Lingbo Mo, Shijie Chen, Ziru Chen, Xiang Deng, Ashley Lewis, Sunit Singh, Samuel Stevens, Chang-You Tai, Zhen Wang, Xiang Yue, Tianshu Zhang, Yu Su, Huan Sun. Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2023.

There has been significant progress in the utilization of heterogeneous knowledge graphs (KG) as auxiliary information in recommendation systems. Reasoning over KG paths sheds light on the user's decision-making process. Previous methods focus on formulating this process as a multi-hop reasoning problem. However, without some form of guidance in the reasoning process, such a huge search space results in poor accuracy and little explanation diversity. In this paper, we propose UCPR, a user-centric path reasoning network that constantly guides the search from the aspect of user demand and enables explainable recommendations. In this network, a multi-view structure leverages not only local sequence reasoning information but also a panoramic view of the user's demand portfolio while inferring subsequent user decision-making steps. Experiments on five real-world benchmarks show UCPR is significantly more accurate than state-of-the-art methods. Besides, we show that the proposed model successfully identifies users' concerns and increases reason-ing diversity to enhance explainability

There has been significant progress in utilizing heterogeneous knowledge graphs (KGs) as auxiliary information in recommendation systems. However, existing KG-aware recommendation models rely solely on Euclidean space, neglecting hyperbolic space, which has already been shown to possess a superior ability to separate embed-dings by providing more "room". We propose a knowledge-based hyperbolic propagation framework (KBHP) which includes hyperbolic components for calculating the importance of KG attributes relative to achieve better knowledge propagation. In addition to the original relations in the knowledge graph, we propose a user purchase relation to better represent logical patterns in hyperbolic space, which bridges users and items for modeling user preference. Experiments on four real-world benchmarks show that KBHP is significantly more accurate than state-of-the-art models. We further visualize the generated embeddings to demonstrate that the proposed model successfully clusters attributes that are relevant to items and highlights those that contain useful information for the recommendation.

Researchers have begun to utilize heterogeneous knowledge graphs(KGs) as auxiliary information in recommendation systems to mitigate the cold start and sparsity issues. However, utilizing a graph neural network (GNN) to capture information in KG and further apply in RS is still problematic as it is unable to see each item's properties from multiple perspectives. To address these issues, we propose the multi-view item network (MVIN), a GNN-based recommendation model that provides superior recommendations by describing items from a unique mixed view from user and entity angles. MVIN learns item representations from both the user view and the entity view. From the user view, user-oriented modules score and aggregate features to make recommendations from a personalized perspective constructed according to KG entities which incorporates user click information. From the entity view, the mixing layer contrasts layer-wise GCN information to further obtain comprehensive features from internal entity-entity interactions in the KG. We evaluate MVIN on three real-world datasets: MovieLens-1M (ML-1M), LFM-1b 2015 (LFM-1b), and Amazon-Book (AZ-book). Results show that MVIN significantly outperforms state-of-the-art methods on these three datasets. Besides, from user-view cases, we find that MVIN indeed captures entities that attract users. Figures further illustrate that mixing layers in a heterogeneous KG plays a vital role in neighborhood information aggregation.

As deep learning models are getting popular, upgrading the retrieval-based content recommendation system to the learning-based system is highly demanded. However, efficiency is a critical issue. For article recommendation, an effective neural network which generates a good representation of the article content could prove useful. Hence, we propose PGA-Recommender, a phrase-guided article recommendation model which mimics the process of human behavior - first browsing, then guided by key phrases, and finally aggregating the gleaned information. As this can be performed independently offline, it is thus compatible with current commercial retrieval-based (keyword-based) article recommender systems. A total of six months of real logs - from Apr 2017 to Sep 2017 - were used for experiments. Results show that PGA-Recommender outperforms different state-of-the-art schemes including session-, collaborative filter-, and content-based recommendation models. Moreover, it suggests a diverse mix of articles while maintaining superior performance in terms of both click and view predictions. The results of A/B tests show that simply using the backward version of PGA-Recommender yields 40% greater click-through rates as compared to the retrieval-based system when deployed to a language of which we have zero knowledge.