To perform online inference efficiently, hashing techniques, devoted to encoding model parameters as binary codes, play a key role in reducing the computational cost of content-aware recommendation (CAR), particularly on devices with limited computation resource. However, current methods for CAR fail align their learning objectives (e.g., squared loss) ranking-based metrics Normalized Discounte...

In the recommendation system, collaborative filtering methods based on graph convolution network can explicitly model interaction between nodes of user–item bipartite and effectively use higher-order neighbor information. However, its representations are very susceptible to noise interaction. response this problem, SGL explored self-supervised learning improve robustness GCN. Nevertheless, cont...

Traditional studies on recommender systems usually leverage only one type of user behaviors (the optimization target, such as purchase), despite the fact that users also generate a large number various types interaction data (e.g., view, click, add-to-cart, etc). Generally, these heterogeneous multi-relational provide well-structured information and can be used for high-quality recommendation. ...

Clause recommendation is the problem of recommending a clause to legal contract, given context contract in question and type which should belong. With not much prior work being done toward generation contracts, this was proposed as first step bigger generation. As an open-ended text problem, distinguishing characteristics lie nature language sublanguage considerable similarity textual content w...

We address the problem of item recommendation in social media sharing systems. We adopt a multi-relational framework capable to integrate different entity types available in the social media system and relations between the entities. We then model different recommendation tasks as weighted random walks in the relational graph. The main contribution of the paper is a novel method for learning th...

Recommender systems use the past experiences and preferences of the target users as a basis to provide personalized recommendations for them and as the same time, solve the information overloading problem. Context as the dynamic information describing the situation of items and users and affecting the user’s decision process is essential to be used by recommender systems. Multidimensional appro...

Social recommendation is popular and successful among various urban sustainable applications like products recommendation, online sharing and shopping services. Users make use of these applications to form several implicit social networks through their daily social interactions. The users in such social networks can rate some interesting items and give comments. The majority of the existing stu...

Using a national cross-sectional survey of 500 primary care physicians conducted between 9 February and 1 March 2011, the objective of this study was to assess the impact of physician BMI on obesity care, physician self-efficacy, perceptions of role-modeling weight-related health behaviors, and perceptions of patient trust in weight loss advice. We found that physicians with normal BMI were mor...

BACKGROUND Physical activity (PA) is powerful for preventing and treating many chronic diseases. Physicians' own PA behaviors are correlated with their likelihood to counsel patients regarding PA. Medical students' PA-related attitudes and behaviors reflect what can be expected from our future physicians. METHODS A 27-item online survey was used to determine the percentage of Canadian medical...