4,731 research outputs found
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
Recommendation & mobile systems - a state of the art for tourism
Recommendation systems have been growing in number over the last fifteen years. To evolve and adapt to the demands of the actual society, many paradigms emerged giving birth to even more paradigms and hybrid approaches. These approaches contain strengths and weaknesses that need to be evaluated according to the knowledge area in which the system is going to be implemented. Mobile devices have also been under an incredible growth rate in every business area, and there are already lots of mobile based systems to assist tourists. This explosive growth gave birth to different mobile applications, each having their own advantages and disadvantages. Since recommendation and mobile systems might as well be integrated, this work intends to present the current state of the art in tourism mobile and recommendation systems, as well as to state their advantages and disadvantages
AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
Recently, there has been an emergence of employing LLM-powered agents as
believable human proxies, based on their remarkable decision-making capability.
However, existing studies mainly focus on simulating human dialogue. Human
non-verbal behaviors, such as item clicking in recommender systems, although
implicitly exhibiting user preferences and could enhance the modeling of users,
have not been deeply explored. The main reasons lie in the gap between language
modeling and behavior modeling, as well as the incomprehension of LLMs about
user-item relations.
To address this issue, we propose AgentCF for simulating user-item
interactions in recommender systems through agent-based collaborative
filtering. We creatively consider not only users but also items as agents, and
develop a collaborative learning approach that optimizes both kinds of agents
together. Specifically, at each time step, we first prompt the user and item
agents to interact autonomously. Then, based on the disparities between the
agents' decisions and real-world interaction records, user and item agents are
prompted to reflect on and adjust the misleading simulations collaboratively,
thereby modeling their two-sided relations. The optimized agents can also
propagate their preferences to other agents in subsequent interactions,
implicitly capturing the collaborative filtering idea. Overall, the optimized
agents exhibit diverse interaction behaviors within our framework, including
user-item, user-user, item-item, and collective interactions. The results show
that these agents can demonstrate personalized behaviors akin to those of
real-world individuals, sparking the development of next-generation user
behavior simulation
InnoJam: A Web 2.0 discussion platform featuring a recommender system
In this Master Thesis we have designed, implemented and evaluated a Web 2.0
platform for massive online-discussion, inspired by Innovation Jams.
Innovation Jams, the original initiative from IBM, has proven to be successful at
bringing together vast amounts of people, capturing their untapped knowledge and, while
the participants are discussing, gather useful insights for a companyʼs innovation strategy
[Spangler et al. 2006, Bjelland and Chapman Wood 2008].
Our approach, based in an open-source forum system, features visualization
techniques and a recommender system in order to provide the participants in the Jam with
useful insights and interesting discussion recommendations for an improved participation.
A theoretical introduction and a state-of-the-art survey in recommender systems has
been gathered in order to frame and support the design of the hybrid recommender
system [Burke 2002], composed by a content-based and a collaborative filtering
recommenders, developed for InnoJam
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