Local low-rank Hawkes processes for modeling temporal user–item interactions
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DOI: 10.1007/s10115-019-01379-6
Abstract: Hawkes processes have become very popular in modeling multiple recurrent user–item interaction events that exhibit mutual-excitation properties in various domains. Generally, modeling the interaction sequence of each user–item pair as an independent Hawkes process is… read more here.
Keywords: local low; low rank; user item; hawkes processes ... See more keywords
ARERec: Attentive Local Interaction Model for Sequential Recommendation
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DOI: 10.1109/access.2022.3160466
Abstract: Previous sequential-recommendation methods have been able to capture patterns of item characteristics that interact with the user. However, they modeled user behavior using a whole interaction sequence, despite possible changes in a user’s behavior over… read more here.
Keywords: user item; sequence; sequential recommendation; interaction ... See more keywords
HIGnet: Hierarchical and Interactive Gate Networks for Item Recommendation
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DOI: 10.1109/mis.2020.3005928
Abstract: Existing research exploits the semantic information from reviews to complement user-item interactions for item recommendation. However, as these approaches either defer the user-item interactions until the prediction layer or simply concatenate all the reviews of… read more here.
Keywords: item; item recommendation; user item; hierarchical interactive ... See more keywords
Attribute Graph Neural Networks for Strict Cold Start Recommendation
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DOI: 10.1109/tkde.2020.3038234
Abstract: Rating prediction is a classic problem underlying recommender systems. It is traditionally tackled with matrix factorization. Recently, deep learning based methods, especially graph neural networks, have made impressive progress on this problem. Despite their effectiveness,… read more here.
Keywords: strict cold; neural networks; cold start; user item ... See more keywords
Incorporating Price into Recommendation With Graph Convolutional Networks
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2021.3091160
Abstract: In recent years, much research effort on recommendation has been devoted to mining user behaviors, i.e., collaborative filtering, along with the general information which describes users or items, e.g., textual attributes, categorical demographics, product images,… read more here.
Keywords: recommendation; user item; incorporating price; item ... See more keywords
HNF: Hybrid Neural Filtering Based on Centrality-Aware Random Walk for Personalized Recommendation
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Network Science and Engineering"
DOI: 10.1109/tnse.2021.3100864
Abstract: Social computing which analyzes users’ behaviors can help personalized recommender system to extract preferences of users. Most of personalized recommender systems exploit a user-item rating matrix to learn representations of users and items for predicting… read more here.
Keywords: recommendation; user item; hnf; neural filtering ... See more keywords
Predicting Dynamic User-Item Interaction with Meta-Path Guided Recursive RNN
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DOI: 10.3390/a15030080
Abstract: Accurately predicting user–item interactions is critically important in many real applications, including recommender systems and user behavior analysis in social networks. One major drawback of existing studies is that they generally directly analyze the sparse… read more here.
Keywords: user item; users items; item; recursive rnn ... See more keywords
Leveraging User Comments for Recommendation in E-Commerce
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DOI: 10.3390/app10072540
Abstract: Collaborative filtering recommender systems traditionally recommend products to users solely based on the given user-item rating matrix. Two main issues, data sparsity and scalability, have long been concerns. In our previous work, an approach was… read more here.
Keywords: rating matrix; user comments; item rating; user item ... See more keywords
LightFIG: simplifying and powering feature interactions via graph for recommendation
Sign Up to like & getrecommendations! Published in 2022 at "PeerJ Computer Science"
DOI: 10.7717/peerj-cs.1019
Abstract: The attributes of users and items contain key information for recommendation. The latest advances demonstrate that better representations can be learned by performing graph convolutions on attribute graph of the user-item pair. Recently proposed models… read more here.
Keywords: recommendation; user item; item attributes; item ... See more keywords