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Time interval aware self-attention for sequential recommendation
Sequential recommender systems seek to exploit the order of users' interactions, in order to
predict their next action based on the context of what they have done recently. Traditionally …
predict their next action based on the context of what they have done recently. Traditionally …
Personalized top-n sequential recommendation via convolutional sequence embedding
Top-N sequential recommendation models each user as a sequence of items interacted in
the past and aims to predict top-N ranked items that a user will likely interact in a» near …
the past and aims to predict top-N ranked items that a user will likely interact in a» near …
Collaborative filtering and deep learning based recommendation system for cold start items
Recommender system is a specific type of intelligent systems, which exploits historical user
ratings on items and/or auxiliary information to make recommendations on items to the …
ratings on items and/or auxiliary information to make recommendations on items to the …
A novel temporal recommender system based on multiple transitions in user preference drift and topic review evolution
Recommender systems are challenging research problems being exploited to suggest new
items or services, such as books, music and movies, and even people, to users based on …
items or services, such as books, music and movies, and even people, to users based on …
Real-time spatiotemporal prediction and imputation of traffic status based on LSTM and Graph Laplacian regularized matrix factorization
Accurate prediction of traffic status in real time is critical for advanced traffic management
and travel navigation guidance. There are many attempts to predict short-term traffic flows …
and travel navigation guidance. There are many attempts to predict short-term traffic flows …
Intention-aware sequential recommendation with structured intent transition
Human behaviors in recommendation systems are driven by many high-level, complex, and
evolving intentions behind their decision making processes. In order to achieve better …
evolving intentions behind their decision making processes. In order to achieve better …
Preference dynamics with multimodal user-item interactions in social media recommendation
Recommender systems elicit the interests and preferences of individuals and make
recommendations accordingly, a main challenge for expert and intelligent systems. An …
recommendations accordingly, a main challenge for expert and intelligent systems. An …
Dynamic tensor recommender systems
Recommender systems have been extensively used by the entertainment industry, business
marketing and the biomedical industry. In addition to its capacity of providing preference …
marketing and the biomedical industry. In addition to its capacity of providing preference …
A time-aware spatio-textual recommender system
Abstract Location-Based Social Networks (LBSNs) allow users to post ratings and reviews
and to notify friends of these posts. Several models have been proposed for Point-of-Interest …
and to notify friends of these posts. Several models have been proposed for Point-of-Interest …
Collaborative filtering and deep learning based hybrid recommendation for cold start problem
Recommender systems (RS) are used by many social networking applications and online e-
commercial services. Collaborative filtering (CF) is one of the most popular approaches …
commercial services. Collaborative filtering (CF) is one of the most popular approaches …