Room: Hall 406D
Session Chair: Robin Burke
Parallel with: Session 5: Sequential Recommendation 1

• RESMulti-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation
  by Yuwei Cao (University of Illinois at Chicago), Liangwei Yang (University of Illinois Chicago), Chen Wang (University of Illinois Chicago), Zhiwei Liu (Salesforce Inc.), Hao Peng (Beihang University), Chenyu You (Yale University) and Philip Yu (University of Illinois Chicago).

  Recommendation systems suffer in the strict cold-start (SCS) scenario, where the user-item interactions are entirely unavailable. The well-established, dominating identity (ID)-based approaches completely fail to work. Cold-start recommenders, on the other hand, leverage item contents (brand, title, descriptions, etc.) to map the new items to the existing ones. However, the existing SCS recommenders explore item contents in coarse-grained manners that introduce noise or information loss. Moreover, informative data sources other than item contents, such as users’ purchase sequences and review texts, are largely ignored. In this work, we explore the role of the fine-grained item attributes in bridging the gaps between the existing and the SCS items and pre-train a knowledgeable item-attribute graph for SCS item recommendation. Our proposed framework, ColdGPT, models item-attribute correlations into an item-attribute graph by extracting fine-grained attributes from item contents. ColdGPT then transfers knowledge into the item-attribute graph from various available data sources, i.e., item contents, historical purchase sequences, and review texts of the existing items, via multi-task learning. To facilitate the positive transfer, ColdGPT designs specific submodules according to the natural forms of the data sources and proposes to coordinate the multiple pre-training tasks via unified alignment-and-uniformity losses. Our pre-trained item-attribute graph acts as an implicit, extendable item embedding matrix, which enables the SCS item embeddings to be easily acquired by inserting these items into the item-attribute graph and propagating their attributes’ embeddings. We carefully process three public datasets, i.e., Yelp, Amazon-home, and Amazon-sports, to guarantee the SCS setting for evaluation. Extensive experiments show that ColdGPT consistently outperforms the existing SCS recommenders by large margins and even surpasses models that are pre-trained on 75 – 224 times more, cross-domain data on two out of four datasets. Our code and pre-processed datasets for SCS evaluations are publicly available to help future SCS studies.

  Full text in ACM Digital Library

• INDLightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee’s Advertisement Recommendation
  by Dang Minh Nguyen (Shopee, SEA Group), Chenfei Wang (Shopee, SEA Group), Yan Shen (Shopee, SEA Group) and Yifan Zeng (Shopee, SEA Group).

  Graph Neural Network (GNN) is the trending solution for item retrieval in recommendation problems. Most recent reports, however, focus heavily on new model architectures. This may bring some gaps when applying GNN in the industrial setup, where, besides the model, constructing graph and handling data sparsity also play critical roles in the overall success of the project. In this work, we report how we apply GNN for large-scale e-commerce item retrieval at Shopee. We detail our simple yet novel and impactful techniques in graph construction, modeling, and handling data skewness. Specifically, we construct high-quality item graphs by combining strong-signal user behaviors with high-precision collaborative filtering (CF) algorithm. We then develop a new GNN architecture named LightSAGE to produce high-quality items’ embeddings for vector search. Finally, we develop multiple strategies to handle cold-start and long-tail items, which are critical in an advertisement (ads) system. Our models bring improvement in offline evaluations, online A/B tests, and are deployed to the main traffic of Shopee’s Recommendation Advertisement system.

  Full text in ACM Digital Library

• RESMulti-Relational Contrastive Learning for Recommendation
  by Wei Wei (University of Hong Kong), Lianghao Xia (University of Hong Kong) and Chao Huang (University of Hong Kong).

  Dynamic behavior modeling has become a crucial task for personalized recommender systems that aim to learn users’ time-evolving preferences on online platforms. However, many recommendation models rely on a single type of behavior learning, which significantly limits their ability to represent user-item relationships in real-life applications where interactions between users and items often come in multiple types (e.g., click, tag-as-favorite, review, and purchase). To offer better recommendations, this paper proposes the Evolving Graph Contrastive Memory Network (EGCM) to model dynamic interaction heterogeneity. Firstly, we develop a multi-relational graph encoder to capture short-term preference heterogeneity and preserve the dedicated relation semantics for different types of user-item interactions. Additionally, we design a dynamic cross-relational memory network that enables EGCM to capture users’ long-term multi-behavior preferences and the underlying evolving cross-type behavior dependencies over time. To obtain robust and informative user representations with both commonality and diversity across multi-behavior interactions, we design a multi-relational contrastive learning paradigm with heterogeneous short- and long-term interest modeling. We further provide theoretical analyses to support the modeling of commonality and diversity from the perspective of enhancing model optimization. Experiments on several real-world datasets demonstrate the superiority of our recommender system over various state-of-the-art baselines.

  Full text in ACM Digital Library

• REPChallenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
  by ito Walter Anelli (Politecnico di Bari), Daniele Malitesta (Polytechnic University of Bari), Claudio Pomo (Politecnico di Bari), Alejandro Bellogin (Universidad Autonoma de Madrid), Eugenio Di Sciascio (Politecnico di Bari) and Tommaso Di Noia (Politecnico di Bari)

  Among the most successful research directions in recommender systems, there are undoubtedly graph neural network-based models (GNNs). Through the natural modeling of users and items as a bipartite, undirected graph, GNNs have pushed up the performance bar for modern recommenders.

  Unfortunately, most of the original graph-based works cherry-pick results from previous baseline papers without bothering to check whether the results are valid for the configuration under analysis. Thus, our work stands first and foremost as a work on the replicability of results. We provide a code that succeeds in replicating the results proposed in the articles introducing six of the most popular and recent graph recommendation models (i.e., NGCF, DGCF, LightGCN, SGL, UltraGCN, and GFCF). In our experimental setup, we test these six models on three common benchmarking datasets (i.e., Gowalla, Yelp 2018, and Amazon Book). In addition, to understand how these models perform with respect to traditional models for collaborative filtering, we compare the graph models under analysis with some models that have historically emerged as the best performers in an offline evaluation context. Then, the study is extended on two new datasets (i.e., Allrecipes and BookCrossing) for which no known setup exists in the literature. Since the performance on such datasets is not entirely aligned with the previous benchmarking one, we further analyze the possible impact of specific dataset characteristics on the recommendation accuracy performance. By investigating the information flow to the users from their neighborhoods, the analysis aims to identify for which models these intrinsic features in the dataset structure impact accuracy performance. The code to reproduce the experiments is available at: https://split.to/Graph-Reproducibility.

  Full text in ACM Digital Library

Back to program