Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback

Liu, Yong; Zhao, Lifan; Liu, Guimei; Lu, Xinyan; Gao, Peng; Liu, Yuanyuan; Jin, Zhihui

doi:10.24963/ijcai.2018/481

Cited by 32 publications

(13 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Conventional recommender systems are usually developed in non-interactive manner and learn the user preferences from logged user behavior data (Liu et al 2017;Yang et al 2018;Liu et al 2018;Wang et al 2018). One main drawback of these systems is that they cannot capture the changes of users' preferences in time.…”

Section: Introductionmentioning

confidence: 99%

Diversified Interactive Recommendation with Implicit Feedback

Liu

Xiao

et al. 2020

AAAI

Self Cite

View full text Add to dashboard Cite

Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attention. Previous methods mainly focus on optimizing recommendation accuracy. However, they usually ignore the diversity of the recommendation results, thus usually results in unsatisfying user experiences. In this paper, we propose a novel diversified recommendation model, named Diversified Contextual Combinatorial Bandit (DC2B), for interactive recommendation with users' implicit feedback. Specifically, DC2B employs determinantal point process in the recommendation procedure to promote diversity of the recommendation results. To learn the model parameters, a Thompson sampling-type algorithm based on variational Bayesian inference is proposed. In addition, theoretical regret analysis is also provided to guarantee the performance of DC2B. Extensive experiments on real datasets are performed to demonstrate the effectiveness of the proposed method in balancing the recommendation accuracy and diversity.

show abstract

Section: Introductionmentioning

confidence: 99%

Diversified Interactive Recommendation with Implicit Feedback

Liu

Xiao

et al. 2020

AAAI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Different from the traditional method (Liu et al , 2015, 2017; Peng et al , 2018), the user’s interactive behavior is strictly time sequential in the CrowdIntell network. Sequential methods use historical behavioral data arranged in chronological order to model mental representation for assisting decisions.…”

Section: Related Workmentioning

confidence: 99%

Behavioral data assists decisions: exploring the mental representation of digital-self

Zhang

Cui

et al. 2021

IJCS

View full text Add to dashboard Cite

Purpose The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell. Design/methodology/approach In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making. Findings The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest. Originality/value In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.

show abstract

“…Four commonly used accuracy metrics are employed to evaluate the recommendation performance of our method and the baselines. They are Recall, F-1 Score, Hit-Ratio (HR) and normalized Discounted Cumulative Gain (nDCG) (Liu et al 2018;Yang et al 2018).…”

Section: Experimental Settingsmentioning

confidence: 99%

Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction

Wang

et al. 2020

AAAI

View full text Add to dashboard Cite

Human behaviors are complex, which are often observed as a sequence of heterogeneous actions. In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. Most of existing approaches often model user behaviors in a mechanical way, namely treating a user action sequence as homogeneous sequential data, such as hourly temperatures, which fails to consider the complexity in user behaviors. In fact, users' choices are driven by certain underlying intentions (e.g., feeding the baby or relieving pain) according to Psychological theories. Moreover, the durations of intentions to drive user actions are quite different; some of them may be persistent while others may be transient. According to Psychological theories, we develop a hierarchical framework to describe the goal, intentions and action sequences, based on which, we design Intention Nets (IntNet). In IntNet, multiple Action Chain Nets are constructed to model the user actions driven by different intentions, and a specially designed Persistent-Transient Intention Unit models the different intention durations. We apply the IntNet to next-basket prediction, a recent challenging task in recommender systems. Extensive experiments on real-world datasets show the superiority of our Psychology-inspired model IntNet over the state-of-the-art approaches.

show abstract

Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback

Cited by 32 publications

References 14 publications

Diversified Interactive Recommendation with Implicit Feedback

Diversified Interactive Recommendation with Implicit Feedback

Behavioral data assists decisions: exploring the mental representation of digital-self

Intention Nets: Psychology-Inspired User Choice Behavior Modeling for Next-Basket Prediction

Contact Info

Product

Resources

About