Repeat Consumption Recommendation Based on Users Preference Dynamics and Side Information

Rafailidis, Dimitrios; Νανόπουλος, Αλέξανδρος

doi:10.1145/2740908.2742732

Cited by 22 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The key factor of generating cross-domain recommendations is to develop a cross-domain attention mechanism to adaptively perform the weighting of user preferences from the source domains, and consequently generate accurate crossdomain recommendations in a target domain. In addition, an interesting future direction is to exploit the proposed neural attention mechanism for pairwise learning [37], social event detection [38], information diffusion [39], [40], exploiting distrust information [41] and capturing users' preference dynamics [42], [43].…”

Section: Discussionmentioning

confidence: 99%

A Neural Attention Model for Adaptive Learning of Social Friends' Preferences

Rafailidis¹,

Weiß²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Social-based recommendation systems exploit the selections of friends to combat the data sparsity on user preferences, and improve the recommendation accuracy of the collaborative filtering strategy. The main challenge is to capture and weigh friends' preferences, as in practice they do necessarily match. In this paper, we propose a Neural Attention mechanism for Social collaborative filtering, namely NAS. We design a neural architecture, to carefully compute the non-linearity in friends' preferences by taking into account the social latent effects of friends on user behavior. In addition, we introduce a social behavioral attention mechanism to adaptively weigh the influence of friends on user preferences and consequently generate accurate recommendations. Our experiments on publicly available datasets demonstrate the effectiveness of the proposed NAS model over other state-of-the-art methods. Furthermore, we study the effect of the proposed social behavioral attention mechanism and show that it is a key factor to our model's performance.

show abstract

Section: Discussionmentioning

confidence: 99%

A Neural Attention Model for Adaptive Learning of Social Friends' Preferences

Rafailidis¹,

Weiß²

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the future, to test this idea we plan to extend our SDPL model to generate recommendations for Netflix users based on user data from Epinions and Slashdot [46]- [49]. In addition, we plan to explore ways to extend the proposed model to account for evolving user preferences [50]- [53], hybrid recommendations [54] and social event detection [55].…”

Section: Discussionmentioning

confidence: 99%

Learning to Rank with Trust and Distrust in Recommender Systems

Rafailidis

Crestani

2017

Proceedings of the Eleventh ACM Conference on Recommender Systems

Self Cite

View full text Add to dashboard Cite

The data scarcity of user preferences and the coldstart problem often appear in real-world applications and limit the recommendation accuracy of collaborative filtering strategies. Leveraging the selections of social friends and foes can efficiently face both problems. In this study, we propose a strategy that performs social deep pairwise learning. Firstly, we design a ranking loss function incorporating multiple ranking criteria based on the choice in users, and the choice in their friends and foes to improve the accuracy in the top-k recommendation task. We capture the nonlinear correlations between user preferences and the social information of trust and distrust relationships via a deep learning strategy. In each backpropagation step, we follow a social negative sampling strategy to meet the multiple ranking criteria of our ranking loss function. We conduct comprehensive experiments on a benchmark dataset from Epinions, among the largest publicly available that has been reported in the relevant literature. The experimental results demonstrate that the proposed model beats other state-of-the art methods, attaining an 11.49% average improvement over the most competitive model. We show that our deep learning strategy plays an important role in capturing the nonlinear correlations between user preferences and the social information of trust and distrust relationships, and demonstrate the importance of our social negative sampling strategy on the proposed model.

show abstract

“…Furthermore, they design a collaborative recommendation algorithm which consider the geographical influence on user check-in behaviors. In the area of ecological monitoring, a participatory noise mapping system is proposed [14].The authors of [15] and [16] study the short-term recomsumption behaviors and repeatable recommendation on user check-in dataset. The authors [14] use mobile phones to determine environmental noise level.…”

Section: A Mcsmentioning

confidence: 99%

A Shared Bus Profiling Scheme for Smart Cities Based on Heterogeneous Mobile Crowdsourced Data

Kong

Xia

et al. 2020

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

Mobile crowdsourcing, as an effective and crucial technique of Industrial Internet of Things, is enabling smart city initiatives in the real world. It aims at incorporating the intelligence of dynamic crowds to collect and compute decentralized ubiquitous sensing data which can be used to solve major urbanization problems such as traffic congestion. The shared bus, as a neotype transportation mode, aims at improving the resource utilization rate and maintaining advantages of convenience and economy. In this paper, we provide a scheme to profile shared buses through heterogeneous mobile crowdsourced data (TRProfiling). First, we design an MCS-based shared bus data generation and collection solution to overcome the above data scarcity issue. Then we propose a Travel Profiling (TP) to profile resident travel and design a method called Multi-Constraint Evolution Algorithm (MCEA) to optimize the routes. Experimental results demonstrate that TRProfiling has an excellent performance in satisfying passengers' travel requirements. Index Terms-Industrial Internet of Things, travel profiling, mobile crowdsensing, route planning, shared buses.

show abstract

Repeat Consumption Recommendation Based on Users Preference Dynamics and Side Information

Cited by 22 publications

References 8 publications

A Neural Attention Model for Adaptive Learning of Social Friends' Preferences

A Neural Attention Model for Adaptive Learning of Social Friends' Preferences

Learning to Rank with Trust and Distrust in Recommender Systems

A Shared Bus Profiling Scheme for Smart Cities Based on Heterogeneous Mobile Crowdsourced Data

Contact Info

Product

Resources

About