2,456 research outputs found
Explainable Reasoning over Knowledge Graphs for Recommendation
Incorporating knowledge graph into recommender systems has attracted
increasing attention in recent years. By exploring the interlinks within a
knowledge graph, the connectivity between users and items can be discovered as
paths, which provide rich and complementary information to user-item
interactions. Such connectivity not only reveals the semantics of entities and
relations, but also helps to comprehend a user's interest. However, existing
efforts have not fully explored this connectivity to infer user preferences,
especially in terms of modeling the sequential dependencies within and holistic
semantics of a path. In this paper, we contribute a new model named
Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for
recommendation. KPRN can generate path representations by composing the
semantics of both entities and relations. By leveraging the sequential
dependencies within a path, we allow effective reasoning on paths to infer the
underlying rationale of a user-item interaction. Furthermore, we design a new
weighted pooling operation to discriminate the strengths of different paths in
connecting a user with an item, endowing our model with a certain level of
explainability. We conduct extensive experiments on two datasets about movie
and music, demonstrating significant improvements over state-of-the-art
solutions Collaborative Knowledge Base Embedding and Neural Factorization
Machine.Comment: 8 pages, 5 figures, AAAI-201
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
Recent advances in personalized recommendation have sparked great interest in
the exploitation of rich structured information provided by knowledge graphs.
Unlike most existing approaches that only focus on leveraging knowledge graphs
for more accurate recommendation, we perform explicit reasoning with knowledge
for decision making so that the recommendations are generated and supported by
an interpretable causal inference procedure. To this end, we propose a method
called Policy-Guided Path Reasoning (PGPR), which couples recommendation and
interpretability by providing actual paths in a knowledge graph. Our
contributions include four aspects. We first highlight the significance of
incorporating knowledge graphs into recommendation to formally define and
interpret the reasoning process. Second, we propose a reinforcement learning
(RL) approach featuring an innovative soft reward strategy, user-conditional
action pruning and a multi-hop scoring function. Third, we design a
policy-guided graph search algorithm to efficiently and effectively sample
reasoning paths for recommendation. Finally, we extensively evaluate our method
on several large-scale real-world benchmark datasets, obtaining favorable
results compared with state-of-the-art methods.Comment: Accepted in SIGIR 201
Empowering recommender systems using automatically generated Knowledge Graphs and Reinforcement Learning
Personalized recommendations have a growing importance in direct marketing,
which motivates research to enhance customer experiences by knowledge graph
(KG) applications. For example, in financial services, companies may benefit
from providing relevant financial articles to their customers to cultivate
relationships, foster client engagement and promote informed financial
decisions. While several approaches center on KG-based recommender systems for
improved content, in this study we focus on interpretable KG-based recommender
systems for decision making.To this end, we present two knowledge graph-based
approaches for personalized article recommendations for a set of customers of a
large multinational financial services company. The first approach employs
Reinforcement Learning and the second approach uses the XGBoost algorithm for
recommending articles to the customers. Both approaches make use of a KG
generated from both structured (tabular data) and unstructured data (a large
body of text data).Using the Reinforcement Learning-based recommender system we
could leverage the graph traversal path leading to the recommendation as a way
to generate interpretations (Path Directed Reasoning (PDR)). In the
XGBoost-based approach, one can also provide explainable results using post-hoc
methods such as SHAP (SHapley Additive exPlanations) and ELI5 (Explain Like I
am Five).Importantly, our approach offers explainable results, promoting better
decision-making. This study underscores the potential of combining advanced
machine learning techniques with KG-driven insights to bolster experience in
customer relationship management.Comment: Accepted at KDD (OARS) 2023 [https://oars-workshop.github.io/
Faithful Path Language Modelling for Explainable Recommendation over Knowledge Graph
Path reasoning methods over knowledge graphs have gained popularity for their
potential to improve transparency in recommender systems. However, the
resulting models still rely on pre-trained knowledge graph embeddings, fail to
fully exploit the interdependence between entities and relations in the KG for
recommendation, and may generate inaccurate explanations. In this paper, we
introduce PEARLM, a novel approach that efficiently captures user behaviour and
product-side knowledge through language modelling. With our approach, knowledge
graph embeddings are directly learned from paths over the KG by the language
model, which also unifies entities and relations in the same optimisation
space. Constraints on the sequence decoding additionally guarantee path
faithfulness with respect to the KG. Experiments on two datasets show the
effectiveness of our approach compared to state-of-the-art baselines. Source
code and datasets: AVAILABLE AFTER GETTING ACCEPTED
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but
more importantly to generate explanations of why particular decisions are made.
This can be achieved by explicit KG reasoning, where a model starts from a user
node, sequentially determines the next step, and walks towards an item node of
potential interest to the user. However, this is challenging due to the huge
search space, unknown destination, and sparse signals over the KG, so
informative and effective guidance is needed to achieve a satisfactory
recommendation quality. To this end, we propose a CoArse-to-FinE neural
symbolic reasoning approach (CAFE). It first generates user profiles as coarse
sketches of user behaviors, which subsequently guide a path-finding process to
derive reasoning paths for recommendations as fine-grained predictions. User
profiles can capture prominent user behaviors from the history, and provide
valuable signals about which kinds of path patterns are more likely to lead to
potential items of interest for the user. To better exploit the user profiles,
an improved path-finding algorithm called Profile-guided Path Reasoning (PPR)
is also developed, which leverages an inventory of neural symbolic reasoning
modules to effectively and efficiently find a batch of paths over a large-scale
KG. We extensively experiment on four real-world benchmarks and observe
substantial gains in the recommendation performance compared with
state-of-the-art methods.Comment: Accepted in CIKM 202
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